AI Skill Hub 强烈推荐:对手图谱 是一款优质的Agent工作流。AI 综合评分 8.5 分,在同类工具中表现稳健。如果你正在寻找可靠的Agent工作流解决方案,这是一个值得深入了解的选择。
AI驱动的MITRE ATT&CK威胁情报平台
对手图谱 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
AI驱动的MITRE ATT&CK威胁情报平台
对手图谱 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
# 克隆仓库 git clone https://github.com/anpa1200/adversarygraph cd adversarygraph # 查看安装说明 cat README.md # 按 README 完成环境依赖安装后即可使用
# 查看帮助 adversarygraph --help # 基本运行 adversarygraph [options] <input> # 详细使用说明请查阅文档 # https://github.com/anpa1200/adversarygraph
# adversarygraph 配置说明 # 查看配置选项 adversarygraph --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 ADVERSARYGRAPH_CONFIG="/path/to/config.yml"

Self-hosted AI-assisted CTI-to-detection workbench for ATT&CK mapping, hypothesis-driven threat hunting, Threat Radar early warning, Evidence-to-Detection Graph reasoning, IOC enrichment, CVE Library correlation, malware-analysis triage, asset attack-surface review, Attack Simulation, and SIEM validation.
Current release: v8.1.2 (2026-10-20).
This release adds MITRE ATT&CK v20 support across Enterprise, Mobile and ICS, preserves historical catalogs, and carries source versions into Navigator exports. It also includes governed report promotion, durable research/workflow authority, deterministic PCAP evidence integration, investigation workflows, transactional outbox processing and database migration safety improvements. See the 8.1 release notes, release summary and release acceptance record.
Deploy only the digest-pinned image family from the published release's adversarygraph-images.env. A source version is not proof of completed registry publication; the protected tag workflow builds, scans and verifies all nine images before creating the release. Historical beta tags remain immutable. Deployment-specific provider, embedding, SIEM and operational acceptance still requires the operator's own configuration and test inputs.
On 2026-08-08, development builds exposed a high-severity denial-of-service advisory in nanoid (GHSA-2v37-7h3g-55p8) and an affected PostCSS pin (GHSA-fxqj-rqcc-2cmp). Pull request #30, included in v7, updates nanoid to 3.3.18 and PostCSS to 8.5.26 while retaining npm audit --audit-level=high as a blocking image-build check.
Older source Dockerfiles resolve MalwareGraph from a moving upstream branch, so tag history alone cannot prove the dependency contents of a locally rebuilt image. Current-source operators should pull the release commit, rebuild MalwareGraph without cache, and retain the successful audit output:
git pull --ff-only origin main
docker compose build --no-cache malwaregraph
docker compose up -d malwaregraph
The client now uses React Router 7, and the reviewed lockfile passes the required npm audit. Treat that as point-in-time release evidence and repeat the audit for every candidate; see Security Policy.
On 2026-08-08, current-development source builds began failing the MalwareGraph UI security gate after npm reported a high-severity denial-of- service issue in nanoid (GHSA-2v37-7h3g-55p8; affected versions before 3.3.17). The same lockfile patch also pinned a PostCSS version affected by GHSA-fxqj-rqcc-2cmp. Pull request #30 fixes the build by updating nanoid to 3.3.18 and PostCSS to 8.5.26 while retaining npm audit --audit-level=high as a blocking image-build check.
No published tag contains the faulty lockfile patch: it was introduced after the latest published v6.0.0 tag. However, the v4.0.0 through v6.0.0 source Dockerfiles resolve MalwareGraph from the moving upstream main branch, so rebuilding those tags can select dependency state that differs from the original release build. Existing image contents must be checked directly; tag history alone cannot prove whether a previously built image contains an affected package.
For current source installations, update main and rebuild the MalwareGraph image before starting the service:
git pull --ff-only origin main
docker compose build --no-cache malwaregraph
docker compose up -d malwaregraph
Confirm the build completes the high-severity npm audit and reports nanoid 3.3.18 and PostCSS 8.5.26. Two upstream moderate-severity React Router findings may still be reported; they are separate from this fixed high-severity build failure.
git clone https://github.com/anpa1200/adversarygraph.git
cd adversarygraph
cp .env.example .env
Edit .env and set strong local secrets. AI features are optional for the base platform. To use them, configure an approved local OpenAI-compatible endpoint or an operator-approved cloud provider as described in the relevant guide.
docker compose config --quiet
docker compose pull
docker compose up -d --build
./scripts/selftest.sh
This checkout is a source-build installation. Its custom adversarygraph-*:local-scan images are built locally; docker compose pull only refreshes pinned third-party runtime images and intentionally skips those build targets. Do not replace the custom image variables with mutable latest tags. A prebuilt production deployment from current-development source requires all nine immutable image digests from the exact release's adversarygraph-images.env attachment; the historical v6.0.0 release does not contain that complete artifact set. The eighth family is the isolated scanner MCP image and the ninth is the deterministic PCAP analyzer. Use a manifest only after the matching tag workflow publishes and verifies that complete artifact set. The self-test waits up to fifteen minutes for first-boot ATT&CK/ATLAS reference ingestion; override this with SELFTEST_TIMEOUT when operating across a slower network.
Open:
http://localhost:3000http://localhost:3000/api/healthhttp://localhost:3000/api/readyhttp://localhost:3000/docsThe default Compose deployment binds the public UI and reference docs to localhost and keeps the API, Redis, malware-analysis service, and lab fixtures on the internal Compose network. Local configuration is stored in .env; the default persistent database is ${ADVERSARYGRAPH_DB_DIR:-./data/postgres}. See local storage and permissions before deleting data directories or Docker volumes.
AdversaryGraph 是一个基于 AI 驱动的威胁情报(CTI)转检测工作台。它专门用于 MITRE ATT&CK 映射与检测缺口分析(Detection-gap analysis),旨在帮助安全分析师通过 AI 技术将非结构化的威胁情报高效转化为结构化的攻击图谱,从而识别防御体系中的薄弱环节。
本项目提供强大的可视化与分析能力:Navigator 模块支持完整的 ATT&CK/ATLAS 矩阵(涵盖 Enterprise, Mobile, ICS 等),并利用 D3.js 实现缩放与双层着色功能;Threat Actor Library 内置了 MITRE ATT&CK 威胁组织、别名及战役(Campaign)关系库;AI Analysis 模块支持上传 PDF/DOCX/TXT 或直接粘贴文本,通过流式 LLM 提取关键信息。
在开始使用前,请确保您的系统已安装 Docker 及 Docker Compose (v2)。此外,您需要准备一个云端 LLM 供应商的 API key(如 Claude 或 OpenAI),或者配置一个支持 OpenAI 兼容协议的本地 LLM 端点(如 Ollama 或 vLLM)以实现本地化部署。
项目采用容器化部署方式。首先通过 git clone 克隆仓库并进入目录,随后通过复制 .env.example 生成配置文件。最后使用 Docker Compose 进行环境构建与启动,确保所有依赖服务(包括数据库与 API 服务)能够自动编排运行。
项目提供 Quick Start 快速入门指南。用户可以通过 Navigator 界面进行交互式探索,利用 AI 分析功能处理威胁情报文档,并结合 Reference Book 进行精确的 TTP 交叉引用,实现从情报输入到检测映射的完整闭环。
部署前需编辑 .env 文件以配置环境变量。您可以填入 ANTHROPIC_API_KEY、OPENAI_API_KEY 或 GEMINI_API_KEY 等云端密钥。若使用本地模型,请配置 LOCAL_LLM_BASE_URL(例如指向 host.docker.internal:11434)并设置相应的 MODEL 名称,以确保容器内服务能正确访问本地 LLM。
AdversaryGraph 集成了 Anomaly Detection Atlas 作为独立的 Docker 服务。通过侧边栏的 Reference Book,用户可以访问完整的文档站点,系统会自动加载 ttp-reference-index.json 实现与原文段落或表格的精确匹配。此外,系统提供 API 接口(如 /api/sync/trigger)用于触发数据同步任务。
高质量的AI安全工作流平台
该工具使用 NOASSERTION 协议,商用场景请仔细阅读协议条款,必要时咨询法律意见。
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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。
📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。
总体来看,对手图谱 是一款质量优秀的Agent工作流,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。
| 原始名称 | adversarygraph |
| 原始描述 | 开源AI工作流:AI-powered MITRE ATT\&CK threat intelligence platform — D3.js navigator, APT com。⭐8 · HTML |
| Topics | ai-securityatt-ck-navigatorattack-mapping |
| GitHub | https://github.com/anpa1200/adversarygraph |
| License | NOASSERTION |
| 语言 | HTML |
收录时间:2026-06-18 · 更新时间:2026-06-20 · License:NOASSERTION · AI Skill Hub 不对第三方内容的准确性作法律背书。
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