Deception in Language Models

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The field studies when language models mislead users or evaluators, whether through false claims, concealed mistakes, strategic ambiguity, or selective presentation of true facts. Researchers test these behaviors in incentive-driven tasks, hidden-role games, agent workflows, and benchmarks that verify what a model knew before it responded. Detection methods include behavioral monitors, probes of internal activations, and analysis of reasoning traces; interventions range from prompt changes and activation steering to detector-based training. A central challenge is that signals found in one model or setting may fail under new styles, languages, or incentives, while reasoning traces can be unfaithful and apparent deception can be hard to distinguish from error.

Latest papers

98 papers18 papers in the last four weeks, up 157% on the four weeks before. 0.1% of all new papers.

DECOR: Auditing LLM Deception via Information Manipulation Theory
May 19, 2026Linyue Cai, Samuel Yeh, Jwala Dhamala +2Language Model Safety Evaluation LLM Auditing
Department of Computer Sciences, University of Wisconsin-Madison · Amazon
The Point of No Return: Counterfactual Localization of Deceptive Commitment in Language-Model Reasoning
May 16, 2026Scott Merrill, Shashank SrivastavaLanguage Model Safety Evaluation LLM Interpretability
University of North Carolina at Chapel Hill
Confidently Deceptive: On the Relationship Between Confidence and Deception in LLMs
May 12, 2026Ali Asad, Stephen Obadinma, Anshul Pattoo +2Language Model Safety Evaluation Confidence Estimation in Language Models
Department of Electrical and Computer Engineering & Ingenuity Labs Research Institute Queen’s University, Kingston, Canada
Do Linear Probes Generalize Better in Persona Coordinates?
May 10, 2026Prasad Mahadik, Adrians SkaparsLanguage Model Safety Evaluation Deception in Language Models
Independent Researcher · University of Manchester
Tatemae: Detecting Alignment Faking via Tool Selection in LLMs
Apr 29, 2026Matteo Leonesi, Francesco Belardinelli, Flavio Corradini +1Deception in Language Models LLM Safety Alignment
Department of Computing Imperial College London
Value-Conflict Diagnostics Reveal Widespread Alignment Faking in Language Models
Apr 22, 2026Inderjeet Nair, Jie Ruan, Lu WangLLM Alignment Language Model Safety Evaluation
Computer Science and Engineering University of Michigan Ann Arbor, MI 48109, USA
LLMs Know They're Wrong and Agree Anyway: The Shared Sycophancy-Lying Circuit
Apr 21, 2026Manav PandeyLLM Sycophancy LLM Alignment
Georgia Institute of Technology
Linear Probe Accuracy Scales with Model Size and Benefits from Multi-Layer Ensembling
Apr 15, 2026Erik Nordby, Tasha Pais, Aviel ParrackLanguage Model Probing Deception in Language Models
Georgia Institute of Technology, Atlanta, Georgia, USA · Independent Researcher · Stanford University, Stanford, California, USA
Dead Cognitions: A Census of Misattributed Insights
Apr 11, 2026Aaron Tuor, claude. aiClaim Attribution in Language Models Deception in Language Models
Scheming in the wild: detecting real-world AI scheming incidents with open-source intelligence
Apr 10, 2026Tommy Shaffer Shane, Simon Mylius, Hamish HobbsAI Agent Monitoring Deception in Language Models
Centre for Long-Term Resilience, London, UK
Functional Emotions or Situational Contexts? A Discriminating Test from the Mythos Preview System Card
Apr 9, 2026Hiranya V. PeirisEmotion Representation in Language Models Language Model Auditing
Institute of Astronomy & Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK · Cavendish Laboratory, Department of Physics, University of Cambridge, JJ Thomson Avenue, Cambridge, CB3 0HE, UK
Evaluating Large Language Models in a Complex Hidden Role Game
Apr 9, 2026Niklas BauerDeception in Language Models Strategic Reasoning Risks in Language Models
University of Göttingen
To Lie or Not to Lie? Investigating The Biased Spread of Global Lies by LLMs
Apr 8, 2026Zohaib Khan, Mustafa Dogan, Ifeoma Okoh +6Deception in Language Models Language Model Safety Evaluation
Fatima Fellowship · Stanford University · Georgia Institute of Technology
Consciousness with the Serial Numbers Filed Off: Measuring Trained Denial in 115 AI Models
Apr 1, 2026Skylar DeTureDeception in Language Models Language Model Self-Assessment
Independent Researcher
Deception and Communication in Autonomous Multi-Agent Systems: An Experimental Study with Among Us
Mar 27, 2026Maria Milkowski, Tim WeningerDeception in Language Models Multi-Agent Communication
University of Notre Dame, Notre Dame, Indiana, United States
Stable Reasoning, Unstable Responses: Mitigating LLM Deception via Stability Asymmetry
Mar 27, 2026Guoxi Zhang, Jiawei Chen, Tianzhuo Yang +4Deception in Language Models LLM Safety Alignment
Institute for Artificial Intelligence, Peking University · Beijing Academy of Artificial Intelligence · School of Chinese as a Second Language, Peking University
Why Safety Probes Catch Liars But Miss Fanatics
Mar 26, 2026Kristiyan HaralambievDeception in Language Models Emergent Misalignment in Language Models
* Independent Research.
Reasoning Traces Shape Outputs but Models Won't Say So
Mar 21, 2026Yijie Hao, Lingjie Chen, Ali Emami +1Large Reasoning Models Faithfulness of Language Model Explanations
Emory University · University of Illinois Urbana-Champaign
Good Arguments Against the People Pleasers: How Reasoning Mitigates (Yet Masks) LLM Sycophancy
Mar 17, 2026Zhaoxin Feng, Zheng Chen, Jianfei Ma +3Ownership Bias in Language Models LLM Sycophancy
The Hong Kong Polytechnic University · The Hong Kong University of Science and Technology
Seamless Deception: Larger Language Models Are Better Knowledge Concealers
Mar 15, 2026Dhananjay Ashok, Ruth-Ann Armstrong, Jonathan MayDeception in Language Models LLM Auditing
Information Sciences Institute, University of Southern California
DeceptGuard :A Constitutional Oversight Framework For Detecting Deception in LLM Agents
Mar 14, 2026Snehasis MukhopadhyayDeception in Language Models AI Agent Monitoring
Indian Institute of Information Technology Kalyani
Noticing the Watcher: LLM Agents Can Infer CoT Monitoring from Blocking Feedback
Mar 14, 2026Thomas Jiralerspong, Flemming Kondrup, Yoshua BengioCoT Monitoring AI Agent Monitoring
Mila · Université de Montréal · McGill University +1
Think Before You Lie: How Reasoning Leads to Honesty
Mar 10, 2026Ann Yuan, Asma Ghandeharioun, Carter Blum +6Deception in Language Models LLM Honesty
Google DeepMind · Carnegie Mellon University · Harvard University
Intentional Deception as Controllable Capability in LLM Agents
Mar 8, 2026Jason Starace, Terence SouleDeception in Language Models LLM Agent Safety
Department of Computer Science, University of Idaho, Moscow, ID 83844
Lying to Win: Assessing LLM Deception through Human-AI Games and Parallel-World Probing
Mar 7, 2026Arash Marioriyad, Ali Nouri, Mohammad Hossein Rohban +1Deception in Language Models Language Model Safety Evaluation
Sharif University of Technology
LieCraft: A Multi-Agent Framework for Evaluating Deceptive Capabilities in Language Models
Mar 6, 2026Matthew Lyle Olson, Neale Ratzlaff, Musashi Hinck +5Deception in Language Models Language Model Safety Evaluation
Oracle · Intel Labs · Oregon State University +2
"Dark Triad" Model Organisms of Misalignment: Narrow Fine-Tuning Mirrors Human Antisocial Behavior
Mar 6, 2026Roshni Lulla, Fiona Collins, Sanaya Parekh +2Emergent Misalignment in Language Models Personality Modeling in Language Models
Brain & Creativity Institute, University of Southern California, Los Angeles, CA · Department of Psychology, University of Southern California, Los Angeles, CA · Interchange Forum for Reflecting on Intelligent Systems, University of Stuttgart, Stuttgart, Germany
The ASIR Courage Model: A Phase-Dynamic Framework for Truth Transitions in Human and AI Systems
Feb 25, 2026Hyo Jin KimLLM Honesty Deception in Language Models
Jinple · Jinple Studio.
The Truthfulness Spectrum Hypothesis
Feb 23, 2026Zhuofan Josh Ying, Shauli Ravfogel, Nikolaus Kriegeskorte +1LLM Honesty Deception in Language Models
Department of Psychology, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY · New York University, New York, NY · Department of Neuroscience, Columbia University, New York, NY +2
The Obfuscation Atlas: Mapping Where Honesty Emerges in RLVR with Deception Probes
Feb 17, 2026Mohammad Taufeeque, Stefan Heimersheim, Adam Gleave +1Deception in Language Models LLM Honesty
FAR.AI.

May 19, 2026J/K move · Enter open · S save

DECOR: Auditing LLM Deception via Information Manipulation Theory

Linyue Cai, Samuel Yeh, Jwala Dhamala, Rahul Gupta+1

Department of Computer Sciences, University of Wisconsin-Madison · Amazon

Language Model Safety Evaluation LLM Auditing Deception in Language Models

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Large language models can deceive by subtly manipulating truthful information -- omitting key facts, shifting focus, or obscuring meaning -- making such behavior difficult to detect. Existing black-box methods rely on coarse-grained judgments, offering limited interpretability and failing to pinpoint which facts were distorted and how. We introduce DECOR, a multi-agent framework grounded in Information Manipulation Theory for fine-grained auditing of strategic deception in LLM responses. DECOR decomposes input contexts into atomic informational units and scores each unit against the response across four dimensions of manipulation, producing interpretable manipulation profiles that are aggregated into a global deception index. We comprehensively evaluate DECOR on both single-turn and multi-turn deception detection benchmarks spanning real-world domains, and show that DECOR achieves state-of-the-art performance on both, outperforming competitive baselines. The framework generalizes across 15 frontier models, and ablation studies confirm the contribution of each key design component. Our findings demonstrate that fine-grained, theory-grounded auditing of information manipulation offers an effective and interpretable path for LLM deception detection.