GhidrAssist
经 AI Skill Hub 精选评估，GhidrAssist 获评「强烈推荐」。这款AI工具在功能完整性、社区活跃度和易用性方面表现出色，AI 评分 8.0 分，适合有一定技术背景的用户使用。
**为什么要使用开源工具而非商业 SaaS？**
对于个人开发者和有隐私需求的用户，本地部署的开源工具意味着数据不离本机，不受第三方服务商的数据政策约束。同时，开源工具通常没有使用次数限制和月度费用，一次安装即可长期使用，对于高频使用场景的总拥有成本（TCO）远低于订阅制商业工具。
**安装与环境准备**
GhidrAssist 依赖 Java 运行环境。建议通过 pyenv（Python）或 nvm（Node.js）管理 Java 版本，避免全局环境污染。对于新手用户，推荐先创建虚拟环境（python -m venv venv && source venv/bin/activate），再安装依赖，这样即使出现问题也可以随时删除虚拟环境重新开始，不影响系统稳定性。
**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues（已关闭的问题），大多数常见问题都已有解答。遇到 Bug 时，提供 pip list 的输出、完整错误堆栈和最小可复现示例，能显著提高开发者响应速度。AI Skill Hub 将持续追踪 GhidrAssist 的版本更新，及时通知重要功能变化。
GhidrAssist 是一款基于 Java 开发的开源工具，专注于 Ghidra、LLM、Java 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
GhidrAssist 是一款基于 Java 开发的开源工具，专注于 Ghidra、LLM、Java 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
# 克隆仓库 git clone https://github.com/symgraph/GhidrAssist cd GhidrAssist # 查看安装说明 cat README.md # 按 README 完成环境依赖安装后即可使用
- 访问 GitHub 仓库页面
- 按照 README 文档完成依赖安装
- 根据系统环境完成初始化配置
- 参考官方示例或文档开始使用
- 遇到问题可在 GitHub Issues 中查找解答
# 查看帮助 ghidrassist --help # 基本运行 ghidrassist [options] <input> # 详细使用说明请查阅文档 # https://github.com/symgraph/GhidrAssist
# ghidrassist 配置说明 # 查看配置选项 ghidrassist --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量（覆盖配置文件） export GHIDRASSIST_CONFIG="/path/to/config.yml"
GhidrAssist
Author: Jason Tang
An advanced LLM-powered plugin for interactive reverse engineering assistance in Ghidra.
Description
GhidrAssist integrates Large Language Models (LLMs) into Ghidra to provide intelligent assistance for binary exploration and reverse engineering. It supports any OpenAI v1-compatible API, including local models (Ollama, LM-Studio, Open-WebUI) and cloud providers (OpenAI, Anthropic, Azure).
Key Features
Core Functionality: Code Explanation - Explain functions and instructions in both disassembly and decompiled pseudo-C - Security analysis panel showing risk level, activity profile, and API usage - Editable summaries with user-edit protection from auto-overwrite Interactive Chat - Multi-turn conversational queries with persistent chat history * Custom Queries - Direct LLM queries with optional context from current function/location
Graph-RAG Knowledge System: Semantic Knowledge Graph - Hierarchical representation of binary analysis - 5-level semantic hierarchy: Statement → Block → Function → Module → Binary - Pre-computed LLM summaries enable fast, LLM-free queries - SQLite persistence with JGraphT graph algorithms - Full-text search (FTS5) on summaries and security annotations Community Detection - Automatic module discovery via Leiden algorithm - Groups related functions into logical modules - Hierarchical community structure with summaries - Visual graph exploration with configurable depth Security Feature Extraction - Comprehensive security analysis - Network APIs: POSIX sockets, WinSock, DNS, SSL/TLS, WinHTTP, WinINet - File I/O APIs: POSIX, Windows, C library functions - Crypto APIs: OpenSSL, Windows crypto, platform-specific - String patterns: IP addresses, URLs, domains, file paths, registry keys - Risk level classification (LOW/MEDIUM/HIGH) and activity profiling Semantic Graph Tab - Visual knowledge graph interface - Graph view with N-hop depth exploration - List view of all indexed functions - Semantic search across summaries - One-click re-indexing and security analysis
Advanced Capabilities: Extended Thinking/Reasoning Control - Adjust LLM reasoning depth for quality vs. speed trade-offs - Support for OpenAI o1/o3/o4, Claude with extended thinking, and local reasoning models - Configurable effort levels: Low (fast), Medium (balanced), High (thorough) - Per-program persistence - different binaries can use different reasoning levels - Provider-agnostic implementation (Anthropic, OpenAI, Azure, LiteLLM, LMStudio, Ollama) ReAct Agentic Mode - Autonomous investigation using structured reasoning (Think-Act-Observe) - LLM proposes investigation steps based on your query - Systematic tool execution with progress tracking via todo lists - Iteration history preservation showing all investigation steps - Final synthesis with comprehensive answer and key findings - Accurate metrics (iterations, tool calls, duration) MCP Integration - Model Context Protocol client for tool-based analysis - Works with GhidrAssistMCP for Ghidra-specific tools - Conversational tool calling with automatic function execution - Support for SSE (Server-Sent Events) transport Function Calling - LLM can autonomously navigate binaries and modify analysis - Rename functions and variables - Navigate to addresses and cross-references - Execute Ghidra commands Actions Tab - Propose and apply bulk analysis improvements - Security vulnerability detection - Code quality analysis - Automated refactoring suggestions RAG (Retrieval Augmented Generation) - Enhance queries with contextual documents - Add custom documentation, exploit notes, architecture references - Lucene-based full-text search - Context injection into queries * RLHF Dataset Generation - Collect feedback for model fine-tuning
LLM Setup
GhidrAssist works with any OpenAI v1-compatible API. Setup details are provider-specific - here are some helpful resources:
Local LLM Providers: - LM Studio - Easy local model hosting with GUI - Ollama - Command-line local model management - Open-WebUI - Web interface for local models
Cloud Providers: - OpenAI API - Anthropic Claude - Azure OpenAI
LiteLLM Proxy (Multi-Provider Gateway): - LiteLLM - Unified API for 100+ LLM providers - Supports AWS Bedrock, Google Vertex AI, Azure, and many others - Select "LiteLLM" as provider type in GhidrAssist settings - Automatic model family detection for proper message formatting
Setup
- Start the MCP Server
2. Configure GhidrAssist: - Open Tools → GhidrAssist Settings → MCP Servers tab - Add server: http://127.0.0.1:8081 as GhidrAssistMCP with transport type SSE
3. Enable MCP in queries: - In the Custom Query tab, check "Use MCP" - Optionally enable "Agentic" for autonomous investigation mode
Getting Started
1. Index the Binary: - Open the Semantic Graph tab - Click "ReIndex Binary" to extract structural relationships - Click "Semantic Analysis" to generate LLM summaries (requires API) - Progress is shown in the status bar
2. Explore the Graph: - List View: Browse all indexed functions with summaries and security flags - Graph View: Visualize call relationships with configurable N-hop depth - Search View: Full-text search across summaries and security annotations
3. Security Analysis: - Click "Security Analysis" to scan for security-relevant features - Results include: network APIs, file I/O, crypto usage, string patterns - Risk levels (LOW/MEDIUM/HIGH) are assigned based on detected features
Quickstart
- If necessary, copy the binary release ZIP archive to the Ghidra_Install/Extensions/Ghidra directory.
- Launch Ghidra -> File -> Install Extension -> Enable GhidrAssist.
- Load a binary and launch the CodeBrowser.
- CodeBrowser -> File -> Configure -> Miscellaneous -> Enable GhidrAssist.
- CodeBrowser -> Window -> GhidraAssistPlugin.
- Ensure the RLHF and RAG database paths are appropriate for your environment.
- Point the API host to your preferred API provider and set the API key.
- (Optional) In the Analysis Options tab, set the Reasoning Effort level (None/Low/Medium/High) for models that support extended thinking.
- Open GhidrAssist with the GhidrAssist option in the Windows menu and start exploring.
Usage Modes
Regular MCP Queries: - Enable "Use MCP" checkbox - Ask questions like "What does the current function do?" - LLM can call tools to get decompilation, cross-references, etc.
Agentic Mode (Recommended): - Enable both "Use MCP" and "Agentic" checkboxes - Ask complex questions like "Find vulnerabilities in this function" or "Analyze the call graph" - The ReAct agent will: 1. Propose investigation steps as a todo list 2. Systematically execute tools to gather information 3. Track progress and accumulate findings 4. Synthesize a comprehensive answer with evidence
Example Queries: - "What security vulnerabilities exist in this function?" - "Trace the data flow from user input to this call" - "Find all functions that modify global variable X" - "Analyze the error handling in the current function"
Screenshots
https://github.com/user-attachments/assets/bd79474a-c82f-4083-b432-96625fef1387
Explain Tab Integration
When viewing a function in the Explain tab: - If the function is indexed, the pre-computed summary is shown instantly - Security panel displays: risk level, activity profile, APIs used - Click "Edit" to modify summaries (protected from auto-overwrite) - Use "Refresh" to re-generate the summary with the LLM
高质量的逆向工程AI辅助工具
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 构建企业知识库 / RAG 检索应用的团队
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- 本地部署优先选 GGUF 量化模型，节省显存并保持响应速度
- 分块大小建议 256-512 tokens，向量库优选 pgvector 或 Qdrant
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- embedding 模型与查询模型不一致导致检索失效
- 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
- CLI：直接 npm install -g / pip install，命令行调用
- 本地部署：CPU 8GB 起，GPU 推荐 16GB+ 显存
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
- 需要让 Claude / Cursor 操作本地工具的 AI 工程师
- 构建多智能体协作系统的 Agent 开发者
- 构建企业知识库 / RAG 检索应用的团队
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- 本地部署优先选 GGUF 量化模型，节省显存并保持响应速度
- 分块大小建议 256-512 tokens，向量库优选 pgvector 或 Qdrant
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- MCP 配置路径拼错或权限不足，重启 Claude Desktop 才生效
- embedding 模型与查询模型不一致导致检索失效
- 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
- +MIT 协议，可免费商用
- +完全开源免费，无授权费用
- +本地部署，数据完全自主可控
- +开发者社区支持，遇问题可查可问
- −安装和初始配置可能需要一定技术基础
- −功能完整性通常不如成熟商业产品
- −技术支持主要依赖开源社区，响应速度不稳定
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一，可自由商用、修改、分发，仅需保留版权声明。
AI Skill Hub 点评：GhidrAssist 的核心功能完整，质量优秀。对于AI 技术爱好者来说，这是一个值得纳入个人工具库的选择。建议先在非生产环境试用，再逐步推广。
| 原始名称 | GhidrAssist |
| 原始描述 | 开源AI工具：An LLM extension for Ghidra to enable AI assistance in RE.。⭐654 · Java |
| Topics | GhidraLLMJava逆向工程 |
| GitHub | https://github.com/symgraph/GhidrAssist |
| License | MIT |
| 语言 | Java |
收录时间：2026-05-25 · 更新时间：2026-05-26 · License：MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。