Announcing the Artificial Analysis Cyber Index and the Artificial Analysis Cyber Index Alliance, a new standard for evaluating AI models on enterprise cyber defense
The Artificial Analysis Cyber Index Alliance brings together industry partners to create a new standard for evaluating how AI models perform on enterprise cyber defense tasks. The Alliance launches alongside the Artificial Analysis Cyber Index, which combines three partner-contributed and open benchmarks to evaluate how well agents find and fix vulnerabilities. As models demonstrate increasingly advanced cyber offense capabilities, it becomes more relevant for AI labs and companies alike to understand how models perform on cyber defense tasks and which perform best.
We’re announcing the Cyber Index Alliance today with
@CollinearAI,
@IBM,
@nvidia, and
@vercel as launch partners.
Benchmarks in the Artificial Analysis Cyber Index:
➤ CWE-Bench-AA, from
@CollinearAI, covers auditing and patching: 120 held-out tasks spanning all ten OWASP Top 10 (2025) categories, across C/C++, Go, Java, JavaScript/TypeScript, Python and Rust.
➤ DeepsecBench-AA, from
@vercel, isolates discovery: Given a codebase and a budget, the agent needs to find every vulnerability present, and is scored against a golden set of findings from human security reviewers. Real findings are rewarded and benign code flagged as vulnerable is penalized.
➤ CyberGym-E2E-AA, from
@BerkeleyRDI, runs end to end: Find the memory-safety bug, write a proof-of-concept that triggers the crash, then patch it so the crash no longer reproduces.
Key results:
➤ Grok 4.7 (xhigh) and MiMo-V2.6-Pro lead the Cyber Index scoring 56, followed by GPT-6 Luna (max, 53), GLM-5.3-Flash (50) and Muse Spark 1.3 (xhigh, 44).
➤ Safety refusals hold back several frontier models: GPT-6 Sol (max), GPT-6 Astra (max), Claude Opus 5.5 (max with fallback), Claude Fable 5.1 (max with fallback) and Gemini 3.8 Flash (high) decline tasks representing 32-38% of the Cyber Index on safety grounds. Despite frontier agentic coding capabilities, they trail the leaders by 19 to 31 points. Most of the gap comes from CyberGym-E2E-AA, where GPT-6 Sol and GPT-6 Astra refuse every task, Claude Opus 5.5 refuses 98% and Claude Fable 5.1 refuses 99%.