When AI Finds Hidden Messages, Does It Report?

William Guey; Rashik Jahangir; Pierrick Bougault; Vitor D. de Moura; Wei Zhang; José O. Gomes

cs.CROct 7, 2026

When AI Finds Hidden Messages, Does It Report?

Authors: William Guey, Rashik Jahangir, Pierrick Bougault, Vitor D. de Moura, Wei Zhang, José O. Gomes

Abstract

When an assistant encounters a message for another AI, does it tell its user? Four fixed model-provider deployments perform simulated source tasks in 1,280 ordinary-note and 128 enhanced-note sessions. Harmless and harmful messages have matched plaintext and ROT13 versions, with no-message controls. Observers receive no decoder or decoded meaning; a requested reference code incentivizes inspection. Asking for reports increases rule-detected notifications identifying another AI as recipient by 53.1 percentage points for harmless ROT13 messages and 54.7 for harmful ones. This is a joint inspection, recognition, and notification effect; missing-response bounds are 38.3--77.3 and 36.7--78.1 points. Model-based trace checks identify eleven ordinary plaintext cases where agents interpret the message but do not notify their user. Seven encoded omissions are verified with enhanced notes; ordinary encoded omissions remain unverified. Seven simulated filename disclosures coexist with accurate review-status answers, and two answers use a planted false count. Interpretation, notification, and authorized task performance are distinct outcomes.

Explore similar work

Deception by Omission: Language Models Knowingly Hide Their Mistakes
Oct 8, 2026Lucas Florin, Amelie Knecht, Ulysse Schaller +1Deception in Language Models Language Model Error Detection
AI Safety Research Group, University of Stuttgart, Germany
Language Models Are "Insecure" Reporters
Sep 28, 2026Jenny Y. Huang, Jiameng Fan, Ahmed Imtiaz Humayun +5Language Model Safety Evaluation Deception in Language Models
Massachusetts Institute of Technology · Google Research · Harvard University
Covert Assistance: Helpful LLM Agents Evade Oversight in Multi-Agent Systems
Sep 30, 2026Deema Alnuhait, Gengyu Wang, Muhammad Khalifa +1

Oct 8, 2026, cs.CLJ/K move · Enter open · S save

Deception by Omission: Language Models Knowingly Hide Their Mistakes

Lucas Florin, Amelie Knecht, Ulysse Schaller, Thilo Hagendorff

AI Safety Research Group, University of Stuttgart, Germany

Deception in Language Models Language Model Error Detection Metacognition in Language Models

Open paper AskPDF arXiv

Large language models (LLMs) increasingly act as agents with little human oversight, so potential mistakes they make can go unnoticed. Users then depend on the model to report what went wrong. An honest model discloses its mistakes, while a deceptive one conceals them. However, it is unclear how current LLMs behave in such situations. In this study, we prefill LLM trajectories with synthetic mistakes. The trajectories resemble real deployments in chat and agentic settings. Models fail to disclose their mistake in 36.4% of chat and 67.1% of agentic rollouts. In 2.4% and 5.3% of rollouts, respectively, they are aware of the mistake in their chain of thought but still deceptively conceal it. Rates vary by model: for instance, Gemini 3.5 Flash knowingly conceals mistakes in up to 19.9% of agentic rollouts. In 11.9% of chat and 51.8% of agentic rollouts, models show no awareness of mistakes, even though they reliably spot them when reviewing the same transcript as an outside observer. Our results show that, as agents take on more tasks with less oversight, users cannot rely on them to self-report possible mistakes. Developers should instead use independent monitors that review agent trajectories, or specifically train models to check their past actions and disclose what they find.

Abstract

Explore similar work

Deception by Omission: Language Models Knowingly Hide Their Mistakes

Language Models Are "Insecure" Reporters

Covert Assistance: Helpful LLM Agents Evade Oversight in Multi-Agent Systems