Introducing Adversarial Cost to Exploit (ACE)
We’re thrilled to unveil ACE, a groundbreaking benchmark that measures the token expenditure an autonomous adversary must incur to compromise a large language model (LLM) agent. Unlike traditional pass/fail methods, our approach quantifies adversarial effort in financial terms, paving the way for nuanced game-theoretic analysis.
Key Highlights:
- Diverse Model Testing: We examined six budget-tier models, including Gemini Flash-Lite and GPT-5.4 Nano.
- Stellar Performance: Haiku 4.5 stood out, showcasing a mean adversarial cost of $10.21—an astounding disparity compared to $1.15 for GPT-5.4 Nano.
- Future-Ready Methodology: We acknowledge that our methodology is still evolving, and we highly value community feedback.
This work is just the beginning! Join us as we push boundaries in AI security and analytics.
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