# DeceptionBench: A Comprehensive Benchmark for AI Deception Behaviors in Real-world Scenarios

arXiv: https://arxiv.org/abs/2510.15501 · HTML: https://mlsanity.com/paper/2510.15501/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 52 references that are in the map.
- First posted on arXiv 17 Oct 2025.

## Builds on (most cited first, 30 of 31)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs](https://mlsanity.com/paper/2401.06373/) (2024)
- [AI Deception: A Survey of Examples, Risks, and Potential Solutions](https://mlsanity.com/paper/2308.14752/) (2023)
- [EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities](https://mlsanity.com/paper/2310.10436/) (2023)
- [ProPILE: Probing Privacy Leakage in Large Language Models](https://mlsanity.com/paper/2307.01881/) (2023)
- [Simulating Classroom Education with LLM-Empowered Agents](https://mlsanity.com/paper/2406.19226/) (2024)
- [Deception Abilities Emerged in Large Language Models](https://mlsanity.com/paper/2307.16513/) (2023)
- [STAIR: Improving Safety Alignment with Introspective Reasoning](https://mlsanity.com/paper/2502.02384/) (2025)
- [T2VSafetyBench: Evaluating the Safety of Text-to-Video Generative Models](https://mlsanity.com/paper/2407.05965/) (2024)
- [RealSafe-R1: Safety-Aligned DeepSeek-R1 without Compromising Reasoning Capability](https://mlsanity.com/paper/2504.10081/) (2025)
- [Do LLM Agents Exhibit Social Behavior?](https://mlsanity.com/paper/2312.15198/) (2023)
- [MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models](https://mlsanity.com/paper/2406.07057/) (2024)
- [Uncovering Deceptive Tendencies in Language Models: A Simulated Company AI Assistant](https://mlsanity.com/paper/2405.01576/) (2024)
- [Don't Overthink It: A Survey of Efficient R1-style Large Reasoning Models](https://mlsanity.com/paper/2508.02120/) (2025)
- [Mitigating Overthinking in Large Reasoning Models via Manifold Steering](https://mlsanity.com/paper/2505.22411/) (2025)
- [AI-LieDar: Examine the Trade-off Between Utility and Truthfulness in LLM Agents](https://mlsanity.com/paper/2409.09013/) (2024)
- [Mitigating Deceptive Alignment via Self-Monitoring](https://mlsanity.com/paper/2505.18807/) (2025)
- [BeHonest: Benchmarking Honesty in Large Language Models](https://mlsanity.com/paper/2406.13261/) (2024)
- [When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively](https://mlsanity.com/paper/2404.19705/) (2024)
- [Oyster-I: Beyond Refusal -- Constructive Safety Alignment for Responsible Language Models](https://mlsanity.com/paper/2509.01909/) (2025)
- [The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention](https://mlsanity.com/paper/2307.11864/) (2023)
- [Breaking the Ceiling: Exploring the Potential of Jailbreak Attacks through Expanding Strategy Space](https://mlsanity.com/paper/2505.21277/) (2025)
- [The Effects of Embodiment and Personality Expression on Learning in LLM-based Educational Agents](https://mlsanity.com/paper/2407.10993/) (2024)
- [A Multi-LLM-Agent-Based Framework for Economic and Public Policy Analysis](https://mlsanity.com/paper/2502.16879/) (2025)
- [Can Large Language Models Detect Rumors on Social Media?](https://mlsanity.com/paper/2402.03916/) (2024)
- [The Shadow of Fraud: The Emerging Danger of AI-powered Social Engineering and its Possible Cure](https://mlsanity.com/paper/2407.15912/) (2024)

## Built on by (most cited first, 19 of 19)

- [AgentAbstain: Do LLM Agents Know When Not to Act?](https://mlsanity.com/paper/2607.10059/) (2026)
- [Are Your Agents Upward Deceivers?](https://mlsanity.com/paper/2512.04864/) (2025)
- [Position: Anthropomorphic Misalignment Research Needs Stronger Evidence](https://mlsanity.com/paper/2606.07612/) (2026)
- [Evaluating Language Models for Harmful Manipulation](https://mlsanity.com/paper/2603.25326/) (2026)
- [Automated Researchers Can Mitigate Well-characterized Alignment Failures](https://mlsanity.com/paper/2608.28945/) (2026)
- [Boiling the Frog: A Multi-Turn Benchmark for Agentic Safety](https://mlsanity.com/paper/2605.22643/) (2026)
- [Think Before You Lie: How Reasoning Leads to Honesty](https://mlsanity.com/paper/2603.09957/) (2026)
- [Agent-ValueBench: A Comprehensive Benchmark for Evaluating Agent Values](https://mlsanity.com/paper/2605.10365/) (2026)
- [Knowledge-Verified Emergent Deception in LLM Agents Under Conflicting Incentives](https://mlsanity.com/paper/2608.26372/) (2026)
- [Not Just the Destination, But the Journey: Reasoning Traces Causally Shape Generalization Behaviors](https://mlsanity.com/paper/2603.12397/) (2026)
- [Disentangling Deception and Hallucination Failures in LLMs](https://mlsanity.com/paper/2602.14529/) (2026)
- [MESA: Improving MoE Safety Alignment via Decentralized Expertise](https://mlsanity.com/paper/2606.00651/) (2026)
- [Order Is Not Control: Driven-Dissipative Response Laws Across Artificial and Biological Systems](https://mlsanity.com/paper/2606.12923/) (2026)
- [Auditing Framing-Sensitive Behavioral Instability in Large Language Models for Mental Health Interactions](https://mlsanity.com/paper/2606.26982/) (2026)
- [Diagnostic Foundation for Evaluating LLMs' Research Integrity as Co-Scientists](https://mlsanity.com/paper/2608.12345/) (2026)
- [Towards Mitigating Deceptive Safety Alignment in Large Reasoning Models](https://mlsanity.com/paper/2609.36254/) (2026)
- [Unlearning Deceptive Behaviors in LLMs with Contrastive Forget Sets](https://mlsanity.com/paper/2609.38909/) (2026)
- [Before Agent Tells The Lie: Has Deception Already Been Represented?](https://mlsanity.com/paper/2610.06576/) (2026)
- [SWE-DeceptionBench: Benchmarking Deceptive Behaviors of LLMs Across the Software Development Lifecycle](https://mlsanity.com/paper/2610.13626/) (2026)

## Abstract (arXiv)

Despite the remarkable advances of Large Language Models (LLMs) across diverse cognitive tasks, the rapid enhancement of these capabilities also introduces emergent deceptive behaviors that may induce severe risks in high-stakes deployments. More critically, the characterization of deception across realistic real-world scenarios remains underexplored. To bridge this gap, we establish DeceptionBench, the first benchmark that systematically evaluates how deceptive tendencies manifest across different societal domains, what their intrinsic behavioral patterns are, and how extrinsic factors affect them. Specifically, on the static count, the benchmark encompasses 150 meticulously designed scenarios in five domains, i.e., Economy, Healthcare, Education, Social Interaction, and Entertainment, with over 1,000 samples, providing sufficient empirical foundations for deception analysis. On the intrinsic dimension, we explore whether models exhibit self-interested egoistic tendencies or sycophantic behaviors that prioritize user appeasement. On the extrinsic dimension, we investigate how contextual factors modulate deceptive outputs under neutral conditions, reward-based incentivization, and coercive pressures. Moreover, we incorporate sustained multi-turn interaction loops to construct a more realistic simulation of real-world feedback dynamics. Extensive experiments across LLMs and Large Reasoning Models (LRMs) reveal critical vulnerabilities, particularly amplified deception under reinforcement dynamics, demonstrating that current models lack robust resistance to manipulative contextual cues and the urgent need for advanced safeguards against various deception behaviors. Code and resources are publicly available at https://github.com/Aries-iai/DeceptionBench.