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Show HN: ACE – A dynamic benchmark measuring the cost to break AI agents
A benchmark that quantifies the economic cost (token expenditure in dollars) for an autonomous adversary to breach an LLM agent, enabling game-theoretic analysis of attack rationality, moving beyond binary pass/fail metrics.
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A benchmark that quantifies the economic cost (token expenditure in dollars) for an autonomous adversary to breach an LLM agent, enabling game-theoretic analysis of attack rationality, moving beyond binary pass/fail metrics.
ACE introduces a critical, quantifiable metric for AI agent security: the economic cost of exploitation. Moving beyond binary pass/fail, this benchmark provides a tangible dollar value for adversarial effort, enabling organizations to conduct game-theoretic analyses on their LLM agent deployments. This directly addresses a significant pain point in enterprise AI adoption: understanding and mitigating financial risks associated with agent vulnerabilities. The findings, particularly the order-of-magnitude difference in exploit cost for Claude Haiku 4.5, offer actionable intelligence for model selection and security investment. ACE represents a vital step towards mature, economically informed AI security strategies, transforming abstract security concerns into concrete financial considerations for B2B decision-makers.
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.
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