Limits of AI Red-Team Evaluations: What Benchmarks Can and Cannot Prove
A new arXiv preprint introduces the concept of the 'evidential ceiling' for AI red-team evaluations, providing a closed-form boundary for what such evaluations can substantiate. The authors show that while current benchmarks can provide strong evidence of safety for common, high-frequency harms, they are insufficient to certify safety against rare, catastrophic failures. This framework applies to both passive benchmarks and adaptive red-teaming methods.
Why it matters: The work offers a rigorous, quantitative basis for understanding the limitations of AI safety evaluations, informing both regulatory and industry practices.
Full story at: arXiv Cryptography and Security ↗