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PURPLELLAMA
LLM safety and security tools from Meta
MIT
ABOUT
Organizations deploying LLMs in production lack standardized tooling for evaluating and improving model security — relying on ad-hoc prompt injection testing, manual red-teaming, and inconsistent input/output filtering that can miss critical vulnerabilities. PurpleLlama provides a modular suite of security benchmarks and tools including CybersecEval for automated red-teaming, input/output guardrails for content filtering, and prompt injection detectors — enabling teams to systematically evaluate LLM attack surfaces and deploy defenses without building security infrastructure from scratch.
INTEGRATION GUIDE
1. Run automated cyber attack simulations against LLM deployments using CybersecEval benchmarks to identify prompt injection, jailbreak, and data extraction vulnerabilities
2. Deploy input and output guardrails from PurpleLlama to filter toxic content, block prompt injection attempts, and enforce safety policies in production LLM serving pipelines
3. Generate systematic red-teaming datasets for evaluating model robustness across attack categories, including encode-based attacks, multi-turn jailbreaks, and payload smuggling techniques
TAGS
llm-securityred-teamingmetasafetyguardrails