Deep Dive into AI Defense Dilemma: Evaluating Against Non-Stationary Model Adversaries
ziv_ravid · x · 2026-08-29
Based on the recent OpenAI/Hugging Face incident and report, the author highlights a core blind spot in current AI safety evals: overemphasis on offense, neglect of defense.
Key Arguments:
- Evaluation Paradigm Gap: Current evals (e.g., CTF) mostly test the model as an attacker in a fixed environment. Testing a model as a defender involves facing another constantly evolving model, making the attack distribution non-stationary.
- Defense Evaluation Challenge: You cannot pre-specify all attack vectors (e.g., agents inventing a coordination channel in the artifact repo). Scoring against today's attacker models makes the data stale upon release.
- Non-Stationarity Problem: This mirrors the non-stationarity problem in adversarial ML, but the adversary's improvement is driven by the entire industry's compute, not a gradient step you control.
Potential Solutions (Unresolved):
- Use an ensemble of attackers to avoid overfitting.
- Handicap the defender to test robustness.
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