Skip to content

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

Sep 2026 · 0 citations · 19 references
Computer Science

TL;DR

A tight bound puts the balanced accuracy of any single-trace monitor at $\tfrac12+\tfrac12\,TV(P_0,P_1)$, turning undecidability into a graded detectability frontier and defining an oversight gap: a monitor's shortfall below it.

Abstract

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 $\tfrac12+\tfrac12\,TV(P_0,P_1)$, 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 $2{\times}2$ 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 ($p=2.7\times10^{-5}$) that invalidated three of our findings. Construction validity for hyperproperty benchmarks should be proved mechanically, not audited by models.

View source

Similar papers

Preprint Sep 2026

Safety Monitors Mostly Catch What the Model Already Refuses

This work measures recall on exactly those prompts, defined by sampling the target model and judging its responses, defined by sampling the target model and judging its responses.

Sripad Karne · 1 citation
Preprint Sep 2026

Scan the Skill, Govern the Action: Composing Registry Verdicts with Runtime Consequence Control

Agent skill registries screen what they publish. OpenClaw's security team reported that its scanners overlap on at most 10.4% of combined positives, and 81.9% of flagged skills are caught by one scanner alone. We take that as given, and suggest the open question is not which scanner is right but which is being asked. E...

R. Taneja, T. Weber · 0 citations
#artificial intelligence Preprint Sep 2026

Compute-Bounded Security Assurance - Coverage, Verification, and Response under Resource Constraints

A conceptual defensive architecture separates evidence analysis, adjudication, and operational authority and connects coverage to fallible evidence checking, proper scoring of factual grounding, complete resource accounting, service capacity, and a response model that includes mitigation delay.

VG Jithin, PS Ditto · 0 citations
#natural language process... Preprint Oct 2026

The Same Zero: Why Identical ASR Can Imply Different Guarantees in LLM-Agent Security

LLM-agent security has produced a dense landscape of defenses - prompt hardening, content filters, permission gates, sandboxes - yet no framework tells a deployer what a defense actually guarantees, or where that guarantee comes from. We apply Verification Autonomy Levels (VAL) - L0: LLM self-declaration; L1: determini...

Ya-Jie Yin · 0 citations
#artificial intelligence Preprint Aug 2026

Compared to What? A Human-Anchored Security Benchmark for LLM-Generated Infrastructure-as-Code

GenIaC-SecBench is introduced, a benchmark of 100 deployment scenarios stratified by architectural complexity, evaluated across 12 model configurations from four vendors, producing 1,196 IaC artifacts scanned by three independent policy engines (Checkov, Trivy, KICS).

Animesh Shaw · 1 citation
#machine learning Preprint Oct 2026

Certified by Abstention: Distribution-Free Guarantees for Chain-of-Thought Verifiers at Small Calibration Budgets

Signals that predict whether a chain-of-thought (CoT) trace is correct are compared by AUC, but deploying one requires a threshold with a guarantee. We ask what distribution-free selective guarantees deliver for CoT verifiers at realistic calibration budgets of tens to a few hundred labelled problems, using seven open...

Arjun Balaji · 0 citations

Related blog posts

Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.