plugins.json

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        "description": "TypeOxidizer converts the C-Style types in your HLIL / decompilation to their Rust equivalents for easier reading / interpretation and shorter decompilation.",
        "longdescription": "# TypeOxidizer (v1.2.1)\nAuthor: **azw / austinzwile**\n\n_TypeOxidizer converts the C-Style types in your HLIL / decompilation to their Rust equivalents for easier reading / interpretation and shorter decompilation._\n\n## Description:\n\nTypeOxidizer is a Binary Ninja plugin that seamlessly transforms the C-Style types in your HLIL / decompilation to their Rust equivalents through automatic typedef declaration and retyping, making for a cleaner and more precise decompilation. With built-in support for standard integer and floating-point types as well as strings, TypeOxidizer enhances your analysis by \"oxidizing\" legacy type definitions into Rust's clean and precise syntax.\n\n## Demo\n\nHere's a nice before:\n\n<img width=\"1281\" alt=\"image\" src=\"https://github.com/user-attachments/assets/25249368-7de6-4f25-99a9-de3e36a8a121\" />\n\nand after:\n\n<img width=\"944\" alt=\"image\" src=\"https://github.com/user-attachments/assets/789aefae-94e6-4a74-9013-34976d4711f5\" />\n\n## Usage\n\nThe following registered commands are available via the Plugins dropdown menu as well as the command pallette with `CMD+P`:\n\n - Convert from C to Rust Types\n - Revert to C Types from Rust\n\n## Features\n\nThe conversion from and to following types are currently handled:\n\n| C Type      | Rust Type |\n|-------------|-----------|\n| `uint8_t`   | `u8`      |\n| `uint16_t`  | `u16`     |\n| `uint32_t`  | `u32`     |\n| `uint64_t`  | `u64`     |\n| `uint128_t` | `u128`    |\n| `int8_t`    | `i8`      |\n| `int16_t`   | `i16`     |\n| `int32_t`   | `i32`     |\n| `int64_t`   | `i64`     |\n| `int128_t`  | `i128`    |\n| `float`     | `f32`     |\n| `double`    | `f64`     |\n| `size_t`    | `usize`   |\n| `ssize_t`   | `isize`   |\n| `char*`     | `str`     |\n\n## Installation Instructions\n\n### Darwin\n\nInstall via the Plugin Manager or by cloning the repo at https://github.com/austinzwile/TypeOxidizer and dropping the folder into your plugins directory.\n\n### Linux\n\nInstall via the Plugin Manager or by cloning the repo at https://github.com/austinzwile/TypeOxidizer and dropping the folder into your plugins directory.\n\n### Windows\n\nInstall via the Plugin Manager or by cloning the repo at https://github.com/austinzwile/TypeOxidizer and dropping the folder into your plugins directory.\n\n## Minimum Version\n\nThis plugin requires the following minimum version of Binary Ninja:\n\n* 3164\n\n## Required Dependencies\n\nThe following dependencies are required for this plugin:\n\n## Todo\n\n- [ ] Add proper conversion of types when typecasting. The decompilation currently doesn't allow for the retyping of anything on the right side of an equals sign so I will have to figure that out.\n- [ ] Implement a drop-down/content menu to allow for selective conversion between C and Rust.\n- [ ] Handle structs and typedefs. The types within already defined type definitions don't get handled right.\n\n## Required Dependencies\n\nThe following dependencies are required for this plugin:\n\n## License\n\nThis plugin is released under a MIT license.\n\n## Metadata Version\n\n2\n",
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        "longdescription": "# bn-ebpf-solana\n\nA pure-python Binary Ninja plugin for Solana EBPF.\n\n## Installation\n\nAvailable in Binja's plugin manager inside of `Plugins > Manage Plugins`\n\n## Manual installation (advanced)\n\nThis is only needed if you wish to tinker with the plugin to modify it.\n\nClone this [repo](https://github.com/otter-sec/bn-ebpf-solana) in your Binja `plugins` folder, located in the [user folder](https://docs.binary.ninja/guide/index.html#user-folder).\n\nNow install the requirements :\n- either by running `Install python3 module` in Binja's `command palette` (Ctrl + P) and install `lief`\n- or by going to the [user folder](https://docs.binary.ninja/guide/index.html#user-folder) and installing `lief` within the `venv` enviroment using:\n\n```\npip install lief\n```\n\nCurrently tested on `lief@0.16.2-d4900dab`\n\nSee our [introductory blog post](https://osec.io/blog/tutorials/2022-08-27-reverse-engineering-solana/).\n\n**Instruction lifting!**\n![](/assets/lift.png)\n\n**Solana SDK Structures!**\n![](/assets/struct.png)\n\n\n\nCopy this directory into your Binary Ninja plugins folder and restart.\n\n## Features\n\n- **Instruction Lifting**: All EBPF instructions are lifted to LLIL\n- **Accurate Memory Maps**: We implement Solana-specific memory maps (0x{1/2/3/4}00000000 addresses for data/stack/heap/input)\n- **Solana ELF Relocations**: Solana-specific ELF relocations\n- **Syscall Function Signatures**: Full signatures for all of the Solana syscalls\n- **(partial) Solana SDK Types**: Type definitions for all Solana SDK objects. (fully complete for C, in-progress for Rust)\n\n_TODO:_\n\n- **Solana SDK Signature Matching**: Automatically match common Solana SDK functions.\n\n## Debugging\n\n```\n[ScriptingProvider] ModuleNotFoundError: No module named 'lief'\n```\n\nIs `lief` installed?\n\nRun the following in the Binja python console\n\n```python\nimport lief\nlief.__version__\n```\n\nIf you get an error, refer to the **Installation** section\n",
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            "description": "Supports loading .per files into Binary Ninja",
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        "version": "v0.0.6",
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        "longdescription": "# BinaryNinja-PSVitaLoader\nPS Vita ELF/PRX2 loader plugin for Binary Ninja\n\n## Table of Contents\n1. [Description](#description)\n2. [Plugin Usage](#plugin-usage)\n3. [Notes/Issues](#notesissues)\n4. [TODO](#todo)\n5. [Credits](#credits)\n6. [Use-case examples](#use-case-examples)\n7. [Legal](#legal)\n\n### Description:\nA Binary Ninja Plugin for PRX2 PS Vita eboot.bin ELFs. \n\nDynamic linking of modules on the PS Vita [is performed](https://wiki.henkaku.xyz/vita/NID#Usage) by NID(**N**umeric **Id**entifier) of a function or variable instead of names. The primary purpose of this plugin is to resolve all import/export function/variable names, create symbols for them, and load them back into the default ELF BinaryView at their respective position. This plugin will also add PS Vita(PRX2) specific datatypes with locations in the binary resolved(if applicable). Additionally, this plugin attempts to do some cleanup resulting from the decompilation of the mixed ARMv7/thumb2 instruction sets, removing some misaligned/junk instructions in places where there should have been in-line data. \n\n\n### Plugin Usage:\nLoading the plugin will prompt for a NID database yaml file\n![Selecting NID DB](/images/nid-db-select.png)\n\nThe included NID database was obtained from the vitasdk [vita-headers](https://github.com/vitasdk/vita-headers) and combined with the following:\n```bash\n\u2514\u2500$ yq ea '. as $item ireduce ({}; . * $item )' vita-headers/db/360/*.yml > merged-vita-nid-db.yml\n```\n\nAfterwards, the plugin will prompt for a header file, this is not necessary, however **highly recommended**. With the header file we are able to resolve every single imported functions argument count, argument name, argument type, and function type/return. If this is not used, imported functions default to void and `variable_arguments` is set on the `binaryninja.types.FunctionType` object.\n\n![Selecting NID DB](/images/header-select.png)\n\nThe header file included is a compilation of all header files from [vitasdk/vita-headers](https://github.com/vitasdk/vita-headers/), all credit for the headers and NID DB goes to the vitasdk team. This header file was generated using the vitasdk toolchain([LICENSE](https://github.com/vitasdk/vita-headers/blob/master/LICENSE.md) added after) like so:\n```\n\u2514\u2500$ /usr/local/vitasdk/bin/arm-vita-eabi-gcc -P -E $VITASDK/arm-vita-eabi/include/vitasdk.h -D\"__attribute__(x)=\" -D\"__extension__(x)=\" -Drestrict= -D__restrict__= > vita_headers.hpp\n```\n\nThe plugin will first run linear sweep analysis until no new functions are created. Symbols with their respective function names are then resolved almost instantly and injected/added into the default ELF BinaryView. \n\n\nAt this point, any sce* function call should be resolved and a few datatypes will be added. If the header file was supplied, all datatypes will be imported along with the sce* functions proper return type, argument names, and argument types. \n![Plugin Sample](/images/plugin-sample.png)\n\n\n### Notes/Issues:\n- Tested to be working on Binary Ninja `4.1.5902-Stable` and `4.2.6075-dev`\n- Binary Ninja appears to trip in ARMv7/thumb2 mixed instruction sets binaries. An issue was encountered where if the binary is detected as ARMv7(All were while testing) and the first instruction is a Thumb2 instruction, it will mangle the entire dis-assembled binary. To fix this, right click initial function/instruction->Make Function at This Address->thumb2->linux-thumb2. Next run Linear Sweep again, this will fix the binary and later instruction set switches(typically `blx`) are sometimes accounted for properly.\n\nA painful but much better solution to thumb2 start: After ensuring the very first function(@base_addr) is set to thumb2 manually, I have had great luck doing the following in the BN console:\n```python\n>>> thumb2 = binaryninja.Architecture['thumb2']\n... \n>>> for func in bv.functions:\n... \tif func.arch != thumb2:\n... \t\tbv.remove_function(func)\n```\nAfter all non-thumb2 functions are removed, either (re)load the Vita Loader plugin(recommended) or run a few linear sweeps, this will correctly identify instruction set switches and give you a clean binary view(for the most part). If anyone knows how to resolve this globally, please do share - I have tried forcing the platform but because the binary is technically `armv7` the platform switches back to `linux-armv7`.\n\n\n### TODO:\n- Split functions and utility across multiple imports to maintain readability.\n- Move examples to wiki\n- Extend support to `scelibstub_psp`, `scelibent_psp` and other PRX1 primitives to support the OG PSP.\n- Extend to full custom BinaryView plugin with support for relocations\n- ~~Potentially extending un-implenented instructions commonly used within Vita/PRX2 elfs(Such as: vcvt, vdiv, vmov, vmrs and other fp related instructions)~~ - The binaryninja team is [already hard at work on these](https://github.com/Vector35/binaryninja-api/commits/dev/arch/armv7).\n\n\n### Credits:\n\n- The HENkaku [Vita Development Wiki](https://wiki.henkaku.xyz/vita/Main_Page) and specifically the [PRX page](https://wiki.henkaku.xyz/vita/PRX) for the detailed breakdown of the `SceModuleInfo` structure and its location within the ELF. Also for the breakdown of the `scelibent_prx2arm` and `scelibstub_prx2arm` function export/import structures.\n\n- VitaSDK for the [NID db](https://github.com/vitasdk/vita-headers/tree/master/db) and all [header files](https://github.com/vitasdk/vita-headers/tree/master/include)(Also the wonderful vita [toolchain](https://github.com/vitasdk/vdpm) and module [documentation](https://docs.vitasdk.org/modules.html))\n\n- The [VitaLoader Redux](https://github.com/CreepNT/VitaLoaderRedux) project, a Vita loader plugin for Ghidra with way more than just symbol mapping support - although not a Ghidra or Java fan, I studied portions of the project and its amazingly clarifying inline comments when stuck. \n\n\n### Use-case examples:\nThere are many great use-cases to learning more about reverse engineering binaries, a good example is to leverage this to patch binaries to unlock the FPS or allow them to run at full PS Vita resolution. \n\nThanks to the talented contributors of [VitaGrafix](https://github.com/Electry/VitaGrafix), we can patch binaries directly on the Vita at loadtime. The VitaGrafix [wiki](https://github.com/Electry/VitaGrafix/wiki/The-VG-Language%E2%84%A2) serves as a great educational resource to get started on this.\n\nEXAMPLE:\n\nPatching FPS limits:\nSearching for cross references on vBlank(vSync) related calls is usually a good start. In a lot of cases, the frame limit is introduced by setting a vblank interval greater than `1`. Typically this would be done with a value moved into `r0` just before `sceDisplayWaitVblankStartMulti` is called. This value(`vcount`) will typically be `1`, `2`, or `3`. This likely indicates the wait for the next vblank start, which will occur after the last scanline and before the next VSync interval. A `vcount` of `2` will wait for `2` vBlank intervals before a VSync, this effectively limits the framerate to half of the displays refresh rate(30fps). A `vcount` of `3` would be refresh-rate/3, effectively limiting to 20fps.\n\nLooking at the popular [nzportable](https://github.com/nzp-team/nzportable) homebrew game running at 60fps natively.\n\n![nzp fps](/images/example2-fps.png)\n\nThe variable here wasn't resolved, however looking at the data at that address, we see its just a `1`, this indicates the game is capped to the screens refresh rate(60Hz), as 1 vBlank interval will occur for every vSync interval. \n\n![nzp fps var](/images/example2-fps-data.png)\n\nNOTE: This isn't always straightforward as many binaries will rely on VSync and modifying anything directly related will usually cause them to run at double speed. Further research is needed on a binary-to-binary basis. \n\n\nPatching resolution:\nThis is much simpler and typically you'd want to load up the binary on a PS Vita with a tool that can poll the frame buffer to determine resolution. Knowing the resolution, you can simply search for that value within Binary Ninja. Looking at the nzp homebrew again, knowing it runs at full Vita resolution of `960x544`, we can search for `0x3c0`(960), this results in only one find and thankfully its a bit more obvious, as `0x3c0`(960) is set just before `0x220` (544)\n\n![nzp res](/images/nzp-find-res.png)\n\n\n\nAnother potential method to patch resolution is to cross reference the `sceDisplaySetFrameBuf` symbol and figure out where the framebuffer is updated/set, this takes in a pointer to the `SceDisplayFrameBuf` struct which contains the framebuffer width and height. These values are sometimes set just before the function call. According to the [vitasdk documentation](https://docs.vitasdk.org/group__SceDisplayKernel.html#structSceDisplayFrameBuf) the following resolutions can be set: `480x272`, `640x368`, `720x408`, `960x544`. \n\nTo show an example of this and practice our reverse engineering, we use the vitasdk toolchain to compile a homebrew app with a resolution of 640(`0x280`) x 368(`0x170`).\n\n\nSearching for `0x280`, there is only one with `0x170` next to it, making it the obvious choice:\n```\n8103d040      int32_t var_44 = 0x280;\n8103d044      int32_t var_40 = 0x170;\n8103d048      sceDisplaySetFrameBuf(&var_54, SCE_DISPLAY_SETBUF_NEXTFRAME);\n```\n\nLooking at the disassembly we see that `0x280` is moved into r1 at addr `0x8103d036` and `0x170` is moved into `r2` at addr `0x8103d03c` before they are both stored on the stack.\n```\n8103d036  5ff42071   movs    r1, #0x280\n8103d03a  0192       str     r2, [sp, #4] {var_48}  {0x0}\n8103d03c  5ff4b872   movs    r2, #0x170\n8103d040  0291       str     r1, [sp, #8] {var_44}  {0x280}\n8103d042  00a8       add     r0, sp, #0 {var_54}\n8103d044  0392       str     r2, [sp, #0xc] {var_40}  {0x170}\n8103d046  0121       movs    r1, #1\n8103d048  e4f14eeb   blx     #sceDisplaySetFrameBuf\n```\n\nWe could use this information to patch the binary by changing the values in the `mov` instructions, or we can patch this directly on the Vita with VitaGrafix. Instead of patching with a set value, VitaGrafix allows us to use a dynamic variable that can be configured before starting the binary, allowing it to patch the binary at load-time. This patch would look like:\n\n```\n[CUSTHOMBRW, eboot.bin]\n@FB\n0:0x3d036 t2_mov(1, 1, fb_w)\n0:0x3d03c t2_mov(1, 2, fb_h)\n```\n\n\n### Legal\n\n\u201cPlayStation\u201d and \"PlayStation Vita\" are registered trademarks of Sony Computer Entertainment Inc. This tool is **NOT** affiliated with, endorsed by, related to, or derived from confidential materials belonging to Sony Computer Entertainment Inc.\n\nThis tool was created for educational, security research purposes. Anything mentioned in this repository, any examples shown, and the plugin itself will **NOT** and can **NOT** break any encryption or circumvent protections in a binary.\n",
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        },
        "platforms": [
            "Darwin",
            "Linux",
            "Windows"
        ],
        "installinstructions": {
            "Darwin": "See README.",
            "Linux": "See README.",
            "Windows": "See README."
        },
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            "text": "Copyright 2024 amtal\n\n                    GNU AFFERO GENERAL PUBLIC LICENSE\n                       Version 3, 19 November 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>\n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n                            Preamble\n\n  The GNU Affero General Public License is a free, copyleft license for\nsoftware and other kinds of works, specifically designed to ensure\ncooperation with the community in the case of network server software.\n\n  The licenses for most software and other practical works are designed\nto take away your freedom to share and change the works.  By contrast,\nour General Public Licenses are intended to guarantee your freedom to\nshare and change all versions of a program--to make sure it remains free\nsoftware for all its users.\n\n  When we speak of free software, we are referring to freedom, not\nprice.  Our General Public Licenses are designed to make sure that you\nhave the freedom to distribute copies of free software (and charge for\nthem if you wish), that you receive source code or can get it if you\nwant it, that you can change the software or use pieces of it in new\nfree programs, and that you know you can do these things.\n\n  Developers that use our General Public Licenses protect your rights\nwith two steps: (1) assert copyright on the software, and (2) offer\nyou this License which gives you legal permission to copy, distribute\nand/or modify the software.\n\n  A secondary benefit of defending all users' freedom is that\nimprovements made in alternate versions of the program, if they\nreceive widespread use, become available for other developers to\nincorporate.  Many developers of free software are heartened and\nencouraged by the resulting cooperation.  However, in the case of\nsoftware used on network servers, this result may fail to come about.\nThe GNU General Public License permits making a modified version and\nletting the public access it on a server without ever releasing its\nsource code to the public.\n\n  The GNU Affero General Public License is designed specifically to\nensure that, in such cases, the modified source code becomes available\nto the community.  It requires the operator of a network server to\nprovide the source code of the modified version running there to the\nusers of that server.  Therefore, public use of a modified version, on\na publicly accessible server, gives the public access to the source\ncode of the modified version.\n\n  An older license, called the Affero General Public License and\npublished by Affero, was designed to accomplish similar goals.  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For a particular\nproduct received by a particular user, \"normally used\" refers to a\ntypical or common use of that class of product, regardless of the status\nof the particular user or of the way in which the particular user\nactually uses, or expects or is expected to use, the product.  A product\nis a consumer product regardless of whether the product has substantial\ncommercial, industrial or non-consumer uses, unless such uses represent\nthe only significant mode of use of the product.\n\n  \"Installation Information\" for a User Product means any methods,\nprocedures, authorization keys, or other information required to install\nand execute modified versions of a covered work in that User Product from\na modified version of its Corresponding Source.  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If propagation of a covered\nwork results from an entity transaction, each party to that\ntransaction who receives a copy of the work also receives whatever\nlicenses to the work the party's predecessor in interest had or could\ngive under the previous paragraph, plus a right to possession of the\nCorresponding Source of the work from the predecessor in interest, if\nthe predecessor has it or can get it with reasonable efforts.\n\n  You may not impose any further restrictions on the exercise of the\nrights granted or affirmed under this License.  For example, you may\nnot impose a license fee, royalty, or other charge for exercise of\nrights granted under this License, and you may not initiate litigation\n(including a cross-claim or counterclaim in a lawsuit) alleging that\nany patent claim is infringed by making, using, selling, offering for\nsale, or importing the Program or any portion of it.\n\n  11. Patents.\n\n  A \"contributor\" is a copyright holder who authorizes use under this\nLicense of the Program or a work on which the Program is based.  The\nwork thus licensed is called the contributor's \"contributor version\".\n\n  A contributor's \"essential patent claims\" are all patent claims\nowned or controlled by the contributor, whether already acquired or\nhereafter acquired, that would be infringed by some manner, permitted\nby this License, of making, using, or selling its contributor version,\nbut do not include claims that would be infringed only as a\nconsequence of further modification of the contributor version.  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No Surrender of Others' Freedom.\n\n  If conditions are imposed on you (whether by court order, agreement or\notherwise) that contradict the conditions of this License, they do not\nexcuse you from the conditions of this License.  If you cannot convey a\ncovered work so as to satisfy simultaneously your obligations under this\nLicense and any other pertinent obligations, then as a consequence you may\nnot convey it at all.  For example, if you agree to terms that obligate you\nto collect a royalty for further conveying from those to whom you convey\nthe Program, the only way you could satisfy both those terms and this\nLicense would be to refrain entirely from conveying the Program.\n\n  13. Remote Network Interaction; Use with the GNU General Public License.\n\n  Notwithstanding any other provision of this License, if you modify the\nProgram, your modified version must prominently offer all users\ninteracting with it remotely through a computer network (if your version\nsupports such interaction) an opportunity to receive the Corresponding\nSource of your version by providing access to the Corresponding Source\nfrom a network server at no charge, through some standard or customary\nmeans of facilitating copying of software.  This Corresponding Source\nshall include the Corresponding Source for any work covered by version 3\nof the GNU General Public License that is incorporated pursuant to the\nfollowing paragraph.\n\n  Notwithstanding any other provision of this License, you have\npermission to link or combine any covered work with a work licensed\nunder version 3 of the GNU General Public License into a single\ncombined work, and to convey the resulting work.  The terms of this\nLicense will continue to apply to the part which is the covered work,\nbut the work with which it is combined will remain governed by version\n3 of the GNU General Public License.\n\n  14. Revised Versions of this License.\n\n  The Free Software Foundation may publish revised and/or new versions of\nthe GNU Affero General Public License from time to time.  Such new versions\nwill be similar in spirit to the present version, but may differ in detail to\naddress new problems or concerns.\n\n  Each version is given a distinguishing version number.  If the\nProgram specifies that a certain numbered version of the GNU Affero General\nPublic License \"or any later version\" applies to it, you have the\noption of following the terms and conditions either of that numbered\nversion or of any later version published by the Free Software\nFoundation.  If the Program does not specify a version number of the\nGNU Affero General Public License, you may choose any version ever published\nby the Free Software Foundation.\n\n  If the Program specifies that a proxy can decide which future\nversions of the GNU Affero General Public License can be used, that proxy's\npublic statement of acceptance of a version permanently authorizes you\nto choose that version for the Program.\n\n  Later license versions may give you additional or different\npermissions.  However, no additional obligations are imposed on any\nauthor or copyright holder as a result of your choosing to follow a\nlater version.\n\n  15. Disclaimer of Warranty.\n\n  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY\nAPPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT\nHOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM \"AS IS\" WITHOUT WARRANTY\nOF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,\nTHE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\nPURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM\nIS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF\nALL NECESSARY SERVICING, REPAIR OR CORRECTION.\n\n  16. Limitation of Liability.\n\n  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING\nWILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS\nTHE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY\nGENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE\nUSE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF\nDATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD\nPARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),\nEVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF\nSUCH DAMAGES.\n\n  17. Interpretation of Sections 15 and 16.\n\n  If the disclaimer of warranty and limitation of liability provided\nabove cannot be given local legal effect according to their terms,\nreviewing courts shall apply local law that most closely approximates\nan absolute waiver of all civil liability in connection with the\nProgram, unless a warranty or assumption of liability accompanies a\ncopy of the Program in return for a fee.\n\n                     END OF TERMS AND CONDITIONS\n\n            How to Apply These Terms to Your New Programs\n\n  If you develop a new program, and you want it to be of the greatest\npossible use to the public, the best way to achieve this is to make it\nfree software which everyone can redistribute and change under these terms.\n\n  To do so, attach the following notices to the program.  It is safest\nto attach them to the start of each source file to most effectively\nstate the exclusion of warranty; and each file should have at least\nthe \"copyright\" line and a pointer to where the full notice is found.\n\n    <one line to give the program's name and a brief idea of what it does.>\n    Copyright (C) <year>  <name of author>\n\n    This program is free software: you can redistribute it and/or modify\n    it under the terms of the GNU Affero General Public License as published by\n    the Free Software Foundation, either version 3 of the License, or\n    (at your option) any later version.\n\n    This program is distributed in the hope that it will be useful,\n    but WITHOUT ANY WARRANTY; without even the implied warranty of\n    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n    GNU Affero General Public License for more details.\n\n    You should have received a copy of the GNU Affero General Public License\n    along with this program.  If not, see <http://www.gnu.org/licenses/>.\n\nAlso add information on how to contact you by electronic and paper mail.\n\n  If your software can interact with users remotely through a computer\nnetwork, you should also make sure that it provides a way for users to\nget its source.  For example, if your program is a web application, its\ninterface could display a \"Source\" link that leads users to an archive\nof the code.  There are many ways you could offer source, and different\nsolutions will be better for different programs; see section 13 for the\nspecific requirements.\n\n  You should also get your employer (if you work as a programmer) or school,\nif any, to sign a \"copyright disclaimer\" for the program, if necessary.\nFor more information on this, and how to apply and follow the GNU AGPL, see\n<http://www.gnu.org/licenses/>."
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Additionally, the usual options are available to configure the search settings and constrain the gadget pool appropriately. For a detailed technical explanation, refer to [this post](https://faultpoint.com/post/2024-06-02-binja-plugin-ropview/).\n\n![1.gif](images/1.gif)\n\n## Gadget Analysis\nIn the right pane, an analysis is shown of the currently selected gadget in the pool. This pane details the effects of the gadget on memory locations, clobbered registers, and accesses made to the stack or other memory regions. The analysis shows a start state, intermediary step states, and an end state. The analysis framework takes advantage of [unicorn engine](https://www.unicorn-engine.org/) and can return a report instantly. Internally, this is done using different hooks to add analysis and diffing functionality in the fetch-execute cycle. Furthermore, access violations are hooked to resolve fetched mappings from the target binary into the analysis context in realtime as they are requested. 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        "longdescription": "# ReverserAI (v1.1)\nAuthor: **Tim Blazytko**\n\n_Provides automated reverse engineering assistance through the use of local large language models (LLMs) on consumer hardware._\n\n## Description:\n\n_ReverserAI_ is a research project designed to automate and enhance reverse engineering tasks through the use of locally-hosted large language models (LLMs). Operating entirely offline, this initial release features the automatic suggestion of high-level, semantically meaningful function names derived from decompiler output. _ReverserAI_ is provided as a Binary Ninja plugin; however, its architecture is designed to be extended to other reverse engineering platforms such as IDA and Ghidra.\n\nWhile local LLMs do not match the performance and capabilities of their cloud-based counterparts like ChatGPT4 and require substantial computing resources, they represent a significant step forward in balancing performance with confidentiality requirements.\n\n_ReverserAI_ serves as an initial exploration into the potential of local LLMs as aids in reverse engineering on consumer-grade hardware. It showcases what is currently achievable and plans to be a playground for future developments in the realm of AI-assisted reverse engineering. Additionally, the project explores the benefits of combining static analysis techniques with modern AI capabilities to improve the accuracy of AI-assisted reverse engineering.\n\n\nSome example use cases can be found in [examples](./examples).\n\n> [!NOTE]\n> Disclaimer: My expertise in machine learning and LLMs is limited. There may exist more efficient models or methods to achieve similar tasks with greater performance. This project represents a culmination of research into viable configurations, offering a stable foundation with acceptable performance. Feedback and contributions to improve _ReverserAI_ are highly encouraged.\n\n\n## Core Features\n\n- **Offline Operation**: Runs LLMs entirely on local CPU/GPU, ensuring data privacy and security.\n\n- **Automatic Function Naming**: Automatically suggests semantically meaningful function names from decompiler output.\n\n- **Binary Ninja Integration**: Seamlessly integrates as a plugin with Binary Ninja.\n\n- **Modular Architecture**: Designed for easy extension to support other reverse engineering tools like IDA and Ghidra.\n\n- **Consumer Hardware Compatibility**: Optimized to run on consumer-grade hardware, such as Apple silicon architectures.\n\n- **Enhanced AI with Static Analysis**: Improves AI-based analysis and suggestions by incorporating insights from static analysis, providing a richer context and more accurate results.\n\n\n## Installation\n\n_ReverserAI_ can be easily integrated via Binary Ninja's plugin manager. Alternatively, for those preferring command line installation, execute in Binary Ninja's `plugins` folder:\n\n```bash\ngit clone https://github.com/mrphrazer/reverser_ai.git\ncd reverser_ai\n\n# install requirements\npip3 install -r requirements.txt\n\n# install ReverserAI\npip3 install .\n```\n\nUpon initial launch, the tool will automatically download the (default `mistral-7b-instruct-v0.2.Q4_K_M.gguf` large language model file (~5GB). The download time varies based on internet connection speed. To manually initiate the download or download other models, execute the [`model_download.py`](scripts/model_download.py) script.\n\n\n## Hardware Requirements\n\nFor optimal LLM performance on consumer-grade hardware, a setup with multiple CPU threads or a powerful GPU is advised. _ReverserAI_ runs efficiently on systems with at least 16 GB of RAM and 12 CPU threads, with queries taking about 20 to 30 seconds. GPU optimizations, especially on Apple silicon devices, can reduce this to 2 to 5 seconds per query.\n\n\n## Usage\n\n_ReverserAI_ is accessible through Binary Ninja's user interface and via command line.\n\n### User Interface\n\nTo invoke the plugin within Binary Ninja, navigate to `Plugins -> ReverserAI` and, for example, run \"Rename All Functions\":\n\n<p align=\"left\">\n<img alt=\"Plugin Menu\" src=\"imgs/plugin_menu.png\" width=\"500\"/>\n</p>\n\nDepending on the total number of functions in the binary, this may take a while. The AI-assisted function name suggestions will appear in the Log window:\n\n<p align=\"center\">\n<img alt=\"Binary Ninja Log\" src=\"imgs/plugin_results.png\"/>\n</p>\n\n\n### Configuration\n\nConfiguring _ReverserAI_ to match your hardware setup optimizes its performance. Key configuration parameters include CPU and GPU utilization preferences: For powerful GPUs, configure _ReverserAI_ to primarily use GPU, reducing CPU threads to minimize overhead. Without a strong GPU, increase CPU thread usage to maximize processing power. For systems with balanced resources, allocate tasks between CPU and GPU for efficient operation. Another dimension is the selection of the underlying model which greatly influences _ReverserAI_'s functionality, performance, and resource consumption. Below, key configuration parameters are outlined along with guidance on model selection based on your computational resources and requirements.\n\n\n* `model_identifier`: Choose between `mistral-7b-instruct` and `mixtral-8x7b-instruct` to best suit your analysis needs and hardware capabilities. \n  - `mistral-7b-instruct` is a smaller, faster model requiring approximately 5GB of RAM, ideal for quick processing tasks. It is best suited for environments with limited computational resources or when high throughput is required, albeit with a trade-off in the quality of outputs compared to larger models.\n  - `mixtral-8x7b-instruct` is a larger model designed, ideal for more complex code analysis tasks, requiring approximately 25GB of RAM. This model is recommended for users with access to high-end hardware and who need enhanced reasoning capabilities and accuracy. It may necessitate disabling memory mapping on machines that cannot support the required RAM level.\n\n* `use_mmap`: Enables memory mapping of the model files when set to `true`. This allows the model to load parts of the data on-demand, which can improve performance and reduce memory usage, especially for very large models.\n\n* `n_threads`:  Specifies the number of CPU threads to utilize. Maximize CPU thread count to the number of available CPU threads for full utilization, or set to 0 to disable.\n\n* `n_gpu_layers`: Determines GPU layer usage. Enter values up to 99 for powerful GPUs, or 0 to disable GPU processing.\n\n* `seed`: A fixed seed ensures deterministic behavior for debugging (consistent output for identical inputs). Modify the seed for varied responses.\n\n* `verbose`:  Enabling `verbose` mode provides detailed logs about the model and configuration settings.\n\nThe default configuration is designed to strike a balance between performance and resource usage, with a preference for GPU acceleration where feasible. Adjust these settings based on your specific hardware capabilities and the requirements of your reverse engineering tasks to achieve optimal performance.\n\n\n#### Binary Ninja\n\nTo adjust settings in Binary Ninja, open `Settings` and search for `reverser_ai`. Changes require Binary Ninja to be restarted.\n\n<p align=\"center\">\n<img alt=\"Plugin Settings\" src=\"imgs/plugin_settings.png\"/>\n</p>\n\nEach change requires a restart of Binary Ninja.\n\n\n#### Parameter Tuning\n\nFor detailed parameter adjustment, utilize the [`gpt_function_namer.py`](scripts/gpt_function_namer.py) script with a configuration file, starting with the provided [`example_config.toml`](example_config.toml):\n\n```\n$ time python3 scripts/gpt_function_namer.py example_config.toml\nSuggested name: xor_two_numbers\n\nreal\t0m1.550s\nuser\t0m0.268s\nsys\t0m0.223s\n```\n\n\n## Static Analysis to Improve AI-based Analysis\n\n_ReverserAI_ explores how to enrich AI-related reverse engineering tasks by providing more context through static analysis. Initially, Context-Aware Function Renaming is the only implemented feature.\n\n\n### Context-Aware Function Renaming\n\nExperiments have shown that function renaming is especially effective for functions with contextual information, such as external API functions or strings that provide context for the AI. The goal is to narrow the analysis scope and focus on functions where context from strings, symbols, and other static analysis data can be leveraged.\n\n\n## Code Organization\n\n_ReverserAI_'s codebase maintains a clear separation between generic LLM functionalities and tool-specific integration, ensuring modularity and ease of extension. Below is an overview of the primary components:\n\n- **`gpt` Folder**: Contains code for interacting with large language models (LLMs). This includes:\n  - A generic agent (`agent.py`) for model-agnostic operations.\n  - A specialized module (`function_name_gpt.py`) for generating function name suggestions.\n\n- **`binary_ninja` Folder**: Hosts wrapper instances that:\n  - Utilize Binary Ninja features to produce decompiler outputs.\n  - Interface with the `gpt` folder's agents, enabling LLM-powered function naming within Binary Ninja.\n  \n\n## Limitations and Future Work\n\n_ReverserAI_ serves as a proof of concept that demonstrates the potential of leveraging local LLMs for reverse engineering tasks on consumer-grade hardware. Currently, its primary functionality is to offer function name suggestions, but there exists significant scope for enhancement and expansion. Future directions could include:\n\n* Investigating additional interaction methods and parameters with LLMs to enhance quality and processing speed.\n\n* Adding network communication for hosting the _ReverserAI_ agent on a powerful server, circumventing local hardware constraints.\n\n* Fine-tuning existing models or developing specialized models tailored to reverse engineering needs.\n\n* Expanding functionality to include code explanations, analysis, and bug detection, subject to scalability and feasibility.\n\n* Extending support to other reverse engineering platforms such as IDA and Ghidra.\n\nThis project welcomes further contributions, suggestions, and enhancements, including pull requests.\n\n\n## Contact\n\nFor more information, contact [@mr_phrazer](https://twitter.com/mr_phrazer).\n",
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        "longdescription": "# Rust String Slicer\n\nA Binary Ninja plugin to help find the addresses and lengths of strings in Rust binaries.\n\n![A screenshot of Binary Ninja with several structs of type \"RustStringSlice\" defined, each of which contains the address and length of a string.](images/sliced-string-screenshot-border.png)\n\n## Usage\n\nThis plugin provides two new commands:\n\n- _Plugins_ > _Rust String Slicer_ > _Recover String Slices from Readonly Data_\n- _Plugins_ > _Rust String Slicer_ > _Recover String Slices from Code_ \n\n![A screenshot of two entries in the Binary Ninja menu, both under Plugins > Rust String Slicer: Recover String Slices from Readonly Data, and Recover String Slices from Code](images/plugin-actions-border.png)\n\nYou can view the list of recovered strings and their addresses in the Log window.\n\n![The Binary Ninja log window, showing the following text logged under the \"rust_string_slicer.binja_plugin.actions\" category: Candidate string slice b'\\xc3\\xcc\\xcc\\xcc\\xcc\\xcc\\xcc\\xcc' does not decode to a valid UTF-8 string; excluding from final results: 'utf-8' codec can't decode byte 0xc3 in position 0: invalid continuation byte\nRecovered string at addr 0x18003d174, len 0x28: \"library\\core\\src\\unicode\\unicode_data.rs\"\nDefined new RustStringSlice at 0x18003d2c8\nRecovered string at addr 0x18003d174, len 0x28: \"library\\core\\src\\unicode\\unicode_data.rs\"\nDefined new RustStringSlice at 0x18003d2e0\nRecovered string at addr 0x180037ff4, len 0x2c: \"Ll0W8FiHZKeuZFO65DVloBXrq3wo05J9TDdw10yl78k=\"\nDefined new RustStringSlice at 0x180045010\nRecovered string at addr 0x180038020, len 0x2c: \"GONj/LrybW0VQXVpyZRIvZLFPOqUKx5aBVCmW0o4AoY=\"](images/recovered-strings-log-border.png)\n\nThe plugin will also create a new `RustStringSlice` type, for any strings defined in read-only data sections that are made up of a pointer to string data + the length of that string data. You can view all created strings of this type by examining cross-references to the `RustStringSlice` type.\n\n![The Binary Ninja Types and Cross References window, showing references to the RustStringSlice type, which has fields char* address and int64_t length. Cross references include variables with names like \"str_\"src\\lib.rs\"\", \"str_\"C:\\Users\\Administrator\\.cargo\\registry\"\", and \"str_\"Impossible: must only have 0 to 8 input bytes in last chunk, with no invalid lengths\"](images/cross-references-rust-string-slice-type-border.png)\n\n## How does this work?\n\nThe original motivation for this plugin was to recreate the string slicing functionality in the tech preview of the [official IDA Rust Analysis Plugin from Hex-Rays](https://hex-rays.com/blog/rust-analysis-plugin-tech-preview/). That plugin is able to find the lengths of Rust strings, which are not null terminated, via some heuristics for finding the string length data.\n\nThe heuristics we use are the following:\n\n- Enumerate all addresses in the binary's read-only data section which have cross-references.\n- Treating each of those addresses as the beginning of a potential string literal.\n- Follow those cross references to see if there is some information around the site of that cross reference about string length.\n\nThe _Recover String Slices from Readonly Data_ command uses cross references which point to a data section. For each of these cross references, try to interpret the cross referenced address as a structure like the following, with some heuristics to determine whether the found length_of_string_literal is reasonable:\n\n```\nQWORD address_of_candidate_string_literal\nQWORD length_of_string_literal\n```\n\n![](images/readonly-data-string-slices-border.png)\n\n\nThe _Recover String Slices from Code_ command uses cross references which point to a code section. For each of these cross references, check to see if the subsequent instructions contain a move of an immediate value to a memory location; the immediate value is then taken as the string length. For example, for x64 binaries, look for instructions like this:\n\n```\nlea rax, <address_of_candidate_string_literal>\nmov [rsp+<string_var>.<string_pointer_field>], rax\nmov [rsp+<string_var>.<string_length_field>], 15\n```\n\n![](images/code-string-slices-border.png)\n\n\n## Development\n\n### Setting up a development environment\n\nTo set up a development environment, including setting up a Python virtual environment:\n\n```\npython -m venv .venv && . .venv/bin/activate\npython -m pip install -r requirements.txt\npython -m pip install -r dev-requirements.txt\npython $PATH_TO_BINARY_NINJA_INSTALLATION/scripts/install_api.py\n```\n\nFor formatting, linting, and running unit tests locally, install [Nox](https://nox.thea.codes/en/stable/tutorial.html), then:\n\n```\nnox\n```\n\nYou can also invoke each task separately; see [noxfile.py](noxfile.py) for more details on available tasks:\n\n```\nnox -s format\nnox -s lint\nnox -s test\n```\n\nLinting and unit testing (both against multiple Python versions) are also set up in CI on [GitHub Actions](.github/workflows/ci.yml).\n\n### Testing local versions of the plugin\n\nTo test the plugin locally in your own Binary Ninja installation during development, create a symbolic link between your development folder, and the [Binary Ninja user plugins folder](https://docs.binary.ninja/guide/index.html#user-folder), so that your development folder is loaded by Binary Ninja on startup as a plugin.\n\n- MacOS:\n\n    ```sh\n    ln -s --relative . ~/Library/Application\\ Support/Binary\\ Ninja/plugins/rust_string_slicer\n    ```\n\n- Linux:\n\n    ```sh\n    ln -s --relative . ~/.binaryninja/plugins/rust_string_slicer\n    ```\n\n- Windows (Powershell):\n    ```powershell\n    New-Item -ItemType Junction -Value $(Get-Location) -Path \"$env:APPDATA\\Binary Ninja\\plugins\\rust_string_slicer\n    ```\n\nYou should then change the values of the following Python settings in Binary Ninja to point to inside your development folder's virtual environment:\n\n- `python.binaryOverride`: Set this to the path of the Python interpreter inside your development virtual environment, e.g. `$DEVELOPMENT_FOLDER/rust_string_slicer/.venv/bin/python/`\n- `python.virtualenv`: Set this to the path of the `site-packages` directory inside your development virtual environment, e.g. `$DEVELOPMENT_FOLDER/rust_string_slicer/.venv/lib/python3.11/site-packages`",
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        "longdescription": "# Binary Ninja Rust Type Layout Helper Plugin \ud83e\udd80\n\nAn extremely experimental Binary Ninja importer for the type layout information emitted by the [`-Zprint-type-sizes` flag](https://nnethercote.github.io/perf-book/type-sizes.html) of the Rust compiler.\n\nThis plugin is meant to help reverse engineers with the following:\n- Getting a sense of how, in general, Rust data structures are laid out in memory.\n- Getting more comfortable with certain core data structures which appear in Rust binaries.\n\n![A screenshot of Binary Ninja's Types view in the sidebar, showing the imported definitions and layouts of several Rust types from `std::sys::windows`.](images/std-sys-windows-types-border.png)\n\n## How to use this plugin\n\nCompile some Rust code with the following options:\n\nMacOS / Linux:\n\n```sh\ncargo clean\nRUSTFLAGS=-Zprint-type-sizes cargo +nightly build -j 1 > type-sizes.txt\n```\n\nWindows (Powershell):\n\n```powershell\ncargo clean\n$env:RUSTFLAGS=\"-Zprint-type-sizes\"; cargo +nightly build -j 1 > type-sizes.txt\n```\n\nThe following are all necessary for this to work:\n- `cargo clean` is required before you do the build, i.e. this needs to be a completely fresh build. This is required to avoid missing information in the output.\n- `-Zprint-type-sizes` in the `RUSTFLAGS` passed to rustc. This flag is what actually triggers `rustc` to produce the type information.\n- `+nightly` passed to cargo, as the `print-type-sizes` flag is only supported on nightly toolchain builds.\n- `-j 1` to avoid shuffled lines in the output.\n\nYou should see output like this in the generated `type-sizes.txt` file:\n\n```\nprint-type-size type: `core::num::dec2flt::decimal::Decimal`: 784 bytes, alignment: 8 bytes\nprint-type-size     field `.digits`: 768 bytes\nprint-type-size     field `.num_digits`: 8 bytes\nprint-type-size     field `.decimal_point`: 4 bytes\nprint-type-size     field `.truncated`: 1 bytes\nprint-type-size     end padding: 3 bytes\nprint-type-size type: `std::result::Result<std::sys::windows::fs::ReadDir, std::io::Error>`: 616 bytes, alignment: 8 bytes\nprint-type-size     variant `Ok`: 616 bytes\nprint-type-size         field `.0`: 616 bytes\nprint-type-size     variant `Err`: 8 bytes\nprint-type-size         field `.0`: 8 bytes\nprint-type-size type: `std::sys::windows::fs::ReadDir`: 616 bytes, alignment: 8 bytes\nprint-type-size     field `.handle`: 8 bytes\nprint-type-size     field `.root`: 8 bytes\nprint-type-size     field `.first`: 596 bytes\nprint-type-size     end padding: 4 bytes\nprint-type-size type: `std::option::Option<std::result::Result<std::fs::DirEntry, std::io::Error>>`: 608 bytes, alignment: 8 bytes\nprint-type-size     discriminant: 8 bytes\nprint-type-size     variant `Some`: 600 bytes\nprint-type-size         field `.0`: 600 bytes\nprint-type-size     variant `None`: 0 bytes\n[...]\n```\n\nYou can now use the _Plugins > Rust Type Layout Helper - Load File..._ command to import the contents of this file into Binary Ninja. The following types in Binary Ninja wil be created from the types shown in the example above:\n\n```c\nstruct core::num::dec2flt::decimal::Decimal __packed\n{\n    char .digits[0x300];\n    int64_t .num_digits;\n    int32_t .decimal_point;\n    char .truncated;\n    char _padding[0x3];\n};\n\nstruct std::result::Result<std::sys::windows::fs::ReadDir, std::io::Error> __packed\n{\n    union __packed\n    {\n        struct std::result::Result<std::sys::windows::fs::ReadDir, std::io::Error>::Ok Ok;\n        struct std::result::Result<std::sys::windows::fs::ReadDir, std::io::Error>::Err Err;\n    } std::result::Result<std::sys::windows::fs::ReadDir, std::io::Error>::variants;\n};\n\nstruct std::result::Result<std::sys::windows::fs::ReadDir, std::io::Error>::Err __packed\n{\n    int64_t .0;\n};\n\nstruct std::result::Result<std::sys::windows::fs::ReadDir, std::io::Error>::Ok __packed\n{\n    char .0[0x268];\n};\n\nstruct std::sys::windows::fs::ReadDir __packed\n{\n    int64_t .handle;\n    int64_t .root;\n    char .first[0x254];\n    int32_t _padding;\n};\n\nstruct std::option::Option<std::result::Result<std::fs::DirEntry, std::io::Error>> __packed\n{\n    enum std::option::Option<std::result::Result<std::fs::DirEntry, std::io::Error>>::discriminant discriminant;\n    union __packed\n    {\n        struct std::option::Option<std::result::Result<std::fs::DirEntry, std::io::Error>>::Some Some;\n        struct std::option::Option<std::result::Result<std::fs::DirEntry, std::io::Error>>::None None;\n    } std::option::Option<std::result::Result<std::fs::DirEntry, std::io::Error>>::variants;\n};\n\nstruct std::option::Option<std::result::Result<std::fs::DirEntry, std::io::Error>>::None __packed\n{\n};\n\nstruct std::option::Option<std::result::Result<std::fs::DirEntry, std::io::Error>>::Some __packed\n{\n    char .0[0x258];\n};\n\nenum std::option::Option<std::result::Result<std::fs::DirEntry, std::io::Error>>::discriminant : uint64_t\n{\n    Some = 0xffffffffffffffff,\n    None = 0xffffffffffffffff\n};\n```\n\n## Caveats and future work\n\nThere are some caveats to using this:\n- The layout of data types is not stable, and can change between compilations!\n- Only the nightly builds of rustc supports the `print-type-sizes` flag.\n- Binary Ninja's support for working with unions in the decompilation is currently quite poor (see [Vector35/binaryninja-api#1013](https://github.com/Vector35/binaryninja-api/issues/1013), [Vector35/binaryninja-api#4218](https://github.com/Vector35/binaryninja-api/issues/4218)). This may make it difficult to work with the generated `variants` unions, such as `std::option::Option<std::result::Result<std::fs::DirEntry, std::io::Error>>::variants` in the example above.\n- When importing sum types (i.e. Rust enums), the discriminant value used to represent each variant in the sum type does not necessarily match the ordering of those variant in the type layout information, i.e. the first variant is not necessarily discriminant value 0, etc. The information emitted by rustc's `print-type-sizes` flag also does not include the discriminant value for each variant. Therefore, all variants are assigned a discriminant value of -1. To determine the actual determinant value, it is up to the user to reverse the code where the sum type is used.\n\nIn the future it would be nice to:\n- Add scripts / plugins to import the type information into IDA and Ghidra.\n- Use a Rust compiler plugin to emit better type information than we get from `-Zprint-type-sizes`? Maybe a combination of the information we get from `-Zprint-type-sizes` and `#[rustc_layout(...)]`. It would also be nice to emit the type information in a format which is slightly easier to parse (e.g. JSON).\n\n## Installation\n\nThis plugin can be installed via either:\n\n1) Searching for the _Rust Type Layout Helper_ plugin in Binary Ninja's built-in plugin manager (_Plugins > Manage Plugins_). _This is the recommended method._\n\n2) Cloning this repository into your user plugins folder.\n    - The [location of the user plugins folder will vary depending on the platform Binary Ninja is installed on](https://docs.binary.ninja/guide/index.html#user-folder). The easiest way to find the location of the folder is via the _Plugins > Open Plugin Folder..._ command.\n    - If you are performing an installation via this method, you must also install this plugin's Python dependencies manually. This can be done by either:\n        - Running the _Install python3 module..._ command (via the Command Palette), and pasting the contents of [`requirements.txt`](requirements.txt) in this repository into the dialog window.\n        - Running `pip install -r requirements.txt` in the Python environment used by Binary Ninja.\n\nThis plugin requires Python >= 3.7, and Binary Ninja version >= 3.2.3814.\n\n## Development\n\n### Setting up a development environment\n\nTo set up a development environment, including setting up a Python virtual environment:\n\n```\npython -m venv .venv && . .venv/bin/activate\npip install -r requirements.txt\npip install -r dev-requirements.txt\npython $PATH_TO_BINARY_NINJA_INSTALLATION/scripts/install_api.py\n```\n\nFor formatting, linting, and running unit tests locally, install [Nox](https://nox.thea.codes/en/stable/tutorial.html), then:\n\n```\nnox\n```\n\nYou can also invoke each task separately; see [noxfile.py](noxfile.py) for more details on available tasks:\n\n```\nnox -s format\nnox -s lint\nnox -s test\n```\n\nLinting and unit testing (both against multiple Python versions) are also set up in CI on [GitHub Actions](.github/workflows/ci.yml).\n\n### Testing local versions of the plugin\n\nTo test the plugin locally in your own Binary Ninja installation during development, create a symbolic link between your development folder, and the [Binary Ninja user plugins folder](https://docs.binary.ninja/guide/index.html#user-folder), so that your development folder is loaded by Binary Ninja on startup as a plugin.\n\n- MacOS:\n\n    ```sh\n    ln -s --relative . ~/Library/Application\\ Support/Binary\\ Ninja/plugins/rust_type_layout_helper\n    ```\n\n- Linux:\n\n    ```sh\n    ln -s --relative . ~/.binaryninja/plugins/rust_type_layout_helper\n    ```\n\n- Windows (Powershell):\n    ```powershell\n    New-Item -ItemType Junction -Value . -Path \"$env:APPDATA\\Binary Ninja\\plugins\\rust_type_layout_helper\"\n    ```\n\nYou should then change the values of the following Python settings in Binary Ninja to point to inside your development folder's virtual environment:\n\n- `python.binaryOverride`: Set this to the path of the Python interpreter inside your development virtual environment, e.g. `$DEVELOPMENT_FOLDER/rust_type_layout_helper/.venv/bin/python/`\n- `python.virtualenv`: Set this to the path of the `site-packages` directory inside your development virtual environment, e.g. `$DEVELOPMENT_FOLDER/rust_type_layout_helper/.venv/lib/python3.11/site-packages`\n\n## Acknowledgements and resources\n\nThe compilation instructions for emitting type information are taken from the instructions in the [`top-type-sizes` crate, by Paul Loyd](https://github.com/loyd/top-type-sizes).",
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        "longdescription": "# #\ufe0f\u20e3 Binary Ninja HashDB Plugin\n\n[HashDB](https://github.com/OALabs/hashdb) is a community-sourced library of hashing algorithms used in malware. This plugin queries the [OALabs HashDB Lookup Service](https://hashdb.openanalysis.net/) for hash values which appear in the currently analyzed file, fetches a list of strings which match those hashes, and collects the string values into an enum type definition. The defined enum type can then be applied to the binary for further analysis.\n\n## Usage\n\n### Looking up a hash (_HashDB - Hash Lookup_)\n\nThe _HashDB - Hash Lookup_ command looks up the hash value highlighted by the cursor.\n\n![](images/hashlookup-screenshot-border.png)\n\n![](images/hashlookup-result-screenshot-border.png)\n\n### Hunting for the correct hash algorithm (_HashDB - Hunt_)\n\nThe _HashDB - Hunt_ command attempts to identify the hash algorithm which was used to generate a particular hash value. 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The names and values of the found hashes can be viewed in the _Types_ menu.\n\n![](images/hash-created-enum-screenshot-border.png)\n\nThe enum type can then be applied to variables in the database.\n\n![](images/hash-created-enum-applied-function-arg-screenshot-border.png)\n\nIf you are using Binary Ninja >= 3.3, you can also apply the resolved hash names using the new _Select Enum_ dialog (accessible by pressing the `M` key, or through _Display as_ > _Enum Member_ in the context menu).\n\n![](images/hash-enum-selection-dialog-screenshot-border.png)\n\nThe name prefix used in the enum type (by default `hashdb_strings`) can be changed in Binary Ninja's settings, under _HashDB > HashDB Enum Name_.\n\n### Looking up multiple hashes (_HashDB - Multiple Hash Lookup_)\n\nMultiple hashes can be selected and looked up at once using the _HashDB - Multiple Hash Lookup_ command.\n\n![](images/multiple-hash-lookup-screenshot-border.png)\n\nThe resolved hashes can then be applied by setting the selected variables to the created enum type.\n\n![](images/multiple-hash-lookup-enum-type-applied-screenshot-border.png)\n\n### Settings\n\nThe settings for this plugin can be changed from Binary Ninja's Settings interface (_Edit > Preferences > Settings_). The settings list can be filtered to show only the HashDB plugin's settings by searching `HashDB` in the Settings search box.\n\nThe following settings are applied globally:\n\n- _HashDB API URL_ (`hashdb.url`). If you wish to use a different HashDB instance than the default OALabs HashDB, such as an internally hosted version, you can change this setting to point at your desired instance.\n- _HashDB Enum Name_ (`hashdb.enum_name`)\n\nThe following settings are specific to an individual binary. They are saved in the analysis database (`.bndb` file) for that binary, and will be restored the next time you open the analysis database.\n\n- _HashDB Hash Algorithm_ (`hashdb.algorithm`)\n- _HashDB Hash Algorithm Data Type_ (`hashdb.algorithm_type`)\n\n![](images/settings-screenshot-border.png)\n\n### Keyboard shortcuts\n\nKeyboard shortcuts can be set for this plugin's commands from Binary Ninja's Keybindings interface (_Edit > Preferences > Keybindings_). The command list can be filtered to show only the HashDB plugin's commands by searching `HashDB` in the Keybindings search box.\n\nThe plugin currently does not ship with any keyboard shortcuts set by default.\n\n## Installation\n\nThis plugin can be installed via either:\n\n1) Searching for the _HashDB_ plugin in Binary Ninja's built-in plugin manager (_Plugins > Manage Plugins_). _This is the recommended method._\n\n2) Cloning this repository into your user plugins folder.\n    - The [location of the user plugins folder will vary depending on the platform Binary Ninja is installed on](https://docs.binary.ninja/guide/index.html#user-folder). The easiest way to find the location of the folder is via the _Plugins > Open Plugin Folder..._ command.\n    - If you are performing an installation via this method, you must also install this plugin's Python dependencies manually. This can be done by either:\n        - Running the _Install python3 module..._ command (via the Command Palette), and pasting the contents of [`requirements.txt`](requirements.txt) in this repository into the dialog window.\n        - Running `pip install -r requirements.txt` in the Python environment used by Binary Ninja.\n\nThis plugin requires Python >= 3.7, and Binary Ninja version >= 2.4.2846.\n\n## License\n\nThis plugin is released under a 3-Clause BSD license.\n\nThis plugin is a derivative work of the [IDA Plugin](https://github.com/OALabs/hashdb-ida/) from [OALabs](https://oalabs.openanalysis.net/) for connecting to their [HashDB service](https://hashdb.openanalysis.net/), and is forked from Vector 35's initial implementation at [psifertex/hashdb-bn](https://github.com/psifertex/hashdb-bn).",
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            "Linux": "Installation via the Binary Ninja plugin manager is recommended. For alternative installation instructions, see the [Installation section in the README on the GitHub repository for this plugin](https://github.com/cxiao/hashdb_bn#installation).",
            "Windows": "Installation via the Binary Ninja plugin manager is recommended. For alternative installation instructions, see the [Installation section in the README on the GitHub repository for this plugin](https://github.com/cxiao/hashdb_bn#installation)."
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It probably works on Windows;\nplease submit a pull request if you've tested it.\n\n### Dependencies\n\n- Python 3.10+\n- `openai` installed with `pip3 install --user openai`\n\n## API Key\n\nThis requires an [API token from OpenAI][token]. The plugin checks for the API\nkey in three ways (in this order).\n\nFirst, it tries to read the key from Binary Ninja's preferences. You can\naccess the entry in Binary Ninja via `Edit > Preferences > Settings > OpenAI`.\nOr, use the hotkey \u2318+, and search for `OpenAI`. You should see customizable\nsettings like so.\n\n![Settings](https://github.com/WhatTheFuzz/binaryninja-openai/blob/main/resources/settings.png?raw=true)\n\nSecond, it checks the environment variable `OPENAI_API_KEY`, which you can set\ninside of Binary Ninja's Python console like so:\n\n```python\nimport os\nos.environ[\"OPENAI_API_KEY\"] = \"INSERT KEY HERE\"\n```\n\nOr you can write it to a file. The file is set in [entry.py][entry] and is a\nparameter to the Agent class. By default it checks for the file\n`~/.openai/api_key.txt`. You can add your API token like so:\n\n```shell\nmkdir ~/.openai\necho -n \"INSERT KEY HERE\" > ~/.openai/api_key.txt\n```\n\nNote that if you have all three set, the plugin defaults to one set in Binary\nNinja. If your API token is invalid, you'll receive the following error:\n\n```python\nopenai.error.AuthenticationError: Incorrect API key provided: <BAD KEY HERE>.\nYou can find your API key at https://beta.openai.com.\n```\n\n## Usage\n\n### What Does this Function Do?\n\nAfter installation, you can right-click on any function in Binary Ninja and\nselect `Plugins > OpenAI > What Does this Function Do (HLIL/Pseudo-C)?`.\nAlternatively, select a function in Binary Ninja (by clicking on any instruction\nin the function) and use the menu bar options `Plugins > OpenAI > ...`. 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Right click on an instruction where a\nvariable is initialized and select `OpenAI > Rename Variable (HLIL)`. Watch the\nmagic happen. Here's a quick before-and-after.\n\n![Before renaming](https://github.com/WhatTheFuzz/binaryninja-openai/blob/main/resources/rename-before.png?raw=true)\n\n![After renaming](https://github.com/WhatTheFuzz/binaryninja-openai/blob/main/resources/rename-after.png?raw=true)\n\nRenaming variables only works on HLIL instructions that are initializations (ie.\n`HighLevelILVarInit`). You might also want this to support assignments\n(`HighLevelILAssign`), but I did not get great results with this. Most of the\nresponses were just `result`. If your experience is different, please submit a\npull request.\n\n## OpenAI Model\n\nBy default, the plugin uses the `text-davinci-003` model, you can tweak this\ninside Binary Ninja's preferences. You can access these settings as described in\nthe [API Key](#api-key) section. 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        "description": "A disassembler and lifter for the Ethereum architecture.",
        "longdescription": "# ethersplay\nBinary Ninja plugin which enables an EVM disassembler and related analysis tools.\n\n![Example](/images/example.png)\n\n- [Installation](#installation)\n- [How to use](#how-to-use)\n- [Automatic Analyses](#automatic-analyses)\n- [Plugins](#plugins)\n- [Known issues](#known-issues)\n\n## Installation\n**Ethersplay only supports Python >= 3.6.**\n\nEnsure that your Binary Ninja's Python library is set to Python 3.6+. You can change the ScriptingProvider in the Advanced Settings.\n\nInstall the dependencies:\n```console\n$ pip install -r requirements.txt\n```\n\nCreate a symbolic link to the Binary Ninja [plugin folder](https://github.com/Vector35/binaryninja-api/tree/dev/python/examples#loading-plugins).\nE.g., in macOS\n```\ncd ~/Library/Application\\ Support/Binary\\ Ninja/plugins\nln -s <your_download_location>/ethersplay/ethersplay .\n```\n\nUbuntu:\n```\ncd ~/.binaryninja/plugins\nln -s <your_download_location>/ethersplay/ethersplay .\n```\n\n## How to Use\n\nEthersplay takes as input the evm bytecode in raw binary format.\n \nTo have the bytecode of a solidity file, use `solc`:\n- `solc --bin-runtime file.sol`: to print the bytecode of the runtime part of the contract (for most of the cases).\n- `solc --bin file.sol`: to print the initialisation bytecode of the contract (constructor),\n\n\n\nExample using `test.sol` with following contents:\n```test.sol:\ncontract Test {\n    uint256 value;\n    function Test() {\n        value = 5;\n    }\n    function set_value(uint256 v) {\n        value = v;\n    }\n    function() payable {}\n}\n```\n\nRun solidity to compile:\n`solc --bin-runtime test.sol`\n\nsolc prints the bytecode to stdout in the format below:\n```\n======= test.sol:Test =======\nBinary of the runtime part:\n60606040523615603d576000357c0100000000000000000000000000000000000000000000000000000000900463ffffffff168063b0f2b72a146041575b5b5b005b3415604b57600080fd5b605f60048080359060200190919050506061565b005b806000819055505b505600a165627a7a72305820c177a64bf54a26574918ddc2201f7ab2dd8619d6c3ee87ce9aaa1eb0e0b1d4650029\n```\n\nCopy the ascii hex string, and then create a new file in Binary Ninja. Right-click and select `Paste From -> Raw Hex`. The output should look identical to the earlier example image. Save this file as `test.evm` and close it. Alternatively, paste the ascii hex string into a new text file, and run the `utils/convert_bytecode.py` on that file.\n\n`test.evm` can now be loaded into Binary Ninja.\n\n**Note:** The file must end in `.evm` to be recognized as an EVM bytecode binary file.\n\n## Plugins\n\n### Render Flowgraphs\nGenerates a clean control flow graph of all functions.\n\nBefore:\n![before](images/cfg_before.png)\n\nAfter:\n![!after](images/cfg_after.png)\n\n### Manticore coverage\nColors the basic blocks explored through Manticore (using the `visited.txt` or `*.trace` files).\n",
        "license": {
            "name": "AGPL-3.0-only",
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If the Program does not specify a version number of the\nGNU Affero General Public License, you may choose any version ever published\nby the Free Software Foundation.\n\n  If the Program specifies that a proxy can decide which future\nversions of the GNU Affero General Public License can be used, that proxy's\npublic statement of acceptance of a version permanently authorizes you\nto choose that version for the Program.\n\n  Later license versions may give you additional or different\npermissions.  However, no additional obligations are imposed on any\nauthor or copyright holder as a result of your choosing to follow a\nlater version.\n\n  15. Disclaimer of Warranty.\n\n  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY\nAPPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT\nHOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM \"AS IS\" WITHOUT WARRANTY\nOF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,\nTHE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\nPURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM\nIS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF\nALL NECESSARY SERVICING, REPAIR OR CORRECTION.\n\n  16. Limitation of Liability.\n\n  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING\nWILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS\nTHE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY\nGENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE\nUSE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF\nDATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD\nPARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),\nEVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF\nSUCH DAMAGES.\n\n  17. Interpretation of Sections 15 and 16.\n\n  If the disclaimer of warranty and limitation of liability provided\nabove cannot be given local legal effect according to their terms,\nreviewing courts shall apply local law that most closely approximates\nan absolute waiver of all civil liability in connection with the\nProgram, unless a warranty or assumption of liability accompanies a\ncopy of the Program in return for a fee.\n\n                     END OF TERMS AND CONDITIONS\n\n            How to Apply These Terms to Your New Programs\n\n  If you develop a new program, and you want it to be of the greatest\npossible use to the public, the best way to achieve this is to make it\nfree software which everyone can redistribute and change under these terms.\n\n  To do so, attach the following notices to the program.  It is safest\nto attach them to the start of each source file to most effectively\nstate the exclusion of warranty; and each file should have at least\nthe \"copyright\" line and a pointer to where the full notice is found.\n\n    <one line to give the program's name and a brief idea of what it does.>\n    Copyright (C) <year>  <name of author>\n\n    This program is free software: you can redistribute it and/or modify\n    it under the terms of the GNU Affero General Public License as published by\n    the Free Software Foundation, either version 3 of the License, or\n    (at your option) any later version.\n\n    This program is distributed in the hope that it will be useful,\n    but WITHOUT ANY WARRANTY; without even the implied warranty of\n    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n    GNU Affero General Public License for more details.\n\n    You should have received a copy of the GNU Affero General Public License\n    along with this program.  If not, see <https://www.gnu.org/licenses/>.\n\nAlso add information on how to contact you by electronic and paper mail.\n\n  If your software can interact with users remotely through a computer\nnetwork, you should also make sure that it provides a way for users to\nget its source.  For example, if your program is a web application, its\ninterface could display a \"Source\" link that leads users to an archive\nof the code.  There are many ways you could offer source, and different\nsolutions will be better for different programs; see section 13 for the\nspecific requirements.\n\n  You should also get your employer (if you work as a programmer) or school,\nif any, to sign a \"copyright disclaimer\" for the program, if necessary.\nFor more information on this, and how to apply and follow the GNU AGPL, see\n<https://www.gnu.org/licenses/>."
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        "longdescription": "# Ariadne: Binary Ninja Graph Analysis Plugin\n\n[Ariadne](https://en.wikipedia.org/wiki/Ariadne#Mythology) is a Binary Ninja plugin that serves a browser-based interactive graph\nvisualization for assisting reverse engineers. It implements some common static\nanalysis tasks including call graph analysis, and can integrate block coverage\ninformation. This enables users to build interactive graphs and see exactly what\nthey are interested in.\n\n![Demo Screen Capture](screenshots/demo.gif)\n\n## Quickstart\n\n1. Install this plugin via the Plugin Manager, OR clone this repo to your Binary\n  Ninja [plugins folder](https://docs.binary.ninja/getting-started.html#user-folder)\n  - NOTE: If you install by cloning the repo, you may need to install the Python\n  dependencies in `requirements.txt`. Use the command palette (`CTRL+P` or\n  equivalent) to do `Install Python3 module` and copy the contents of\n  requirements.txt into the dialog.\n2. Open a binary in Binary Ninja\n3. Right click: Plugins > Ariadne > Analyze Target\n4. Open the Ariadne Sidebar (`A` icon in upper right by default) and wait for analysis to complete (watch log for issues)\n5. Open a browser and surf to `http://localhost:8800` to view the interactive\n   graph (web UI)\n6. Navigate around in Binary Ninja; the graph will update when the current\n   function changes\n\n## Graph Styling\n\nThe quick rundown on what the shapes and colors on the graph mean:\n\n- Regular functions are green circles\n- Import functions are diamond-shaped and colored orange\n- Node size is based on cyclomatic complexity; more complex functions are\n  bigger circles\n- The current function active in BN is colored red\n- Nodes with double borders mean they have edges that weren't included for the\n  current graph (default: local neighborhood for active function in BN, see note\n  below)\n- Functions that you've looked at in the BN UI have light blue borders\n- If you click on a node, it becomes the \"focus node\"\n  - The focus node is colored purple\n  - Out edges/nodes (callees) are colored pink\n  - In edges/nodes (calleRs) are colored blue\n  - Clicking on the focus node deselects it\n  - Clicking on another node makes that node the focus node\n\nNOTE: the default graph is a 2-hop neighborhood of the current function _BUT_ it\nwill be automatically pruned to a smaller graph if two hops would include too\nmany nodes. Use the context menu function graph to push the full context for\nthe current function or use\n[networkx](https://networkx.org/) to build custom graphs and push them to\nthe web UI.\n\n## Motivation\n\n[Longer blog post on motivation](https://seeinglogic.com/posts/why-of-ariadne/)\n\nThis tool is a proof-of-concept that was built to fill a gap that we observed in\nour own reverse-engineering workflows, with the goals of being highly\nconfigurable and to help make reverse-engineering faster.\n\nThe key insight we found building/using a graph tool is that looking at too many\nnodes is unhelpful and layout matters a lot, so we focused on just the analysis\nresults we wanted in the smallest and cleanest view possible.\n\nFrom there, we built the backend so any graph could be pushed to the backend and\ncommon graph tasks would be easy. Adding extra analysis tasks is also easy since\nthere are places for per-function and target-wide analysis.\n\n## Common Workflows\n\nAriadne was built to handle some common workflows encountered in RE and fuzzing:\n\n- Source/Sink analysis: Context command allows you to select a function and see\n  all the paths to/from the current function in the web UI. ![source-sink](screenshots/source_sink.png)\n- Coverage analysis via [bncov](https://github.com/ForAllSecure/bncov): allows\n  visualization of coverage and shows where your coverage stops and uncovered\n  complexity resides. Requires bncov, but if coverage information is detected\n  before analysis starts it will automatically be added, or it can be added\n  separately. [More in-depth post on coverage automation and Ariadne](https://seeinglogic.com/posts/automated-coverage-analysis/)\n  - The Descendent Complexity metrics (\"uncovered\" as well as the normal one) show\n    the sum of complexity for all functions reachable from a given function as\n    well as the sum for just functions with zero coverage. Very useful for\n    fuzzing!\n  ![Coverage View](screenshots/coverage_analysis.png)\n- Import Hiding: Sometimes imports are helpful, other times they just convolute\n  the graph because it's more important to see just the internal functions\n- Custom graphs: create any graph based on the target's graph (`ariadne.core.targets[bv].g`) and push it to the web UI with `ariadne.core.push_new_graph(new_graph)`\n- Standard styling: the default graph styling allows you to see which functions\n  you have already looked at, which functions are imports, and caller/callee\n  relationships. Helps you see which functions you haven't looked at that may be\n  of interest. ![Breadcrumb demo](screenshots/breadcrumbing.png)\n- Collapsible Function Metadata sidebar: Shows all the relevant static analysis\n  results for any function you click on. ![Focus node view](screenshots/focus_node.png)\n- Function search bar: start typing the name of the function you want to find in\n  the search bar in the upper left, when the name turns green you can stop\n  typing and hit enter to center the graph on the target function.\n- Freezing/unfreezing the graph: sometimes you don't want auto-updates\n- Save/Load analysis: redoing analysis is no good; headless analysis and\n  save/load features allow you to crunch binaries on a separate machine if you\n  want.\n- Callgraph exploration: using the web UI's `Graph Focus Function` button, now\n  you can see what nodes aren't fully expanded in the current view and navigate\n  between functions from within the web UI.\n\nSee the [tutorial](./tutorial/README.md) for detailed explanation of features\nand intended workflows that you can test out on an example binary.\n\n## Troubleshooting\n\nIf the web UI is unresponsive, check the websocket status in the upper right\ncorner. If you push a really large graph to the web UI, the page may freeze\nwhile the graph layout is computed. In any case, refreshing the page should\nreset the UI.\n\nUnhandled Python exceptions on startup or during processing are bugs and it'd be\ngreat if you would open a GitHub issue on the repo here and describe the problem\n(and include a binary to reproduce the problem, if possible).\n\n...And of course, PR's are always welcome!\n\n## Thank you!\n\nTo everyone who tries out this tool, it would mean a lot to me if you reach out\nand give me your thoughts on [Twitter](https://twitter.com/seeinglogic) or starring this repo. I\nhope this helps you or gives you ideas on how to look at things a little\ndifferently.\n",
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A few examples:\n\n* A CTF challenge has a custom encoding/decoding scheme you need to use in your solution script\n* A piece of malware uses a custom hashing or encryption function you need to implement\n* You need to make sure your reimplementation behaves _exactly_ as it would on the original architecture\n\nripr attempts to automatically generate a python class that is functionally identical to a selected piece of code by statically gathering sufficient information and wrapping it all into a \"harness\" for the unicorn emulator. \n\nFor some concrete examples, check out the `sample` folder!\n\n# Installation\nThe manual installation process looks like this:\n\n1. Clone this repo to your local machine\n2. Find the location of your plugins directory from Binary Ninja by clicking on `Tools --> Open Plugin Folder...`\n3. 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How should this be handled?\n\nChoosing \"Hook\" will allow you to write-in your own functionality that runs in lieu of imported functions. Selecting \"Nop out Calls\" will replace the call instruction with a series of NOPs.\n\n#### Target code may depend on outside code. Attempt to map automatically?\n\nYour selected code contains calls to other functions within the binary. Answering yes will automatically map those functions.\n\n#### Use Section Marking Mode for data dependencies?\n\nAnswering yes will map all sections of the binary that are touched by the target code. Answering No will use Page-Marking mode, where every page used by the target code is mapped into emulator memory.\n\n## Using a ripr \"package\"\nOnce a selection of code has been packaged, you will have a python class which encapsulates its functionality. The basic process of using it looks like this:\n\n1. Instantiate the class\n2. Call the run() method\n\nAssuming `my_ripped_code` is the class name:\n\n```python\nx = my_ripped_code()\ny = x.run()\n```\n\nAll Unicorn functionality is exposed via the `mu` attribute and should work as expected. \n\n## Implementing \"Imported Calls\"\nIf you choose to hook calls to `imported functions` during the packaging stage, your generated class will contain stub-functions that are called when the imported call would originally have been called.\n\nFor example, if your code contained calls to `puts` and `malloc`, the following would be generated in your class:\n```python\ndef hook_puts(self):\n    pass\ndef hook_malloc(self):\n    pass\n```\nAny code you write within these functions will be called in lieu of the actual imported call. If you wanted a reasonable approximation of `puts` (and were emulating x64 code), you could do:\n\n```python\ndef hook_puts(self):\n    addr = self.mu.reg_read(UC_X86_REG_RDI)\n    mem = self.mu.mem_read(addr, 0x200)\n    print \"%s\" % (mem.split(\"\\x00\")[0])\n```\n\nYou have full access to all of Unicorn's methods via the `mu` attribute so it is possible to update the emulator context in any way necessary in order to mimic the behavior of a call or perform any actions you'd like instead of the call.\n\n## Function Arguments\nripr has some support for automatically generating \"argument aware\" output. 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        "longdescription": "# Hyara\n\n![Version](https://img.shields.io/badge/version-2.3-blue.svg?cacheSeconds=2592000)\n\n![](https://github.com/hyuunnn/Hyara/blob/master/images/Hyara.gif?raw=true)\n\n> Hyara is plugin that provides convenience when writing yararule.\n> \n> The plugin is currently undergoing a major revision!\n\n- [Demo video](https://youtu.be/zgL4BkQJZ-w)\n\n- [IDA Plugin Contest 2018](https://hex-rays.com/contests_details/contest2018/#Hyara)\n\n## Instructions\n\n### Start Screen and Options\n\n- When you run Hyara, it docks itself to the right and docks the output window to the left.\n- After specifying the address, press the `Make` button to show the specified hexadecimal or strings as a result.\n- The results are saved in the table below when you click `Save`.\n- If you double-click the table, you can clear the rule.\n- You can modify the values to wildcards by right clicking after dragging.\n\n![](https://github.com/hyuunnn/Hyara/blob/master/images/wildcard_0.png?raw=true)\n\n- `Export Yara Rule`\n  - Exports the previously created yara rules.\n\n![](https://github.com/hyuunnn/Hyara/blob/master/images/Hyara_1.png?raw=true)\n\n\n- `Right Click`\n  - You can select either start address or end address. (IDA Pro, Cutter)\n\n![](https://github.com/hyuunnn/Hyara/blob/master/images/Hyara_7.png?raw=true)\n  \n- `Comment Option`\n  - Annotates the instructions next to the condition rule(s).\n- `Rich Header` and `imphash`\n  - Adds rich header and imphash matching to the rule.\n- `String option`\n  - This option extracts strings within the range specified.\n\n![](https://github.com/hyuunnn/Hyara/blob/master/images/Hyara_3.png?raw=true)\n![](https://github.com/hyuunnn/Hyara/blob/master/images/cutter_1.png?raw=true)\n\n## Installation\n\n### IDA Pro & BinaryNinja\n\n- IDA Pro\n  ```bash\n  pip install -r requirements.txt\n  ```\n  - copy ``Hyara_IDA.py and hyara_lib folder`` to $ida_dir/plugins\n  - Activate via Edit -> Plugins -> Hyara (or CTRL+SHIFT+Y)\n\n- BinaryNinja\n  - Just use the plugin manager!\n  - Activate via View -> Other Docks -> Show Hyara\n\n### Cutter\n\n- Windows\n\nCheck the python version installed in the cutter and install it.\n\n![](https://github.com/hyuunnn/Hyara/blob/master/images/cutter_0.png?raw=true)\n\n```bash\nC:\\\\Users\\\\User\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python3X\\\\python.exe -m pip install -I -t $cutter_dir/python3X/site-packages -r requirements.txt\n```\n\ncopy ``__init__.py, Hyara_Cutter.py and hyara_lib folder`` to $cutter_dir/plugins/python/Hyara\n\n- Linux\n\n![](https://github.com/hyuunnn/Hyara/blob/master/images/cutter_install__1.png?raw=true)\n\n```bash\ncp -r /tmp/.mount_Cutter5o3a5G/usr /root\n```\n\nCheck the python version installed in the cutter and install it.\n\n![](https://github.com/hyuunnn/Hyara/blob/master/images/cutter_01.png?raw=true)\n\n```bash\npip3.X install -I -t /root/usr/lib/python3.X/site-packages -r /root/Hyara/requirements.txt\n./Cutter-v2.0.3-x64.Linux.AppImage --pythonhome /root/usr\n```\n\ncopy ``__init__.py, Hyara_Cutter.py and hyara_lib folder`` to /root/.local/share/rizin/cutter/plugins/python/Hyara\n\nActivate via Windows -> Plugins -> Hyara\n\n![](https://github.com/hyuunnn/Hyara/blob/master/images/cutter__0.png?raw=true)\n\n### Ghidra (WIP)\n\nInstall <a href=\"https://github.com/mandiant/Ghidrathon\">Ghidrathon</a> (<a href=\"https://youtu.be/Aatbqf6lcjU\">Installation Guide</a>) to use Hyara Plugin.\n\n```bash\npip install PySide2 or pip install PySide6\n```\n\n- Windows\n\ncopy ``Hyara_Ghidra.py and hyara_lib folder`` to ``C:\\\\Users\\\\User\\\\.ghidra\\\\.ghidra.X.X.X\\\\Extensions\\\\Ghidrathon-X.X.X\\\\data\\\\python\\\\``\n\n```python\n# Window -> Ghidrathon\nimport Hyara_Ghidra\nHyara_Ghidra.run()\n```\n\n![](https://github.com/hyuunnn/Hyara/blob/master/images/ghidra_0.png?raw=true)\n\n\n## Features\n\n- GUI-based\n- Supports IDA, BinaryNinja, Cutter and Ghidra.\n- YaraChecker\n  - Tests the yararule on the fly.\n  - ![](https://github.com/hyuunnn/Hyara/blob/master/images/Hyara_4.png?raw=true)\n- YaraDetector\n  - Shows which part is detected in the sample loaded to disassembler, and when \"Address\" is clicked, it moves to the corresponding address on the disassembler view.\n  - ![](https://github.com/hyuunnn/Hyara/blob/master/images/Hyara_5.png?raw=true)\n- YaraIcon\n  - Creates yara rules for icon resources embedded in the PE.\n  - ![](https://github.com/hyuunnn/Hyara/blob/master/images/Hyara_6.png?raw=true)\n\n## Author\n\n\ud83d\udc64 **hyuunnn**\n\n* Github: [@hyuunnn](https://github.com/hyuunnn)\n\n### Special Thanks\n\n* Twitter: <a href=\"https://twitter.com/kjkwak12\">kjkwak12</a>\n* Github: <a href=\"https://github.com/gaasedelen\">gaasedelen</a> - <a href=\"https://github.com/hyuunnn/Hyara/blob/master/hyara_lib/integration/bn_hyara/binaryninja_api.py#L9\">Link</a>\n* Github: <a href=\"https://github.com/ITAYC0HEN\">ITAYC0HEN</a> - <a href=\"https://github.com/hyuunnn/Hyara/pull/14\">Link</a>\n* Github: <a href=\"https://github.com/psifertex\">psifertex</a> - <a href=\"https://github.com/hyuunnn/Hyara/pull/18\">Link</a>\n",
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        "longdescription": "## Analog Devices Blackfin Architecture Plugin\nA Binary Ninja architecture plugin for the AD Blackfin architecture. It includes a standalone disassembler, and fairly robust lifting capabilities.\n\n## Disassembler\nThe disassembler used in this plugin (relagated to the disassembler/ subdirectory) can be used as a standalone decomposer/disassembler, and is not tied to the binaryninja API. It outputs tokenized structures representing each parsed machine code instruction, which can be parsed as desired by the disassembler frontend.\n| ![Disassembly](images/disasm.png) |\n|:--:|\n| *Yes, this is disassembly, not LLIL -- Blackfin uses arithmetic syntax for assembly* |\n### Instruction Support\nDisassembly of all instructions defined in the Blackfin Processor Programming Reference (Rev 2.2) is implemented. Additionally, a set of `pseudo_` instructions are implemented based on the libopcodes blackfin instruction parser, though these instructions do not appear in the above reference manual.\n\nThis disassembler supports only the Blackfin ISA, and _not_ the Blackfin+ superset used on BF7xx processor models. In addition to several new instructions, the Blackfin+ ISA also features a number of additional registers, and support for and usage of 64 bit instructions. At a minimum, support for detecting 64 bit instructions is planned, which would at least allow for disassembly of Blackfin compatible instructions in a Blackfin+ image.\n\n## Lifter\n| ![HLIL](images/hlil.png) |\n|:--:|\n| *HLIL output example* |\n\nThe lifter is mostly complete, with all standard operations supported, but lacking in support of some of the more complex/unusual DSP instructions and operating modes, including operations which saturate rather than overflow, and things like add with reverse bit carry that are complicated to implement correctly in BNIL. Additionally, some vector operations have yet to be implemented.\n\n## Architecture Misc.\n- Currently one standard calling convention (used by the bfin-* compilers, on uCLinux) and one system calling convention (same compilers) are defined.\n- Extremely rudimentary ELF relocation handling is implemented via BlackfinElfRelocationHandler, and this handler is registered to the BinaryNinja Elf view with machine_id == 106.\n- Equally rudimentary bFLT relocation handling is implemented via BlackfinBFLTRelocationHandler. Most of the heavy lifting for this handler is implemented in the bFLT view plugin.\n\n## Known Limitations\n- No lifting support for some DSP operatations\n    - Proper handling of flags: In most cases, conditionals are evaluated based on a manually set CC flag. However in some cases, the dedicated flags (AZ, AC, etc.) are used via the CCFlag instruction -- this instruction is not currently lifted, and flags are not set by operations presently.\n    - DSP multiplication operations, with/without fractionality, with/without saturation\n    - DSP vector shift operations\n    - PACK operation\n    - ABS operation\n    - DSP 'to reg from accumulator' move operations (saturation, fractionality)\n    - Accumulator/accumulator arithmetic\n    - DIVQ and DIVS division primitives\n    - ROT instruction edge cases; common cases handled by lifter\n    - Vector addition/subtraction edge cases; common cases handled by lifter\n    - EXTRACT instruction\n    - SIGNBITS instruction\n    - ALIGN8, ALIGN16, ALIGN32 instruction\n    - DSP LSHIFT operation\n- Out-of-spec handling of 16 bit immediate loads\n    - A common pattern observed in a number of sample binaries was the usage of a `reg.h = imm16; reg.l = imm16` idiom for loading a 32 bit immediate into a full width register. In most cases, the high load immediately precedes the low load, and as such the lifter combines the two operations into a single 32 bit load, which improves the resulting IL. However, in some cases the two operations are split up, and the resulting IL becomes messy, and breaks Binary Ninja's Value Set Analysis (VSA). In order to combat this, the lifter currently interprets a lone load high instruction as a 32 bit load to the full width register, assuming that the low bytes will be overwritten by a load low at some point before the register is read. This has been true in all cases observed during the development of this plugin, but _does not follow the Blackfin spec_ and technically produces incorrect IL. So far, the risk of occasional incorrect IL is outweighed by the greatly improved IL in the majority of cases, but be warned.\n- No support for parallel execution\n    - Blackfin ISA supports the parallel execution of up to three instructions at once. This is not implemented in the disassembler or lifter at present, and input machine code is treated as entirely sequential. As a consequence, under very specific circumstances the resulting disassembly may be misinterpreted -- for example, when one instruction in the parallel issued set sets a register, and the next in the set uses that same register as a source. In reality, because the instructions are being executed in parallel, the value of the register used in the latter operation will not have been updated at execution time, but the disassembly output will indicate that this _is_ the case.",
        "license": {
            "name": "GPL-3.0",
            "text": "Copyright 2022 sen\n\nThis program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.\n\nThis program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>."
        },
        "platforms": [
            "Linux"
        ],
        "installinstructions": {
            "Linux": "##Installing\nCopy the libarch_blackfin.so binary to the binaryninja plugin directory.\n## Building From Source (Adapted from Vector35/arch-armv7)\nBuilding the architecture plugin requires `cmake` 3.9 or above. You will also need the\n[Binary Ninja API source](https://github.com/Vector35/binaryninja-api).\n\nFirst, set the `BN_API_PATH` environment variable to the path containing the\nBinary Ninja API source tree.\n\nRun `cmake`. This can be done either from a separate build directory or from the source\ndirectory. If your app is installed in a non-default location, set BN_INSTALL_DIR in your\ncmake invocation, like `cmake -DBN_INSTALL_DIR=/Applications/BinaryNinjaDEV.app/'.`\nOnce that is complete, run `make` in the build directory to compile the plugin.\n\nThe plugin can be found in the root of the build directory as `libarch_blackfin.so`,\n`libarch_blackfin.dylib` or `arch_blackfin.dll` depending on your platform.\n\nYou can copy the plugin into the user plugins directory (you can locate this by using the \n'Open Plugin Folder' option in the Binary Ninja UI).\n\n## Build Example\n\n### acquire repositories\n```\nmkdir ~/repos/vector35\ncd ~/repos/vector35\ngit clone git@github.com:Vector35/binaryninja-api.git\ngit clone git@github.com:git@github.com:Ethan-ks/arch-blackfin.git\n```\n### environment variables\n\n`export BN_API_PATH=~/repos/vector35/binaryninja-api`\n\n### cmake, make\n```\ncd arch-blackfin\nmkdir build\ncd build\ncmake ..\nmake\n```\n## Build Troubleshooting\n\n### example\n\n    CMake Error at CMakeLists.txt:8 (message):\n      Provide path to Binary Ninja API source in BN_API_PATH\n    resolution:\n    ensure BN_API_PATH is in your environment\n\n### example\n\n    CMake Error at /Users/andrewl/repos/vector35/binaryninja-api/CMakeLists.txt:53 (message):\n      Binary Ninja Core Not Found\n    resolution:\n    ensure BN_INSTALL_DIR is supplied at command line invocation of cmake\n    ensure some bad directory is not cached in CMakeCache.txt\n\n### example\n\n    cmake seems to ignore your setting of BN_INSTALL_DIR and other cmake variables\n    resolution:\n    rm CMakeCache.txt\n\n### example\n\n    undefined symbols at link time, like:\n    Undefined symbols for architecture x86_64:\n      '_BNClearUserVariableValue', referenced from:\n      BinaryNinja::Function::ClearUserVariableValue(BinaryNinja::Variable const&, unsigned long long) in libbinaryninjaapi.a(function.cpp.o)\n    resolution:\n    ensure that your api repo is on the same channel and at the same commit as the libbinaryninjacore you're linking against\n    eg: binaryninja is on dev update channel and is up-to-date and binaryninja-api repo is on branch dev with latest pulled"
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        "longdescription": "# dewolf\n\ndewolf is a research decompiler we developed during a research cooperation from 2019 to 2021 between Germany (Fraunhofer FKIE) and Singapore (DSO National Laboratories).\n\nThe restructuring of dewolf is based on the former DREAM/DREAM++ approach [Yakdan et al. NDSS 2015, IEEE (SP) 2016].\n\nThe decompiler dewolf is implemented as a plugin for Binary Ninja and uses their Medium-Level intermediate language as the starting point.\nAlthough we consider dewolf to be pretty stable, it is still a research prototype and not extensively optimized for production use.\nConsequently, you will likely observe a few bugs or even decompilation failures when applying dewolf on real-world binaries.\n\n**If you encounter any bugs, please report them to us so that we can further improve dewolf. :)**\n\n## Usage\n\nAfter enabling the dewolf decompilation dock widget via **View > Other Docks > Show Dewolf**, the decompiled code for the currently active symbol will be displayed.\nIn the dewolf dock, it is possible to navigate through functions by double-clicking them.\n\n![Widget Menu](https://user-images.githubusercontent.com/12004321/145460440-be4b7dfd-bf7e-497f-a7af-1911bf3efc50.png)\n\nThe automatic decompilation of selected functions can be toggled with the *follow* button.\nDecompiled code is cached and can be generated again with the *decompile* button, e.g. after patching instructions in the binary view.\n\n![Widget](https://user-images.githubusercontent.com/12004321/145460476-f869e5cc-d585-4f53-8920-6ecfa4b346d5.png)\n\n## Configuration\ndewolf has multiple configuration options of which some are configurable via the GUI.\n\nYou can configure dewolf from the Binary Ninja GUI by navigating to **Edit > Preferences > Settings** or by pressing <kbd>Ctrl</kbd> + <kbd>,</kbd>.\nSearch for **dewolf** in the search bar and all dewolf related settings will be displayed.\n\n## Support\n\nIf you have any suggestions, or bug reports, please create an issue in the [Issue Tracker](https://github.com/fkie-cad/dewolf/issues).\n\nIn case you have any questions or other problems, feel free to send an email to:\n\n[dewolf@fkie.fraunhofer.de](mailto:dewolf@fkie.fraunhofer.de).",
        "license": {
            "name": "LGPL-2.1",
            "text": "This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version.\n\nThis library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.\n\nYou should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA"
        },
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            "Linux",
            "Darwin"
        ],
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            "Linux": "## Install Dependencies\nPlease make sure you have the following dependencies installed and available on your computer:\n\n- At least [Python 3.10](https://www.python.org/)\n- Latest stable release of [Binary Ninja (>=3.0)](https://binary.ninja/)\n- [astyle](https://code.tools/man/1/astyle/) for proper indentation of the decompiled code\n\nUnder **Linux** (Ubuntu / Debian), you can use the following command to install **astyle**:\n\n```bash\nsudo apt install astyle\n```",
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            "architecture"
        ],
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            "python3"
        ],
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        "type": [
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            "python3"
        ],
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        "license": {
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            "text": "Copyright (c) 2019 Yu-Jye Tung\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
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        "longdescription": "# <p align='center'> Opaque Predicates Detective </p>\n\n![OP Detective](op_detective.png)\n\n## Introduction: \nPrevious approaches to generically identify opaque predicates work by identifying if a conditional branch contains an invariant expression. Our approach generically identify opaque predicates from a different perspective: __the damage caused by the obfuscation__. The damage is localized at the basic block level (or at the function level) regardless of how an opaque predicate's invariant expression is constructed. This approach allows us to also detect an opaque predicate whose invariant expression is constructed across multiple processes! \n\nIDA also detects opaque predicates from the damage but its approach cannot identify the exact superfluous branch, [which allows for the creation of stealthier opaque predicates](https://yellowbyte.github.io/blog/2018/the_return_of_disassembly_desynchronization/).\n\nThere are two main types of damage resulting from opaque predicates: code bloat or disassembly desynchronization. __Current implementation focuses on detecting opaque predicates when the damage is disassembly desynchronization__. Disassembly desynchronization is a umbrella term for obfuscation techniques that disrupt static disassembly by the creative placement of junk bytes (random data bytes) into the instruction stream such that a disassembler will parse those junk bytes as code instructions. In the case of an opaque predicate, junk bytes are inserted into the target basic block of the opaque predicate's superfluous branch. To identify opaque predicates' superfluous branches, we analyze each conditional branch's outgoing basic blocks for __illogical behaviors__ (which can manifest from code instructions that are actually junk bytes). Note that identifying the superfluous branch will allow us to trace back to the offending opaque predicate.\n\nFuture work will look into detecting opaque predicates when the damage is code bloat.\n\n## Identifying Illogical Behaviors\nWe have a set of rules that checks if a basic block contains the damage performed by opaque predicates. Essentially, the damage translates to illogical code behaviors since current approach is limited to detecting opaque predicates when the damage is [disassembly desynchronization](https://github.com/yellowbyte/reverse-engineering-reference-manual/blob/master/contents/anti-analysis/Anti-Disassembly.md#-disassembly-desynchronization-). \n\nThe names we used for the rules in the [paper](https://archive.bar/pdfs/bar2020-preprint4.pdf) compared to the code are not the same. This means that annoyance might arise if one tries to match a rule described in paper to its actual implementation. \n\nBelow is a Python Dictionary depicting the relationship between the names in code (dictionary key) vs in paper (dictionary value): \n\n```python\nRULES_RELATIONSHIP = {\n    # 'names in code'                   'names in paper'\n    'weird_cutoff':                     'abrupt_basic_block_end',\n    'prob_of_unimpl':                   'unimplemented_BNILs_percentage',\n    'priviledged_instructions':         'privileged_instruction_usage',\n    'def_no_use_dep':                   'defined_but_unused',\n    'memaccess_self':                   'memory_pointer_constraints',\n    'conditional_unused':               'defined_but_unused',\n    'stack_pointer_oddity':             'memory_pointer_constraints',\n    'crazy_mem_offset':                 'unreasonable_memory_offset',\n    'type_discrepency_ptr_in_mult_div': 'memory_pointer_constraints',\n    'memaccess_nonexist':               'nonexistence_memory_address',\n    'memaccess_src_dest_discrepancy':   'memory_pointer_constraints',\n    'call_dest_nonexist':               'nonexistence_memory_address',\n    'jmp_dest_nonexist':                'nonexistence_memory_address',\n}\n```\n\nAs seen in the dictionary, it is not a one-to-one relationship. The rule names in code is more fine-grained than the rule names in paper. For example, the 'nonexistence\\_memory\\_address' rule (in paper) corresponds to 'memaccess\\_nonexist', 'call\\_dest\\_nonexist', and 'jmp\\_dest\\_nonexist' rules (in code). \n\n## Detective in Action:\nCurrent implementation is a [BinaryNinja](https://binary.ninja) plugin.\n\nHow to run plugin: \n![Plugin Run](whole.png)\n\nOutput:\n![Plugin Output](current_output.png)\n\nNote that the printed virtual addresses (highlighed in green) are the target addresses of the superfluous branches. (The addresses can easily be changed to the addresses of the opaque conditional statements if desired.)\n\n## Reference \\[[link to paper](https://www.ndss-symposium.org/wp-content/uploads/2020/04/bar2020-23004-paper.pdf)\\]\n\n    @inproceedings{tungheuristic,\n      title={A Heuristic Approach to Detect Opaque Predicates that Disrupt Static Disassembly},\n      author={Tung, Yu-Jye and Harris, Ian G}\n      booktitle={Proceedings of the NDSS Workshop on Binary Analysis Research},\n      year={2020}\n    }\n\n__NOTE__: Google Scholar has the incorrect BibTex but unfortunately I cannot update it directly on Google Scholar\n\n",
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        "author": "Tim Blazytko",
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        "longdescription": "# Obfuscation Detection (v2.3)\nAuthor: **Tim Blazytko**\n\n_Automatically detect obfuscated code and other interesting code constructs_\n\n## Description:\n\n_Obfuscation Detection_ is a Binary Ninja plugin to detect obfuscated code and interesting code constructs (e.g., state machines) in binaries. Given a binary, the plugin eases analysis by identifying code locations which might be worth a closer look during reverse engineering.\n\nBased on various heuristics, the plugin pinpoints functions that contain complex or uncommon code constructs. Such code constructs may implement\n\n* obfuscated code\n* state machines and protocols\n* C&C server communication\n* string decryption routines\n* cryptographic algorithms\n\nThe following blog posts provide more information about the underlying heuristics and demonstrate their use cases:\n\n* [Automated Detection of Control-flow Flattening](https://synthesis.to/2021/03/03/flattening_detection.html)\n* [Automated Detection of Obfuscated Code](https://synthesis.to/2021/08/10/obfuscation_detection.html)\n* [Statistical Analysis to Detect Uncommon Code](https://synthesis.to//2023/01/26/uncommon_instruction_sequences.html)\n* [Identification of API Functions in Binaries](https://synthesis.to/2023/08/02/api_functions.html)\n\nSome example use cases can be found in [examples](./examples). Furthermore, the REcon talk [\"Unveiling Secrets in Binaries using Code Detection Strategies\"](https://cfp.recon.cx/2023/talk/QD8UNJ/) demonstrates some use cases. The slides can be found [here](./presentation/recon23_code_detection.pdf); the recording can be found [here](https://www.youtube.com/watch?v=y95MNr2Xu-g).\n\n\n## Core Features\n\n* identifies interesting code constructs in large binaries\n* highlights disaligned instructions in Binary Ninja's graph view\n* efficient and architecture-agnostic implementation\n* runs as a background task\n* can be used in UI and headless mode\n\n\n## Installation\n\nThe tool can be installed using Binary Ninja's plugin manager.\n\nFor the headless version, follow these steps:\n\n```\ngit clone https://github.com/mrphrazer/obfuscation_detection.git\ncd obfuscation_detection\n\n# install obfuscation_detection\npip install .\n```\n\n\n## Usage\n\nThe plugin can be used in the user interface and in headless mode.\n\n\n### User Interface\n\nChoose the index tab `Plugins -> Obfuscation Detection` to run one or more detection heuristics in Binary Ninja's user interface:\n\n<p align=\"left\">\n<img alt=\"Plugin Menu\" src=\"imgs/plugin_menu.png\" width=\"500\"/>\n</p>\n\nThe results are displayed in the Log window:\n\n<p align=\"center\">\n<img alt=\"Binary Ninja Log\" src=\"imgs/plugin_results.png\"/>\n</p>\n\nBy clicking on the identified function addresses, Binary Ninja navigates to the selected function.\n\n\n### Headless\n\nTo use the plugin in headless mode, run [`scripts/detect_obfuscation.py`](scripts/detect_obfuscation.py):\n\n```\n$ python3 scripts/detect_obfuscation.py <binary>\n```\n\n\n## Detection Heuristics\n\nThe plugin implements various detection heuristics to detect different code constructs. In the following, we briefly describe the individual heuristics and explain their usage. \n\n### Large Basic Blocks\n\nThe large basic block heuristic identifies the top 10% of functions with the largest average number of instructions per basic block. It allows to detect\n\n* unrolled code\n* cryptographic implementations\n* initialization routines\n* arithmetic obfuscation / Mixed Boolean-Arithmetic\n\n### Complex Functions\n\nTo complex functions heuristic identifies the top 10% of functions with the most complex control-flow graphs (based on cyclomatic complexity). It allows to identify\n\n* complex dispatching routines and protocols\n* state machines\n* functions obfuscated with opaque predicates\n\n\n### Flattened Functions\n\nThe flattened function heuristic uses some graph-theoretic properties to identify functions implementing state machines. Usually, such state machines can be represented as switch statements that are dispatched in a loop. The heuristic allows to identify\n\n* network protocol dispatching\n* file parsing logic\n* C&C server communication / command dispatching\n* control-flow flattening\n\n\n### Uncommon Instruction Sequences\n\nThe uncommon instruction sequences heuristic performs a statistical analysis to identify the top 10% of functions whose code patterns deviate from a pre-computed ground truth. This way, the heuristic allows to identify\n\n* cryptographic implementations\n* intense usage of floating point arithmetic\n* arithmetic obfuscation / Mixed Boolean-Arithmetic\n* generic obfuscation patterns\n\n\n### Instruction Overlapping\n\nThe instruction overlapping heuristic identifies functions with disaligned instructions (instruction bytes are shared by two different instructions). The heuristic identifies\n\n* broken disassembly (e.g., data which is marked as code)\n* opaque predicates which jump into other instructions \n\nIf the heuristic is used in Binary Ninja's user interface, overlapping instructions are also highlighted in the graph view.\n\n\n### Most Called Functions\n\nThe heuristic for most called functions identifies the top 10% of functions with the largest number of calls from different functions. This way, the heuristic can identify\n\n* string decryption routines\n* library functions in statically linked binaries\n\n\n### High Loop Frequency\n\nThe heuristic identifies functions with a high number of loops. These kind of functions might implement\n\n* a complex parsing logic for structured data formats\n* intensive algorithmic computations\n\nThe heuristic also helps pinpointing potential performance bottlenecks.\n\n\n### Irreducible Loops\n\nThe heuristic identifies functions with rare and complex loop structures that typically suggest\n\n* aggressive compiler optimizations\n* hand-written assembly\n* usage of goto statements\n* obfuscated code\n\n\n### XOR Decryption Loops\n\nThe heuristic identifies functions which perform an XOR operation with a constant inside of a loop. This way, the heuristic can identify\n\n* string decryption routines\n* code decryption stubs\n* cryptographic implementations\n\n\n### Complex Arithmetic Expressions \n\nThe heuristic identifies functions in which the expressions have more than one arithmetic operation and one boolean operation simultaneously. This way, the heuristic can identify\n\n* mixed-boolean arithmetic obfuscation\n* initialization routine\n* cryptographic implementations\n\n\n### Duplicate Subgraphs\n\nThe heuristic uses an iterative context-hashing approach to detect repeated multi-block structures within each function\u2019s control-flow graph. By comparing each block\u2019s opcode signature along with the signatures of its successors, the heuristic identifies subgraphs that are duplicated or near-duplicated in a single function. This helps pinpoint:\n\n* obfuscation stubs like small state machines or dispatcher blocks cloned multiple times\n* unrolled or inlined loops introduced by compiler optimizations\n* multi-way branching logic like range dividers and decision trees\n* repeated cryptographic or checksumming stubs\n\n\n## Utils\n\nContrary to the detection heuristics which target a wider scope of code, the plugin also implements various helpers which aim to identify functions with a narrower scope. In the following, we describe these helpers and explain their characteristics.\n\n\n### Entry Functions\n\nThis helper identifies functions without known callers. These functions might be \n\n* entry points in the binary\n* indirect jumps targets where the call hierarchy could not be recovered by the disassembler\n\n\n### Leaf Functions\n\nThis helper identifies functions that do not call other functions. These kinds of functions may, for example, be functions that\n\n* are outlined by the compiler to implement functionalities utilized across various code locations\n* are trampolines to other functions\n* are part of code obfuscation schemes (e.g., outlined computations for control-flow obfuscation)\n\n\n### Recursive Functions\n\nThis helper identifies recursive functions---functions that directly or indirectly call themselves. Recursive functions may indicate:\n\n* algorithmic implementations such as tree or graph traversal\n* obfuscated code designed to complicate static analysis\n* implementations of mathematical algorithms or complex parsing routines\n* potential stack overflow vulnerabilities due to deep recursion\n\n\n### Section Entropy\n\nThis helper evaluates the entropy of each section. Entropy is a statistical measure of randomness with values ranging between 0 and 8. Sections with an entropy close to 8 indicate a high degree of randomness and can hint at:\n\n* packed or compressed code\n* encrypted data or code\n* random data\n\n\n### RC4\n\nThis helper detects potential RC4 algorithm implementations by employing heuristic markers typically associated with RC4's Key Scheduling Algorithm (KSA) and Pseudo-Random Generation Algorithm (PRGA). RC4 is widely used in malware for purposes such as:\n\n* decrypting strings and other data payloads\n* obfuscating command and control (C&C) communications\n\n\n## Contact\n\nFor more information, contact [@mr_phrazer](https://twitter.com/mr_phrazer).\n\n",
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            "Linux",
            "Darwin"
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        "longdescription": "# BNIL Instruction Graph\r\nA BinaryNinja plugin to graph a BNIL instruction tree and meta-program python instruction matchers.\r\n\r\n## Installation\r\n\r\nInstallation is supported two ways, the first using the new plugin manager and the second being a manual install.\r\n\r\n### Plugin Manager\r\nUse the new plugin manager by selecting \"Manage Plugins\" from the \"Edit\" menu. Search the plugin list for \"BNIL Instruction Graph\", right click on it and click \"Install\" then right click again and select \"Enable\".\r\n\r\n## Manual Installation\r\n1. Clone the repository to your prefered location: `$ git clone https://github.com/withzombies/bnil-graph.git`\r\n1. Change to the Binary Ninja plugins directory: `$ cd ~/Library/Application\\ Support/Binary\\ Ninja/plugins`\r\n1. Create a symlink to the folder: `$ ln -s ~/git/bnil-graph .`\r\n1. Restart Binary Ninja\r\n\r\n## Usage\r\n\r\nTo use bnil-graph, right click on an instruction and select \"BNIL Instruction Graph\". This graphs the BNIL instructions assocaited with that address and displays them as an HTML form.\r\n\r\nBinary Ninja adds operand accessors dynamically, due to this the convenient accesors do not show up in `dir()` calls or in the api documentation. bnil-graph shows the structure of the IL instruction including its nice accessor names (such as `insn.src` for the source register or memory)\r\n\r\n![Menu Example](https://raw.githubusercontent.com/withzombies/bnil-graph/master/images/menu.png)\r\n\r\nExample graph:\r\n\r\n![Example Graph](https://raw.githubusercontent.com/withzombies/bnil-graph/master/images/graph.png)\r\n\r\n### Matchers\r\n\r\nIn addition to the graph plugin, bnil-graph also will generate a matcher function that will match the selected instructions exactly. This feature will allow new plugin developers to quickly match instructions. The intended use is to find an instruction similar to the one you want to match, generate a matcher function, then modify the generated function to better support your needs.\r\n\r\nAn example would be trying to find all MediumLevelILSSA MLIL\\_CALL\\_SSA instructions that take 3 parameters. I generated a matcher against an unrelated function with 0 parameters:\r\n\r\n```python\r\ndef match_MediumLevelILSSA_140001194_0(insn):\r\n    # mem#1 = 0x14000d49c() @ mem#0\r\n    if insn.operation != MediumLevelILOperation.MLIL_CALL_SSA:\r\n        return False\r\n\r\n    # invalid\r\n    if insn.output.operation != MediumLevelILOperation.MLIL_CALL_OUTPUT_SSA:\r\n        return False\r\n\r\n    if insn.output.dest_memory != 0x1:\r\n        return False\r\n\r\n    if len(insn.output.dest) != 0:\r\n        return False\r\n\r\n    # 0x14000d49c\r\n    if insn.dest.operation != MediumLevelILOperation.MLIL_CONST_PTR:\r\n        return False\r\n\r\n    if insn.dest.constant != 0x14000d49c:\r\n        return False\r\n\r\n    if len(insn.params) != 0:\r\n        return False\r\n\r\n    if insn.src_memory != 0x0:\r\n        return False\r\n\r\n    return True\r\n```\r\n\r\nWe can modify this to remove some specific constraints:\r\n\r\n```python\r\ndef match_MediumLevelILSSA_140001194_0(insn):\r\n    # mem#1 = 0x14000d49c() @ mem#0\r\n    if insn.operation != MediumLevelILOperation.MLIL_CALL_SSA:\r\n        return False\r\n\r\n    # invalid\r\n    if insn.output.operation != MediumLevelILOperation.MLIL_CALL_OUTPUT_SSA:\r\n        return False\r\n\r\n    # 0x14000d49c\r\n    if insn.dest.operation != MediumLevelILOperation.MLIL_CONST_PTR:\r\n        return False\r\n\r\n    if len(insn.params) != 0:\r\n        return False\r\n\r\n    return True\r\n```\r\n\r\nWe removed the call destination and the memory versioning constraints. Next, update the params check to check for 3 parameters:\r\n\r\n```python\r\ndef match_3_param_MLIL_CALL_SSA(insn):\r\n    if insn.operation != MediumLevelILOperation.MLIL_CALL_SSA:\r\n        return False\r\n\r\n    if insn.output.operation != MediumLevelILOperation.MLIL_CALL_OUTPUT_SSA:\r\n        return False\r\n\r\n    if insn.dest.operation != MediumLevelILOperation.MLIL_CONST_PTR:\r\n        return False\r\n\r\n    if len(insn.params) != 3:\r\n        return False\r\n\r\n    return True\r\n```\r\n\r\nNow, we have a matcher which will identify MLIL\\_CALL\\_SSA instructions with 3 parameters! Now iterate over MLIL SSA instructions and call the matcher and we're done:\r\n\r\n```python\r\nif __name__ == '__main__':\r\n    bv = binaryninja.BinaryViewType.get_view_of_file(sys.argv[1])\r\n    bv.update_analysis_and_wait()\r\n\r\n    for func in bv.functions:\r\n        mlil = func.medium_level_il\r\n\r\n        for block in mlil.ssa_form:\r\n            for insn in block:\r\n                if match_3_param_MLIL_CALL_SSA(insn):\r\n                    print \"Match: {}\".format(insn)\r\n```\r\n\r\nExample matcher:\r\n\r\n![Example Matcher](https://raw.githubusercontent.com/withzombies/bnil-graph/master/images/matcher.png)\r\n\r\n\r\n## License\r\n\r\nThis project copyright Ryan Stortz (@withzombies) and is available under the Apache 2.0 LICENSE.\r\n",
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    {
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        "author": "Mark Griffin",
        "type": [
            "helper"
        ],
        "api": [
            "python3"
        ],
        "description": "Scriptable code coverage analysis and visualization plugin",
        "longdescription": "# bncov - Scriptable Binary Ninja plugin for coverage analysis and visualization\n\nbncov provides a scriptable interface for bringing together coverage\ninformation with Binary Ninja's static analysis and visualization. Beyond\nvisualization, the abstractions in bncov allow for programmatic reasoning\nabout coverage. It was designed for interactive GUI use as well as for\nfactoring into larger analysis tasks and standalone scripts.\n\n![Demo Overview](https://github.com/ForAllSecure/bncov/raw/master/pictures/bncov_demo.png)\n\nThis plugin is provided as a way to give back to the community,\nand is not part of the Mayhem product.  If you're interested in Mayhem, the\ncombined symbolic execution and fuzzing system, check us out at\n[forallsecure.com](http://forallsecure.com).\n\n## CHANGELOG\nFeb 2023: drcov format version 3 now supported.\nOct 2021: Some changes in the API:\n\n- Added a `ctx` object that is keyed off the Binary View and helpers\n  `bncov.get_ctx` and `bncov.get_covdb` to support multibinary use case in the\n  UI, so now you can interactively use bncov across tabs!\n- Changed old helpers `bncov.get_bv`/`bncov.get_covdb` to\n  `bncov.make_bv`/`bncov.make_covdb` for making a Binary View from a target file\n  and covdb from a binary and a coverage directory, respectively\n- All function-related covdb member functions now default to keying off of\n  function start addresses rather than names since function starts are unique\n  and more usable for many applications. Extra optional args or helper functions\n  implement the existing behavior.\n- Abandoning Python2 suport\n- Added `download_dynamorio.py` for the sloths\n- Minor quality-of-life fixes\n\n## Installation\n\nThe easiest way is to install via the Binary Ninja plugin manager!\nThe only difference when installing via plugin manager is that wherever\nyou see `import bncov`, you may have to do `import ForAllSecure_bncov as bncov`.\n\nAlternatively:\n\n - Clone or copy this directory into your binja plugins folder.\n([More detailed instructions here](https://docs.binary.ninja/guide/plugins/index.html#using-plugins))\n - (Optional) pip install msgpack if you want to enable loading/saving\ncoverage database files.\n\n## Usage\n\nCheck out the [tutorial](/tutorial/) for a complete walkthrough or how to get\nstarted right away using data that's already included in this repo!\n\nFirst collect coverage information in DynamoRIO's drcov format\n([example script](/dr_block_coverage.py)).\n\nTo use in Binary Ninja GUI:\n\n1. Open the target binary, then import coverage files using one of\nthe commands in `bncov/Coverage Data/Import *`\neither from the Tools menu or from the context (right-click) menu.\n2. Explore the coverage visualization and explore additional analyses from\nthe right-click menu or with the built-in interpreter and `import bncov`\nfollowed by `covdb = bncov.get_covdb(bv)`.\n\nScripting:\n\n1. Ensure bncov's parent directory is in your module search path\nOR add it to sys.path at the top of your script like this:\n`sys.path.append(os.path.split(os.path.normpath('/path/to/bncov'))[0])`\n2. `import bncov` and write scripts with the CoverageDB class in\n`coverage.py`, check out the `scripts` folder for examples.\n\n## Screenshots\n\nImport a coverage directory containing trace files to see blocks colored in\nheat map fashion: blocks covered by most traces (blue) or by few traces\n(red). Additional context commands (right-click menu) include frontier\nhighlighting and a per-function block coverage report.\n\n* Watch a directory to have new coverage results get automatically highlighted\nwhen new coverage files appear\n* See at a glance which blocks are only covered by one or a few traces\n(redder=rarer, bluer=more common)\n* Quickly discover rare functionality visually or with scripting\n* Identify which blocks have outgoing edges not covered in the traces\n* See coverage reports on functions of interest or what functionality may not\nbe hit, or write your own analyses for headless scripting.\n\n![Block Coverage Report](https://github.com/ForAllSecure/bncov/raw/master/pictures/Coverage-Report.png)\n\n## Notes\n\nCurrently the plugin only deals with block coverage and ingests files in the\ndrcov format or module+offset format. Included in the repo is\n`dr_block_coverage.py` which can be used for generating coverage files, just\nspecify your DynamoRIO install location with an environment variable (or\nmodify the script) and it can process a directory of inputs. DynamoRIO binary\npackages can be found\n[here](https://github.com/DynamoRIO/dynamorio/wiki/Downloads) or you can use the\nincluded `download_dynamorio.py` script. See the\n[tutorial](/tutorial/) for a complete walkthrough.\n\nPlease file any feature requests/bugs as issues on GitHub, we welcome any input\nor feedback.\n\n## Scripting\n\nbncov was designed so users can interact directly with the data structures\nthe plugin uses. See the `scripts/` directory for more ideas.\n\n* Helpful CoverageDB members:\n    * trace_dict (maps filenames to set of basic block start addresses)\n    * block_dict (maps basic block start addresses to files containing it)\n    * total_coverage (set of start addresses of the basic blocks covered)\n\n* Helpful CoverageDB functions:\n    * get_traces_from_block(addr) - get files that cover the basic block\n      starting at addr.\n    * get_rare_blocks(threshold) - get blocks covered by <= 'threshold' traces\n    * get_frontier() - get blocks that have outgoing edges that aren't covered\n    * get_functions_from_blocks(blocks, by_name=False) - return dict mapping\n      function starts/names to blocks they contain\n    * get_traces_from_function(function_start) - return set of traces that have\n      coverage in the specified function\n    * get_traces_from_function_name(function_name, demangle=False) - return set\n      of traces that have coverage in the specified function\n\n* You can use Binary Ninja's python console and built-in python set operations with\nbncov.highlight_set() to do custom highlights in the Binary Ninja UI.\n",
        "license": {
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        "longdescription": "BinaryNinja CryptoScan\n======================\nPlugin for [Binary Ninja](https://binary.ninja/) platform\n\n## General\nThis Binja plugin is effectively trying to replicate [findcrypt](https://github.com/you0708/ida/tree/master/idapython_tools/findcrypt) functionality. It supports a larger range of constants.\n\n## Features\n### Overview\nThe plugin registers a single call that is accessible from the 'Tools' menu or by right-clicking in the main binary view window. \n\nScan configurations are reloaded on _every_ call to the scan function. This means you can CRUD json config files and rescan immediately. \n\nThe plugin supports the following types of scans:\n\n - Scanning for constants in data \n - Scanning for constants in the IL \n\n Will additionally add a tag at each identified location with the family name as the data.\n\n### Supported cryptography\n\nThe following constants are defined in scan configurations:\n\n - AES: sboxes, td0-4, te0-4\n - ARIA: sbox2, sbox4 (1 and 3 are equal to Rijndael)\n - BLAKE: 224, 256, 384 and 512 inits \n - Blowfish: p_array and sbox\n - CRC32: lzma tables 0-7, m_tab_le and m_tab_be, ms_table0-6\n - DES: p32i, pc1_left, pc1_right, pc2, sbox1-8\n - DFC: sbox\n - Elliptic Curves: p-192, p-224, p-256, p-384, p-521, Curve25519\n - IKE: modp group shared component\n - KASUMI: key expansion mod, sbox_s7, sbox_s9\n - MD5: initstate and md5_t\n - NewDES: sbox\n - RC5/RC6: combined constant\n - Salsa20: sigma and tau\n - SHA1: h\n - SHA224: h\n - SHA256: both k and h \n - SHA512: h\n - SM3: init\n - SM4: sbox, ck and fk\n - TEA: delta\n - Mersenne Twister: 19937 edition\n - Zlib: distance_starts, distance_extrabits, length_starts, length_extrabits\n\n### Reporting\nIf any matches are identified a Markdown (for GUI) or text (for CLI) report will be shown, listing which scans were matched, what family they belong to as well as the address in the binary.\nThe aim of the report was to allow easy copy-pasting of the address for use with the 'Go to address...' function. \n\nWhilst data matches only contain the address at which the constant is defined, IL matches will also indicate the function they were discovered in.\n\n### Configuration\nIndividual scan configurations are kept in the scans subfolder. The following fields are required in the JSON:\n\n - name: short name for the scan\n - description: long name or description, only for author's quality of life \n - threshold: for multi-byte constants, minimum amount of n-sized chunks that must be found (e.g. when loading chunked in registers) \n - type: static or signature (signatures are currently not implemented though)\n - flags: array of single byte strings, usually 4 bytes but no upper bound is set. You can now add null bytes. \n - on_match: object with subfields\n   - type: symbol is the only supported value right now, adds a symbol at the detected address\n   - name: name to give to the symbol\n\n## TODO\n- [x] Add more constants \n- [x] Make CryptoScan run in the background\n- [x] Deal with null bytes in the flags better\n- [ ] Flesh out how signature detection will work\n- [ ] Refactor data scanning to improve speed on very large binaries \n",
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        "license": {
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            "Linux": "",
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            "Linux"
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            "Windows": "Copy to `%APPDATA%\\Binary Ninja\\plugins` or use Plugin Manager",
            "Linux": "Copy to `~/.binaryninja/plugins/` or use Plugin Manager"
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