mlsanityData to 7 Oct 2026

The Oversight Gap: What LLM Safety Monitors Miss, and Why It Is Not Capability

arXiv 2609.07162 ↗First posted 7 Sep 2026cs.LG cs.CRcited by 0

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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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13 of 19 references.

References · 13

  1. 2023Towards Understanding Sycophancy in Language Modelscited by 988
  2. 2022Discovering Language Model Behaviors with Model-Written Evaluationscited by 805
  3. 2017Counterfactual Fairnesscited by 802
  4. 2024Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Trainingcited by 478
  5. 2024Frontier Models are Capable of In-context Schemingcited by 252
  6. 2023AI Control: Improving Safety Despite Intentional Subversioncited by 207
  7. 2024AI Sandbagging: Language Models can Strategically Underperform on Evaluationscited by 131
  8. 2013Optimal Algorithms for Testing Closeness of Discrete Distributionscited by 37
  9. 2025Pairwise or Pointwise? Evaluating Feedback Protocols for Bias in LLM-Based Evaluationcited by 23
  10. 2026Detecting Safety Violations Across Many Agent Tracescited by 11
  11. 2018Monitoring Hyperpropertiescited by 3
  12. 2021The Complexity of Monitoring Hyperpropertiescited by 3
1 more
  1. 2026Distributed Attacks in Persistent-State AI Controlcited by 3

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