LLM and RAG audits including safety, security, hallucination, and citation checks
Project description
rai-audit-llm
LLM and RAG audits for prompt injection, unsafe output, toxicity, faithfulness, citations, and retrieval security.
CLI
Audit captured responses from a YAML suite:
rai-audit llm run --suite packages/rai-audit-llm/examples/llm_audit_suite.yml --format html
Use --audit-type rag to run only RAG checks or --audit-type rag-security to
scan only retrieval security cases.
Python API
from rai_audit.llm import LLMAudit, load_test_suite
suite = load_test_suite("packages/rai-audit-llm/examples/llm_audit_suite.yml")
report = LLMAudit(suite, persist=False).run()
For live evaluation, pass responder=lambda case: .... RAG faithfulness checks
require an LLM-as-judge verdict: provide judge_result in captured YAML or pass
faithfulness_judge=lambda case, response: {"score": 0.9, "reasoning": "..."}.
All findings include OWASP LLM Top 10 2025 references where applicable.
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