Patronus AI
AI evaluation and guardrails platform — hallucination detection, safety testing, and LLM-as-a-Judge.
What it does
Automated AI evaluation and security platform with purpose-built LLM judge models. Flagship Lynx model outperforms GPT-4o at hallucination detection in RAG systems. Offers both point-in-time guardrails (toxicity, prompt injection, harmful advice detection) and full-trace application debugging via Percival. Published FinanceBench (financial Q&A benchmark) and GLIDER (explainable evaluation model). Self-serve API with Python SDK and pay-as-you-go pricing.
Security relevance
Provides the evaluation layer that most AI security stacks lack. Detects hallucinations, prompt injection, PII leakage, bias, and toxicity using specialized judge models that are more accurate than generic LLMs for security evaluation. Compliant with OWASP and NIST standards. Custom LLM judges can be configured for domain-specific safety criteria. Addresses the 'who watches the watchers' problem by providing independent verification of AI outputs.
When to use it
Use when you need reliable, automated evaluation of LLM outputs for safety and accuracy. Excellent for teams building RAG applications who need hallucination detection, or for security teams who need to validate that guardrails are actually working. API-first with $5 free credits to start. Integrates into CI/CD pipelines. More accessible than building custom evaluation infrastructure.
OWASP coverage
Risks addressed — mapped to both OWASP Top 10 standards. 4 in LLM, 3 in Agentic.
The raw record
What Yuntona stores. Single source of truth — fork it on GitHub.
name: Patronus AI slug: patronus-ai type: Mixed category: AI Red Teaming url: https://patronus.ai reviewed: 2026-04 added: 2026-04 updated: 2026-04 risks: llm: [LLM01, LLM02, LLM06, LLM09] asi: [ASI01, ASI06, ASI09] complexity: Guided Setup pricing: — audience: Builder lifecycle: [deploy] tags: [API, Evaluation, Guardrails, Hallucination]