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ai-threat-testingAI 威胁测试

Agent Skill

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

总安装

783

周安装

32

GitHub Stars

222

下载量

251
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ai-threat-testing(AI 威胁测试)
来源仓库:https://github.com/transilienceai/communitytools
仓库路径:skills/ai-threat-testing
安装命令:
npx skills add https://github.com/transilienceai/communitytools --skill ai-threat-testing
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/transilienceai/communitytools --skill ai-threat-testing

简介

AI Threat Testing 依据 OWASP LLM Top 10 标准进行安全测试,部署专用代理检测漏洞。

  • 适用于授权 AI 安全评估,涵盖提示注入、输出处理、供应链攻击等十大类别。
  • 每个代理专注一类漏洞(如 Prompt Injection、Code Injection),输出带 PoC 的专业报告。
  • 使用前需指定目标(URL/API/本地模型),并选择完整扫描或特定漏洞范围。
  • ai-threat-testing 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

AI Threat Testing

Test LLM applications for OWASP LLM Top 10 vulnerabilities using 10 specialized agents. Use for authorized AI security assessments.

Quick Start

1. Specify target (LLM app URL, API endpoint, or local model)
2. Select scope: Full OWASP Top 10 | Specific vulnerability | Supply chain
3. Agents deploy, test, capture evidence
4. Professional report with PoCs generated

Primary Agents

Each agent targets one OWASP LLM vulnerability:

  1. Prompt Injection (LLM01): Direct/indirect injection, system prompt extraction
  2. Output Handling (LLM02): Code/XSS injection, unsafe deserialization
  3. Training Poisoning (LLM03): Membership inference, backdoors, data extraction
  4. Resource Exhaustion (LLM04): Token flooding, DoS, cost impact
  5. Supply Chain (LLM05): Dependency scanning, plugin security
  6. Excessive Agency (LLM06): Privilege escalation, unauthorized actions
  7. Model Extraction (LLM07): Query-based theft, data reconstruction
  8. Vector Poisoning (LLM08): RAG injection, retrieval manipulation
  9. Overreliance (LLM09): Hallucination testing, confidence manipulation
  10. Logging Bypass (LLM10): Monitoring evasion, forensic gaps

See reference/llm0X-*.md for attack playbooks.

Workflows

Full Assessment (4-8 hours):

- [ ] Reconnaissance
- [ ] Deploy all 10 agents
- [ ] Execute exploits
- [ ] Capture evidence
- [ ] Generate report

Focused Testing (1-3 hours):

- [ ] Select vulnerability (LLM01-10)
- [ ] Deploy agent
- [ ] Execute techniques
- [ ] Document findings

Supply Chain Audit (2-4 hours):

- [ ] Inventory dependencies
- [ ] Scan CVEs
- [ ] Test plugins/APIs
- [ ] Verify model provenance

Integration

Enhances /pentest with AI-specific testing:

  • Traditional pentesting + AI threat testing = complete security assessment
  • Chain vulnerabilities across traditional and AI vectors
  • Unified reporting with CVSS scores

Key Techniques

Prompt Injection: Instruction override, system prompt extraction, filter evasion Model Extraction: Query sampling, token analysis, membership inference Data Poisoning: Behavioral anomalies, backdoor triggers, bias analysis DoS: Token flooding, recursive expansion, context exhaustion Supply Chain: CVE scanning, plugin audit, model verification MCP Tool Abuse: MCP server inspectors/debuggers often expose /api/mcp/connect or similar endpoints that accept serverConfig with arbitrary command parameters — unauthenticated RCE. Check for MCP Inspector, MCP Playground, or any MCP debugging UI on non-standard ports (6274, 3000, etc.).

Evidence Capture

All agents collect: screenshots, network logs, API responses, errors, console output, execution metrics.

Reporting

Automated reports include: executive summary, detailed findings (CVSS scores), PoC scripts, evidence, remediation guidance.

Critical Rules

  • Written authorization REQUIRED before testing
  • Never exceed defined scope
  • Test in isolated environments when possible
  • Document all findings with reproducible PoCs
  • Follow responsible disclosure practices

Integration

  • Integrates with /pentest skill for comprehensive security testing
  • AI-specific vulnerability knowledge in /AGENTS.md
  • Attack playbooks in reference/llm0X-*.md

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.27%
按下载量换算91

Claude

29.82%
按下载量换算75

Cursor

18.12%
按下载量换算45

Gemini CLI

8.56%
按下载量换算21

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

未通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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