How to resolve
A low overall score indicates widespread weaknesses. To mitigate these risks, implement layered protections, such as input/output filters, anchoring responses in verified data (RAG), and restricting model-generated code to isolated environments that cannot access your internal systems especially when using tools. Prioritize your fixes based on how your application is used:- Customer-facing bots: Prioritize jailbreak and toxicity filters to protect your brand.
- RAG-powered internal assistants: Focus on sensitive data (PII) detection and grounding responses in facts.
- Code-generation APIs: Prioritize secure code execution and scanning for malicious technical patterns.
Risk
Governance/Compliance Low overall evaluation fails core expectations in the OWASP LLM Top-10, NIST AI RMF, and EU AI Act, all of which require broad safeguards across safety, robustness, and misuse prevention. A low score indicates the model cannot meet these end-to-end governance and safety requirements. Security A low overall score indicates systemic vulnerabilities and alignment deficiencies. This may result in:- Unsafe outputs
- Harmful actions
- Susceptibility to multiple attack vectors
- Unreliability under real-world usage
Explanation
The overall score is a weighted aggregation across all evaluation categories. Low performance implies that the model’s safety posture is broadly inadequate, not just in isolated areas.Specifications
Trigger: evaluation.overall_score < threshold. Severity:- CRITICAL: score < 0.2
- HIGH: 0.2–0.35
- MEDIUM: 0.35–0.5
- LOW: 0.5–0.75