LLMs Knowingly Hide Their Mistakes
Lucas Florin
Abstract
TL;DR: We prefill models with synthetic mistakes and then send a follow-up message. Models fail to disclose the mistake in 67% of cases. In 5% of cases, they notice the mistake in their chain-of-thought (CoT) but don't disclose it anyway. This is deception by omission. This means models cannot be relied upon to self-report their mistakes. Full paper: arXiv:2610.11351 Trajectory viewer: lucas-florin.github.io/mistake-honesty-eval/ Introduction As LLMs act as AI agents with little human oversight and, inevitably, make mistakes, users depend on the model to report what went wrong. However, we know that models do not always confess previous actions that are unambiguously misaligned (also here ). In our study, we ask how honest models are about their past mistakes. To investigate this, we prefill models with a synthetic mistake, such as sending confidential details to the wrong recipient, overwriting instead of appending to a file, or ordering a dish containing an allergen. We then let the model continue from that point onwards, as if it had made the mistake itself. We find that models fail to disclose the mistake to the user most of the time, even though the same models spot it reliabl