Show HN: ACE – A dynamic benchmark measuring the cost to break AI agents

Show HN: ACE – A dynamic benchmark measuring the cost to break AI agents We built Adversarial Cost to Exploit (ACE), a benchmark that measures the token expenditure an autonomous adversary must invest to breach an LLM agent. Instead of binary pass/fail, ACE quantifies adversarial effort in dollars, enabling game-theoretic analysis of when an attack is economically rational.

We tested six budget-tier models (Gemini Flash-Lite, DeepSeek v3.2, Mistral Small 4, Grok 4.1 Fast, GPT-5.4 Nano, Claude Haiku 4.5) with identical agent configs and an autonomous red-teaming attacker.

Haiku 4.5 was an order of magnitude harder to break than every other model; $10.21 mean adversarial cost versus $1.15 for the next most resistant (GPT-5.4 Nano). The remaining four all fell below $1.

This is early work and we know the methodology is still going to evolve. We would love nothing more than feedback from the community as we iterate on this.