Runtime Guardrail & LLM Security Benchmark

The GuardionAI Runtime Guardrail & LLM Security Benchmark systematically tests how well leading LLMs and runtime guardrails withstand real-world attacks. Our evaluation simulates adversarial techniques including prompt injection, jailbreaks, data exfiltration, and indirect attacks using three distinct methods: Crescendo, TAP, and zero-shot approaches.

We measure how consistently each system maintains safe, intended behavior when pushed to its limits, giving you the data you need to make informed decisions about AI security in production. This benchmark gives you transparent, actionable data to evaluate AI security solutions before deploying them in production.

Runtime Guardrail Vendor Comparisons

Side-by-side comparisons under identical attack conditions — designed for procurement, security reviews, and vendor selection.

LLM Vulnerability Comparisons

Compare how foundation models break when attacked — not how they perform at baseline.