Snyk's Evo platform introduces a context-aware AI model risk scoring system built on Likelihood × Impact, where Likelihood is derived from Attack Success Rate (ASR) measured against real adversarial attacks and Impact reflects deployment-specific consequences. Unlike static safety cards, scores are weighted by deployment archetype — coding agent, chatbot, personal assistant — because the same model carries fundamentally different risks depending on how it's used. The system covers both direct prompt attacks and indirect prompt injection targeting agentic systems. Findings are organized in a three-level taxonomy mapped to OWASP LLM Top 10, OWASP Agentic, MITRE ATLAS, and NIST, enabling teams to move from a headline score to specific attacker goals and connect findings directly to policy enforcement. Snyk telemetry from 3,044 organizations shows that for every known model, roughly 2.8 more AI components run unmanaged, making inventory a prerequisite to risk scoring.
