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[2602.16984] Fundamental Limits of Black-Box Safety Evaluation: Information-Theoretic and Computational Barriers from Latent Context Conditioning
Summary
This paper explores the limitations of black-box safety evaluations in AI systems, highlighting the challenges posed by latent context conditioning and establishing fundamental barriers to reliable risk estimation.
Why It Matters
Understanding the limitations of black-box safety evaluations is crucial for developing safer AI systems. This research provides insights into when additional safeguards are necessary, which is vital for practitioners aiming to ensure deployment safety in AI applications.
Key Takeaways
- Black-box evaluations may not reliably predict deployment performance due to latent context conditioning.
- Minimax lower bounds indicate significant expected errors in passive evaluations.
- Adaptive evaluations still face substantial worst-case errors, emphasizing the need for more robust testing methods.
- Computational barriers exist that prevent reliable risk estimation without privileged information.
- Architectural constraints and monitoring are essential for ensuring worst-case safety assurance.
Computer Science > Artificial Intelligence arXiv:2602.16984 (cs) [Submitted on 19 Feb 2026] Title:Fundamental Limits of Black-Box Safety Evaluation: Information-Theoretic and Computational Barriers from Latent Context Conditioning Authors:Vishal Srivastava View a PDF of the paper titled Fundamental Limits of Black-Box Safety Evaluation: Information-Theoretic and Computational Barriers from Latent Context Conditioning, by Vishal Srivastava View PDF HTML (experimental) Abstract:Black-box safety evaluation of AI systems assumes model behavior on test distributions reliably predicts deployment performance. We formalize and challenge this assumption through latent context-conditioned policies -- models whose outputs depend on unobserved internal variables that are rare under evaluation but prevalent under deployment. We establish fundamental limits showing that no black-box evaluator can reliably estimate deployment risk for such models. (1) Passive evaluation: For evaluators sampling i.i.d. from D_eval, we prove minimax lower bounds via Le Cam's method: any estimator incurs expected absolute error >= (5/24)*delta*L approximately 0.208*delta*L, where delta is trigger probability under deployment and L is the loss gap. (2) Adaptive evaluation: Using a hash-based trigger construction and Yao's minimax principle, worst-case error remains >= delta*L/16 even for fully adaptive querying when D_dep is supported over a sufficiently large domain; detection requires Theta(1/epsilon) quer...
