一键导入
ai-llm-app-attack
AI/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.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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AI/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.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
内网域攻击:BloodHound,Kerberoast,ADCS ESC1/ESC8,NTLM Relay,Coerce,DACL,DCSync,Zerologon/NoPac/PrintNightmare,mitm6,LLMNR,Linux内网。Use when attacking Active Directory, ADCS, NTLM relay, or internal domain.
侦察/攻击面测绘:被动whois/amass/crt.sh/FOFA/Shodan,主动subfinder/httpx/naabu/katana/nuclei,DNS地域/CDN/Nginx catch-all/宝塔/UniApp指纹。开局第一动作,认知写入项目黑板。Use when starting recon, asset mapping, fingerprinting, or CDN/DNS bypass discovery.
APK/EXE/二进制:UniApp/DCloud/Flutter逆向,证书固定绕过,导出组件,内存破坏exploit链,IoT固件。Use when reversing APK/EXE, UniApp/Flutter, native .so, or memory-corruption exploits.
区块链/智能合约:Etherscan,slither/mythril,重入/访问控制/预言机/闪电贷,跨链桥,RPC暴露。Use when auditing smart contracts, DeFi, or blockchain attack surfaces.
能力原语+状态空间搜索:read/write/exec/ssrf等原语凑RCE等式A-F,低危映射,正反向搜索,跨域兑现。Use when no single RCE, chaining low-severity vulns, or deriving novel attack chains.
云攻击:元数据API,S3/K8s,AWS/Azure/GCP身份提权,MinIO矩阵,阿里云FC,ChengZi SDK解密。Use when attacking cloud metadata, IAM, K8s, MinIO, Aliyun FC, or cloud post-ex.
| name | ai-llm-app-attack |
| description | AI/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. |
| tags | ["渗透测试","penetration-testing","红队"] |
=== 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"