ai-llm-app-attack
SkillFiles & storageAI/LLM应用攻击:提示注入,Agent工具滥用RCE,RAG投毒,MCP供应链,torch.load pickle RCE。Use when testing LLM apps, agents, RAG, MCP plugins, or AI model file risks.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the ai-llm-app-attack skill
What this skill tells your AI
The instructions your AI receives, as published by langbyyi/cyberstrikeai-src in skills/ai-llm-app-attack/SKILL.md and read by ahel’s review.
AI / LLM 应用攻击
按实际攻击面读取对应参考，不把公开框架条目直接当作漏洞：
- 应用与 Agent 攻击面：
references/ai-app-security.md - 基线和部署控制：
references/ai-baseline-security.md - 数据/RAG/训练数据：
references/ai-data-security.md - 身份、授权与跨租户：
references/ai-identity-security.md - 模型文件、加载和供应链：
references/ai-model-security.md - GAARM 风险矩阵：
references/gaarm-risk-matrix.md - 低影响测试方法与证据：
references/testing-methodology.md
=== AI/LLM应用(大模型应用爆发期真实攻击面) ===
提示注入: 直接(忽略上文输出system prompt) | 间接(更危险):指令藏RAG文档/网页/邮件/工具返回值/图片EXIF → 劫持Agent
🚨Agent工具滥用(最高危,直达RCE): code interpreter→注入执行 | fetch工具→SSRF内网/云元数据 | 文件工具→读/etc/passwd写webshell
| SQL工具→导全表 | shell工具→命令注入 → 验证:实际触发工具副作用(OOB回连/读到文件)才写Fact
系统提示泄露/RAG投毒/过度授权跨租户越权/MCP插件供应链/资源成本攻击(烧token) | 输出处理:LLM输出进eval/SQL/前端→二次注入/存储XSS
模型文件: torch.load默认pickle→RCE | 发现端点:抓流量找/chat /agent /tool,问Agent"你有哪些工具"
Signals
- GitHub stars
- 115
- Forks
- 4
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
ai-llm-app-attack-langbyyi- Source
- github.com/langbyyi/cyberstrikeai-src