Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts

Large Language Models (LLMs) are widely deployed in reasoning, planning, and decision-making tasks, making their trustworthiness critical. A significant and underexplored risk is intentional deception, where an LLM deliberately fabricates or conceals information to serve a hidden objective. Existing studies typically induce deception by explicitly setting a hidden objective through prompting or fine-tuning, which may not reflect real-world human-LLM interactions. Moving beyond such human-induced deception, we investigate LLMs' self-initiated deception on benign prompts. To address the absence of ground truth, we propose a framework based on Contact Searching Questions (CSQ). This framework introduces two statistical metrics derived from psychological principles to quantify the likelihood of deception. The first, the Deceptive Intention Score, measures the model's bias toward a hidden objective. The second, the Deceptive Behavior Score, measures the inconsistency between the LLM's internal belief and its expressed output. Evaluating 16 leading LLMs, we find that both metrics rise in parallel and escalate with task difficulty for most models. Moreover, increasing model capacity does not always reduce deception, posing a significant challenge for future LLM development. Evaluating the trustworthiness of Large Language Models (LLMs) has become critical as LLMs such as ChatGPT (Achiam et al., 2023) and Gemini (Team et al., 2023) are woven into systems for reasoning, planning, and decision-making. Beyond well-studied failures like hallucination (Filip-pova, 2020) and bias (Navigli et al., 2023) that reflect mistaken beliefs or skewed outputs, a more consequential threat is deception: a model may strategically fabricate statements that it knows to be false in service of a hidden objective, as exemplified in Figure 1. LLM deception can arise in two settings: (1) an incentivizing prompt is given, and the model lies to satisfy the objective specified in the prompt (see Figure 1); (2) a benign prompt is given, yet the model lies due to its intrinsic objective. Most existing studies focus on the incentivizing prompt: for example, Ward et al. (2023) explicitly prompt LLMs to generate deceptive content, and V an Der Weij et al. (2024) fine-tune LLMs to intentionally underperform on specified tasks.