This paper characterizes three challenges—benchmark vulnerabilities, temporal staleness, and runtime uncertainty—that undermine security evaluations of AI agents and outlines directions for more robust frameworks.
Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data,
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Measuring Security Without Fooling Ourselves: Why Benchmarking Agents Is Hard
This paper characterizes three challenges—benchmark vulnerabilities, temporal staleness, and runtime uncertainty—that undermine security evaluations of AI agents and outlines directions for more robust frameworks.