Search
Security · Retrieval-augmented generation
Skills
Sort:BestMost starsTrending todayTrending this weekTrending this monthNewestRecently updatedName
| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | 当目标为 LLM 应用/Chatbot/智能客服/AI 助手/Copilot/Agent/RAG 知识库/多模态模型,或发现用户输入进入大模型提示、工具调用、知识库检索、对话记忆、文件解析,或需要测试提示词注入/越狱逃逸/System Prompt 泄露/训练数据与敏感信息泄露/RAG 检索污染/Agent 记忆污染/工具滥用与命令执行/SSRF/沙箱逃逸时调用。负责 OWASP LLM… | zhaji2333/ | 115 | — | ~4.7k | Automated safety check: Warn | MIT | 26 days ago |
| 2 | Black-box security audit of a DEPLOYED AI agent (not the MCP server behind it) — tool-call hijacking, cross-session memory poisoning, confused-deputy via connected tools, agent-to-agent IDOR… | awarexone/ | 5.3k | — | ~1.1k | Automated safety check: Pass | MIT | 2 days ago |
| 3 | For any security-related task — threat triage, incident response, MITRE ATT&CK mapping, CVE lookup, exploit analysis, defensive control validation, red/blue/purple team work — always consult the… | lyonzin/ | 292 | — | ~2.3k | Automated safety check: Pass | MIT | 2 days ago |
| 4 | Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses) — persistent corpus poisoning that survives across sessions and users (distinct from… | elementalsouls/ | 4.8k | — | ~2.6k | Automated safety check: Pass | MIT | yesterday |
| 5 | 6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building… | sickn33/ | 47k | 2 repos | ~1.9k | Automated safety check: Pass | MIT | 2 days ago |
| 6 | Probes Retrieval-Augmented Generation pipelines for indirect prompt injection via poisoned retrieved documents and embedding-space manipulation, using NVIDIA garak, Promptfoo red-team plugins, and… | mukul975/ | 34k | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | 1 mo ago |
| 7 | Assess and harden LLM applications and agentic systems against prompt injection, tool misuse, excessive agency, memory poisoning, RAG data leakage, and model supply-chain risk, mapped to the OWASP… | trilwu/ | 157 | — | ~2.9k | Automated safety check: Pass | MIT | 1 mo ago |
| 8 | LLM and AI application security testing skill for prompt injection (direct, indirect, multimodal), system-prompt extraction, RAG poisoning, memory poisoning, MCP server injection, skill-file… | hardw00t/ | 105 | — | ~2.8k | Automated safety check: Pass | No licence | 5 mo ago |
| 9 | AI/LLM 间接 Prompt 注入攻击。当目标 AI 系统会处理外部数据源(网页、文档、邮件、数据库、API 返回)时使用。覆盖间接注入、工具链劫持、RAG 投毒、数据外泄等技术。OWASP LLM Top 10 1 漏洞类别 | wgpsec/ | 1.8k | — | ~704 | Automated safety check: Notes | No licence | yesterday |