The Oversight Gap: What LLM Safety Monitors Miss, and Why It Is Not Capability
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Abstract (arXiv)
Several properties safety monitors are asked to certify, among them cross-tenant noninterference, sandbagging and evaluation awareness, are 2-safety hyperproperties, witnessed only by two executions. The standard consequence is a binary impossibility: one trace cannot decide them. We replace the binary with a measurement. A tight bound puts the balanced accuracy of any single-trace monitor at tfrac12+tfrac12,TV(P0,P1), turning undecidability into a graded detectability frontier and defining an oversight gap: a monitor's shortfall below it. On a leak family with closed-form TV, nine LLM monitors are optimal at TV=0 but capture little signal as TV grows; at TV=1, where a 20-line membership check scores 100%, they average 60.9%. That shortfall is mostly not capability: naming what to check closes 61% of it while leaving the TV=0 control at chance. The same split runs through a 2×2 factorial: an imagined second run leaves monitors at chance (50.4%) while the same rule on an executed second run reaches 90.0%, and a stored oracle without a comparison procedure yields only 68.2%. Information and procedure are each necessary and neither is capability. Under nondeterminism, replay tracks a closed-form k-replay curve only under the right projection, and a projection frontier shows the resulting dilemma is forced: narrow misses 98.6% of off-channel leaks, broad flags 75.7% of clean traffic, and attainable accuracy decays like 1/(qm) in the benign-variation rate and the channel count. Finally, two frontier LLM judges certified an earlier version of our own benchmark as sound while a sign test found a directional bias (p=2.7×10-5) that invalidated three of our findings. Construction validity for hyperproperty benchmarks should be proved mechanically, not audited by models.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 19 references that are in the map. First posted on arXiv 7 Sep 2026.
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References · 13
- 2023Towards Understanding Sycophancy in Language Modelscited by 988
- 2022Discovering Language Model Behaviors with Model-Written Evaluationscited by 805
- 2017Counterfactual Fairnesscited by 802
- 2024Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Trainingcited by 478
- 2024Frontier Models are Capable of In-context Schemingcited by 252
- 2023AI Control: Improving Safety Despite Intentional Subversioncited by 207
- 2024AI Sandbagging: Language Models can Strategically Underperform on Evaluationscited by 131
- 2013Optimal Algorithms for Testing Closeness of Discrete Distributionscited by 37
- 2025Pairwise or Pointwise? Evaluating Feedback Protocols for Bias in LLM-Based Evaluationcited by 23
- 2026Detecting Safety Violations Across Many Agent Tracescited by 11
- 2018Monitoring Hyperpropertiescited by 3
- 2021The Complexity of Monitoring Hyperpropertiescited by 3
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- 2026Distributed Attacks in Persistent-State AI Controlcited by 3
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