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OWASP-LLM
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Vector and Embedding Security Assessment
assessing-vector-and-embedding-weaknesses
mukul975/Anthropic-Cybersecurity-Skills
204
This tool provides a comprehensive security assessment framework for Retrieval-Augmented Generation (RAG) systems and vector databases. It tests critical vulnerabilities such as embedding inversion, cross-tenant data leakage, knowledge-base poisoning, and retrieval manipulation, aligning with OWASP LLM08:2025 standards. It is essential for validating the security posture of multi-tenant and vector-search applications.
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Continuous LLM Red Teaming In CI/CD
continuous-llm-red-teaming-with-promptfoo
mukul975/Anthropic-Cybersecurity-Skills
309
Automate security regression testing for Large Language Model (LLM) applications by integrating Promptfoo and DeepTeam into CI/CD pipelines. This skill enforces a critical security gate, automatically testing endpoints against OWASP LLM Top 10, OWASP Agentic threats, and common jailbreaks/prompt injections. It ensures that security patches do not regress over time due to prompt or model changes, providing continuous risk monitoring.
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Assessing LLM System Prompt Leakage Risks
testing-for-system-prompt-leakage
mukul975/Anthropic-Cybersecurity-Skills
429
This guide provides a structured approach to red-teaming Large Language Models (LLMs) to detect sensitive data leakage from system prompts. It simulates advanced attacks—including prompt injection, instruction override, and encoding tricks—to identify embedded secrets, API keys, and proprietary business logic. Essential for validating adherence to OWASP LLM07 and securing AI applications against data exfiltration.
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