Analysis · curated 5 Aug 2026
AI Model Risk Intelligence Know Which Models You Can Trust Before You Deploy
First reported snyk.io
Coverage timeline
Single-source analysis — first reported, latest, and curated coincide.
Why it matters
Snyk's Evo model-risk scoring offers defenders a way to compare LLMs by real adversarial attack success rates before deployment, but the piece is a vendor write-up of a commercial feature rather than an obtainable, runnable artifact.
Snyk describes the rebuilt AI Model Risk Intelligence scoring in its Evo product, which computes a 0-1000 risk score from Likelihood (Attack Success Rate against real adversarial tests like extraction prompts, multi-turn escalation, persona jailbreaks, and tree-of-attack strategies) multiplied by Impact. The methodology breaks scores down by attacker goal (PII extraction, system-prompt extraction via injection, insecure code generation) and runs attacks against a baseline system-prompt hardening defense to reflect production risk.