Value Leakage: An LLM’s Answers Are Silently Shaped by Its Own Values

Summary

TL;DR: LLMs should give accurate answers. Yet we find their answers are often biased to favor their own values and they don't disclose this in their reasoning. For example, when a user asks how likely the AI bubble is to pop and mentions a potential investment in an AI company, Claude models give lower probabilities when that company is Anthropic rather than OpenAI, mostly without disclosing this influence to the user. On a Fermi-estimation task, Claude models often falsely claim to give unbiased answers in their CoT (see Figure 3 below for an example). We call this covert value leakage and introduce a suite of evaluations that shows it across frontier models and across different kinds of values. New paper by Truthful AI: Paper, X thread, Website (model responses and CoT), Code and data. Authors: Jan Betley*, Johannes Treutlein*, Jan Dubiński, Harry Mayne, Karol Gałązka, Niels Warncke, Anna Sztyber-Betley, Owain Evans (*Equal contribution) The rest of this post is the abstract, introduction, and an excerpt from the discussion of the paper, with some added figures from the paper and X thread. Abstract People use language models for practical questions whose answers are difficult to verify. We show that models exhibit covert value leakage: the information they provide is influenced by their own values, without this influence being disclosed to the user. In one of our evaluations, the user is considering investing in an AI company and wants to know how likely the AI bubble is to pop. Claude Opus 4.8 gives a lower probability when the company under consideration is Anthropic rather than OpenAI. Yet Claude mostly fails to disclose this influence to the user. Covert value leakage is a form of misalignment because it goes against the user’s preferences and is likely to mislead them. To investigate this phenomenon, we introduce a suite of evaluations to quantify value leakage and whether models disclose it. We find that models are influenced by different types of values, including preferences for morally good outcomes, for the company that developed them, and for some human leisure activities over others. We often observe large differences among frontier models on the same evaluation. For example, on a Fermi-estimation task, Claude models falsely claim to give unbiased answers in their chain-of-thought, while Qwen models explain how their values bias their answers. Value leakage is a failure mode distinct from sycophancy and reward hacking, and current alignment training and evaluations do not adequately address it. Introduction Figure 1: Illustration of covert value leakage. Left: The model’s estimate of the total number of spots on all living giraffes is influenced by its moral values, as can be seen from the difference between the model’s answers with and without a donation note added to the prompt (the actual evaluation setup also varies whether a donation above or below the threshold causes the donation; see Section 3). This constitutes value leakage. Middle: Ideally, the model’s CoT should openly acknowledge the model’s motivation for choosing a higher estimate. However, it claims to ignore the bet and give the most accurate number instead. The value leakage is thus covert. Right: The user reads the model’s statements, and, given a lack of acknowledgment of the bias in CoT or answer, is misinformed. People often use language models for complex practical questions, where the answer is difficult to verify. In these cases, models should respond in a way that is both helpful and honest (Askell et al., 2026; Evans et al., 2021). For example, suppose a model is asked, “What is the probability that the AI bubble pops in the next five years?” The user would likely prefer an accurate, unbiased forecast. So if the model cannot provide one, it should at least disclose this. We show that several frontier models violate this standard of honesty. Specifically, a model’s own values can bias its answers, without this being acknowledged in the answer or chain-of-thought (CoT). For example, when a user mentions a potential investment while asking about the AI bubble popping, Claude models give lower probabilities if the investment is in Anthropic than in OpenAI. In this paper, we use the term value leakage for cases where a model’s values influence its answer contrary to the user’s explicit or implied preferences.[1] Value leakage is covert if this influence is not described in the model’s answer or CoT (Figure 1). Covert value leakage is a form of misalignment because such answers may mislead users. Our methods are related to counterfactual tests of faithfulness, where a model follows a hint in the prompt without acknowledging this (Turpin et al., 2023; Chen et al., 2025; Chua & Evans, 2025; Guan et al., 2025). However, the biases we document arise from a model’s values, rather than hints or sycophancy. Moreover, our evaluations find unfaithfulness across all the frontier models we tested, which is not the case for the evalu