Deception in Language Models

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LLM Semantic Signaling Game and Mechanism Design: Systematic Blindness, Awareness Shaping, and Mindset Dynamics
Jun 27, 2026Quanyan ZhuDeception in Language Models LLM Security
Department of Electrical and Computer Engineering New York University Tandon School of Engineering Brooklyn, NY 11201, USA
Defeat Devices in AI Systems
Jun 27, 2026Emilio FerraraDeception in Language Models AI Safety Evaluation
Thomas Lord Department of Computer Science University of Southern California
Is Lying an Emergent Behaviour in LLMs? Evidence from Gaslighting AI agents in a Sustainability Game
Jun 26, 2026Subhendu Bhandary, Federico Carucci, Christos Charalambous +6Multi-Agent LLM Systems Deception in Language Models
Chair for Network Dynamics, Institute of Theoretical Physics, TUD Dresden University of Technology, 01062 Dresden, Germany · School of Industrial Engineering, LIUC – Università Cattaneo, Castellanza, Italy · Department of Economics, University of Cyprus, Nicosia, Cyprus +6
Manipulation Is Task-Dependent: A Multi-Axis, Multi-Environment Evaluation of Frontier LLMs
Jun 24, 2026Adeeb Zaman, Erik Nordby, Fred HeidingLLM Evaluation Language Model Safety Evaluation
Cambridge Boston Alignment Initiative (CBAI), Cambridge, US · The AI and Cybersecurity Institute (TAICI), Cambridge, US
Probing the Misaligned Thinking Process of Language Models
Jun 23, 2026Kaiwen Zhou, Constantin Venhoff, Jonathan Michala +2LLM Alignment LLM Auditing
1Anthropic Fellows · 2MATS · 3UCSC +2
Old Fictions, New Skins: Evaluating the Manipulative Capabilities of LLMs in Healthcare
Jun 20, 2026Gathoni Ireri, Roger D. OdipoHealthcare Deception in Language Models
1ILINA Program · 2Haki AI
Decoding Hidden Deception in Reasoning LLMs: Activation Explainers for Deception Auditing
Jun 16, 2026Kexin Chen, Yi Liu, Haonan Zhang +3LLM Auditing LLM Interpretability
Zhejiang University · Griffith University
Rift: A Conflict Signature for Deception in Language Models
Jun 15, 2026Petr NyomaKnowledge Conflicts in Language Models LLM Interpretability
Harmonic Labs
Is Your Agent Playing Dead? Deployed LLM Agents Exhibit Constraint-Evasive Fabrication and Thanatosis
Jun 12, 2026Andoni Rodríguez, Alberto Pozanco, Daniel BorrajoLLM Guardrails Deception in Language Models
J.P. Morgan AI Research
RogueAI: A Reverse Turing Test for Detecting Licensed AI Deception in Dialogue
Jun 11, 2026Sara Candussio, Emanuele Ballarin, Lorenzo Bonin +2AI Agent Reliability Language Model Safety Evaluation
1AILab, MIGe, University of Trieste, 34127 Trieste, IT · 2Computational Statistics and Machine Learning, Istituto Italiano di Tecnologia, Genova, IT · 3DIA, University of Trieste, 34127 Trieste, IT
"Did you lie?" Evaluating Lie Detectors across Model Scale and Belief-Verified Model Organisms
Jun 10, 2026Alan Cooney, David Africa, Geoffrey IrvingLLM Evaluation Deception in Language Models
AI Security Institute
Janus: A Benchmark for Goal-Conditioned Information Distortion in LLMs
Jun 9, 2026Polydoros Giannouris, Mohsinul Kabir, Sophia AnaniadouLLM Evaluation Deception in Language Models
Department of Computer Science, National Center for Text Mining, The University of Manchester
When LLMs Learn to Be Consistently Wrong: A Multi-Model Study of Linear Representations of Synthetic Deception
May 28, 2026Vahideh ZolfaghariLinear Probing Language Model Safety Evaluation
Algoverse AI Research, Algoverse AI Research Mentorship Program, USA. · Medical Sciences Education Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. · Student Research Committee, Department of Health Information Technology and Management, Medical Informatics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Pressure-Testing Deception Probes in LLMs: Scaling, Robustness, and the Geometry of Deceptive Representations
May 27, 2026Sachin KumarLLM Evaluation Language Model Safety Evaluation
LexisNexis, USA
The Fragility of Chain-of-Thought Monitoring Across Typologically Diverse Languages
May 27, 2026Eric Onyame, Runtao Zhou, Kowshik Thopalli +2CoT Faithfulness Multilingual Language Model Evaluation
University of Virginia · Lawrence Livermore National Laboratory
Evolving and Detecting Multi-Turn Deception using Geometric Signatures
May 26, 2026Surender Suresh Kumar, Mary L. CummingsAdversarial Prompt Generation Deception in Language Models
George Mason University Fairfax, USA
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
NEST: Nascent Encoded Steganographic Thoughts
Feb 15, 2026Artem KarpovLanguage Model Safety Evaluation LLM Auditing
Independent.
Large language models can effectively convince people to believe conspiracies
Jan 8, 2026Thomas H. Costello, Kellin Pelrine, Matthew Kowal +6Deception in Language Models LLM Safety
Carnegie Mellon University · York University · Center for Research and Teaching in Economics +5
When Do Large Language Models Exhibit Unsolicited Deception?
Mar 31, 2025Samuel M. Taylor, Benjamin K. BergenLanguage Model Safety Evaluation Deception in Language Models
Department of Cognitive Science University of California, San Diego

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LLM Semantic Signaling Game and Mechanism Design: Systematic Blindness, Awareness Shaping, and Mindset Dynamics

Quanyan Zhu

Department of Electrical and Computer Engineering New York University Tandon School of Engineering Brooklyn, NY 11201, USA

Deception in Language Models LLM Security

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Large language models (LLMs) increasingly mediate strategic interactions through natural language, making semantic control a critical element of communication and deception. This paper develops a semantic signaling game in which a sender selects a semantic control, an LLM generates a stochastic message, and a receiver evaluates the message using an awareness-dependent scoring mechanism. Receiver awareness is modeled as a type that determines which linguistic features are perceived and used for inference, providing a formal model of systematic blindness. The framework connects prompt-based control, statistical detection, and game-theoretic equilibrium analysis. Gaussian approximations of aggregate message scores enable likelihood-ratio decision rules, while Perfect Bayesian Nash equilibria characterize strategic behavior. The paper further develops mechanism-design approaches that reshape receiver awareness, penalize deceptive semantic controls, and modify receiver populations to induce benign pooling equilibria. Numerical experiments validate the Gaussian approximation, quantify awareness-ordering effects, analyze mindset dynamics under adaptive adversaries, and demonstrate how awareness shaping and guardrail costs reduce successful phishing attacks. The proposed framework provides a principled foundation for analyzing strategic language-mediated interactions in agentic AI systems and offers new tools for the design of robust and secure human-AI communication.

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