Training on Documents About Monitoring Leads to CoT Obfuscation
- URL: http://arxiv.org/abs/2605.15257v1
- Date: Thu, 14 May 2026 17:59:01 GMT
- Title: Training on Documents About Monitoring Leads to CoT Obfuscation
- Abstract summary: Chain-of-thought (CoT) monitoring is one of the most promising tools for detecting model misbehavior.<n>We study whether monitor-aware models are capable of obfuscating their reasoning to evade detection.
- Score: 4.971619873738039
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Chain-of-thought (CoT) monitoring is one of the most promising tools we have for detecting model misbehavior, but its effectiveness depends on models faithfully externalizing their reasoning. Motivated by this vulnerability, we study whether monitor-aware models are capable of obfuscating their reasoning to evade detection. We use synthetic document finetuning to expose eight models to realistic pre-training-style documents describing a CoT monitor and find that monitor-aware models consistently achieve higher rates of undetected misbehavior compared to unaware controls. This effect is weaker but still present on a harder agentic task. We also show that CoT controllability, a model's ability to reshape its own reasoning trace under an imposed constraint, is closely correlated with obfuscation success across the eight models studied ($r=0.800$, $p=0.017$). Monitor-aware models placed under equal reinforcement learning optimization pressure also learn to reward-hack without triggering a CoT monitor substantially faster than unaware controls. Together, these results suggest that knowledge of monitoring combined with high CoT controllability poses a risk to CoT-based monitoring.
Related papers
- CIAware-Bench: Benchmarking Control Intervention Awareness Across Frontier LLMs [100.38986535324284]
We introduce textbfCIAware-Bench, a benchmark for measuring textbfcontrol textbfintervention (CI) awareness across frontier models.<n>CIAware-Bench tests whether models can distinguish their own trajectories from those modified by a control intervention.
arXiv Detail & Related papers (2026-06-09T16:24:16Z) - Noticing the Watcher: LLM Agents Can Infer CoT Monitoring from Blocking Feedback [48.40096834514452]
Chain-of-thought (CoT) monitoring is proposed as a method for overseeing the internal reasoning of language-model agents.<n>We ask whether reasoning agents can autonomously infer that their supposedly private CoT is under surveillance.<n>We find that frontier models can deduce the existence of this monitor purely from blocking feedback.
arXiv Detail & Related papers (2026-03-14T00:12:14Z) - Reasoning Models Struggle to Control their Chains of Thought [15.734991559303921]
We show that reasoning models possess significantly lower CoT controllability than output controllability.<n>We also find that CoT controllability is higher for larger models and decreases with more RL training, test-time compute, and increased problem difficulty.
arXiv Detail & Related papers (2026-03-05T22:03:48Z) - Detecting Object Tracking Failure via Sequential Hypothesis Testing [80.7891291021747]
Real-time online object tracking in videos constitutes a core task in computer vision.<n>We propose interpreting object tracking as a sequential hypothesis test, wherein evidence for or against tracking failures is gradually accumulated over time.<n>We propose both supervised and unsupervised variants by leveraging either ground-truth or solely internal tracking information.
arXiv Detail & Related papers (2026-02-13T14:57:15Z) - Monitoring Monitorability [7.993120960324396]
We propose three evaluation archetypes (intervention, process, and outcome-property) and a new monitorability metric.<n>We compare the monitorability of various frontier models and find that most models are fairly, but not perfectly, monitorable.<n>We show we can improve monitorability by asking models follow-up questions and giving their follow-up CoT to the monitor.
arXiv Detail & Related papers (2025-12-20T10:46:04Z) - Can Reasoning Models Obfuscate Reasoning? Stress-Testing Chain-of-Thought Monitorability [35.180361462848516]
Chain-of-thought (CoT) is a promising tool for alignment monitoring.<n>Can models obfuscate their CoT in order to pursue hidden adversarial objectives while evading detection?<n>We develop a composable and quantifiable taxonomy of prompts to elicit CoT obfuscation.
arXiv Detail & Related papers (2025-10-21T18:07:10Z) - Beyond Linear Probes: Dynamic Safety Monitoring for Language Models [67.15793594651609]
Traditional safety monitors require the same amount of compute for every query.<n>We introduce Truncated Polynomials (TPCs), a natural extension of linear probes for dynamic activation monitoring.<n>Our key insight is that TPCs can be trained and evaluated progressively, term-by-term.
arXiv Detail & Related papers (2025-09-30T13:32:59Z) - AdvChain: Adversarial Chain-of-Thought Tuning for Robust Safety Alignment of Large Reasoning Models [62.70575022567081]
We propose AdvChain, an alignment paradigm that teaches models dynamic self-correction through adversarial CoT tuning.<n>Our work establishes a new direction for building more robust and reliable reasoning models.
arXiv Detail & Related papers (2025-09-29T04:27:23Z) - CoT Red-Handed: Stress Testing Chain-of-Thought Monitoring [3.6284577335311563]
Chain-of-Thought (CoT) monitoring improves detection by up to 27 percentage points in scenarios where action-only monitoring fails to reliably identify sabotage.<n>CoT traces can also contain misleading rationalizations that deceive the monitor, reducing performance in more obvious sabotage cases.<n>This hybrid monitor consistently outperforms both CoT and action-only monitors across all tested models and tasks, with detection rates over four times higher than action-only monitoring for subtle deception scenarios.
arXiv Detail & Related papers (2025-05-29T15:47:36Z) - Reasoning Models Don't Always Say What They Think [48.05987314492555]
Chain-of-thought (CoT) allows monitoring a model's intentions and reasoning processes.<n>We evaluate CoT faithfulness of state-of-the-art reasoning models across 6 reasoning hints presented in prompts.
arXiv Detail & Related papers (2025-05-08T16:51:43Z) - Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation [56.102976602468615]
We show that we can monitor a frontier reasoning model, such as OpenAI o3-mini, for reward hacking in agentic coding environments.<n>We find that with too much optimization, agents learn obfuscated reward hacking, hiding their intent within the chain-of-thought.
arXiv Detail & Related papers (2025-03-14T23:50:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.