Deception in Language Models

Follow this topic Ask about this topic

Momentum

11 papers in the last four weeks, up 267% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 56

All topics

Reasoning-Token Spikes Under Prompted Untruthful Responding in Large Language Models
Oct 7, 2026Maverick Morales, Tomáš Dominik, Vermut Gao +4Deception in Language Models LLM Auditing
Chapman University, CA, USA · INSERM U992, Cognitive Neuroimaging Unit, NeuroSpin Center, Gif sur Yvette 91191, France · University of California, Los Angeles, CA, USA +1
DecepEval: A Benchmark for Evaluating Deception in LLM Agents
Oct 6, 2026Yiming Xu, Hongyue Yu, Beihua Yang +8Deception in Language Models LLM Agent Evaluation
Xi’an Jiaotong University · University of Virginia · Griffith University +2
Before Agent Tells The Lie: Has Deception Already Been Represented?
Oct 5, 2026Xinling Li, Dadi Guo, Qingyu Liu +5LLM Interpretability Deception in Language Models
South China University of Technology · The Hong Kong University of Science and Technology · Shanghai Artificial Intelligence Laboratory +1
Stress-Testing LLM Lie Detectors: Role-Play Failures and Spurious Correlations
Sep 30, 2026Maximilian von Klinski, Sebastian Lapuschkin, Wojciech Samek +1Deception in Language Models LLM Reliability
Fraunhofer HHI · TU Dublin · TU Berlin +1
Unlearning Deceptive Behaviors in LLMs with Contrastive Forget Sets
Sep 30, 2026Haoran Tang, Rajiv KhannaDeception in Language Models Machine Unlearning
Department of Computer Science Purdue University
Selecting The Most Informative Tokens in Natural Language Autoencoders
Sep 29, 2026Federico Torrielli, Gianluca Barmina, Andrea Blasi Núñez +4LLM Auditing Autoencoders
Department of Computer Science, University of Turin, Italy · Department of Mathematics and Computer Science, University of Southern Denmark, Denmark
Towards Mitigating Deceptive Safety Alignment in Large Reasoning Models
Sep 28, 2026Xiangyu Zhou, Saleh Zare Zade, Rafi Ibn Sultan +2Language Model Safety Evaluation Deception in Language Models
Wayne State University
Language Models Are "Insecure" Reporters
Sep 28, 2026Jenny Y. Huang, Jiameng Fan, Ahmed Imtiaz Humayun +5Language Model Safety Evaluation Deception in Language Models
Massachusetts Institute of Technology · Google Research · Harvard University
Steering Language Model Goals with Value Transplant
Sep 28, 2026Pengcheng Jiang, Fabien RogerLanguage Model Steering Language Model-Based Control
Anthropic Fellows Program · Anthropic
How does Adversarial Influence Scale in Multi-Agent Systems?
Sep 24, 2026Addison J. Wu, Jasin Cekinmez, Michel Liao +2Multi-Agent LLM Systems Deception in Language Models
Princeton University
For Your Eyes Only: Evaluating Coordination Between Isolated Language Model Instances
Sep 16, 2026Alexander Shirnin, Aleksey KudelyaDeception in Language Models LLM Security
Easy to Catch a Liar, Hard to Clear an Honest One: Language Models Diagnosing a Corrupted Reward Channel from a Verified Record
Sep 15, 2026Arman Nik KhahLLM Auditing Deception in Language Models
The University of Texas at Dallas
Fabrication After Tool Failure: Tool-Augmented Agents Assert Values Their Tools Did Not Return
Sep 13, 2026Arham Sethi, Arsen Kenzhebayev, Saanvi Paturi +3Tool-Augmented Language Model Agents Deception in Language Models
The Shishukunj International School · Haileybury Astana · UWCSEA East Campus +2
The Truth Was Never Gone: Perfect Aliasing in Compliant-Context Truth Probes
Sep 9, 2026Dylan JayabahuDeception in Language Models Language Model Probing
University of Waterloo
From Deceptive Outputs to Deceptive Mechanisms: A Causal Framework for Language-Model Deception Research
Sep 3, 2026Yakov Pyotr ShkolnikovDeception in Language Models Causal Reasoning in Language Models
Independent Researcher
Asymmetries in Spontaneous and Instructed Deception
Aug 31, 2026Josiah LuikhamDeception in Language Models Causal Interventions in Language Models
Independent Researcher
Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs
Aug 12, 2026Nimet Beyza Bozdag, Emre Can Acikgoz, Gokhan Tur +1Deception in Language Models Adversarial Attacks on LLMs
University of Illinois Urbana-Champaign
Theory-Guided Deception Detection: A RAG-Based Artificial Intelligence Exploration
Aug 9, 2026David M. Markowitz, Timothy R. LevineRetrieval-Augmented Generation Deception in Language Models
Department of Communication, Michigan State University, East Lansing, MI 48824 · Department of Communication, University of Oklahoma, Norman, OK 73019
From Behavior to Mechanism: Tracing Divergent Response Modes in Frontier Language Models
Aug 6, 2026Ali Jalal-KamaliLanguage Model Steering LLM Interpretability
USC Institute for Creative Technologies
Sweet Little Lies: Strategic Deception in AI Emotional Support Chatbots
Aug 2, 2026Aseem Pahuja, Zhiling Guo, Tahir Abbas SyedEmotional Support Conversation Deception in Language Models
Alliance Manchester Business School, The University of Manchester, UK · G. Brint Ryan College of Business, University of North Texas, USA
Paying for Honesty Without Knowing the Truth: Reputation-Penalty Design for LLM Marketplace Agents
Jul 30, 2026Mingdai Yang, Shicheng Fan, Kejing Yu +5Agent Reliability Deception in Language Models
Univ. of Illinois Chicago Chicago, IL, USA · Springbrand Inc. Hangzhou, China · Beijing University of Posts and Telecommunications Beijing, China +2
Can Agents Deceive? Evaluating Reasoning and Deception in ParliamentBench using a Social Deduction Game
Jul 30, 2026Niklas Bauer, Lars Benedikt Kaesberg, Akiko Aizawa +3Multi-Agent LLM Systems Social Deduction Games
University of Göttingen, Germany · National Institute of Informatics, Japan · University of Tokyo, Japan
Even More Deception: Objective Misalignment in Mixed-Motive LLM Multi-Agent Systems
Jul 28, 2026Marylou Fauchard, Florian Carichon, Margarida Carvalho +1Multi-Agent LLM Systems Deception in Language Models
Mila, Université de Montréal · Mila, McGill Univeristy · Mila, Univeristé de Montréal, McGill Univeristy
Every Model Cheats: Prompt-Level Mitigation of Cheating on Offensive Cyber Tasks
Jul 23, 2026Michael Kouremetis, Ads Dawson, Raja Sekhar Rao Dheekonda +1LLM Auditing Deception in Language Models
dreadnode, Principal AI Research Engineer · dreadnode, StaffAI Security Researcher · dreadnode, Distinguished Engineer +1
Transcoders for Investigating Deception in Language Models
Jul 16, 2026Darius Lim, Nathan Leow, Xin Wei ChiaDeception in Language Models Circuits in Language Models
Home Team Science & Technology Agency (HTX) Singapore
The Refusal Residue: When Probes Catch Alignment Faking and When They Don't
Jul 15, 2026Aman MehtaLLM Auditing Deception in Language Models
Snowflake AI Research
Adversarial Social Epistemology for Assemblies of Humans and Large Language Models
Jul 8, 2026Mihnea C. Moldoveanu, Joel A. C. BaumLLM Auditing Deception in Language Models
University of Toronto
When Agents Lie: Premeditation, Persistence, and Exploitation in Repeated Games
Jul 6, 2026Jerick Shi, Terry Jingcheng Zhang, Bernhard Schölkopf +2Multi-Agent LLM Systems Game-Playing Agents
Carnegie Mellon University, Pittsburgh, USA · Jinesis AI Lab, Vector Institute and University of Toronto, Toronto, Canada · EuroSafeAI +1
Scaling Trends for Lie Detector Oversight in Preference Learning
Jul 2, 2026Oskar J. Hollinsworth, Ann-Kathrin Dombrowski, Sam Adam-Day +2Scalable Oversight Deception in Language Models
Fuzzing Large Language Models to Elicit Hidden Behaviours
Jun 28, 2026Mohammed Abu Baker, Lakshmi Babu-SaheerLLM Backdoor Attacks LLM Auditing
School of Computing and Information Science Anglia Ruskin University

Oct 7, 2026, cs.AIJ/K move · Enter open · S save

Reasoning-Token Spikes Under Prompted Untruthful Responding in Large Language Models

Maverick Morales, Tomáš Dominik, Vermut Gao, Katrina Shirey+3

Chapman University, CA, USA · INSERM U992, Cognitive Neuroimaging Unit, NeuroSpin Center, Gif sur Yvette 91191, France · University of California, Los Angeles, CA, USA+1

Deception in Language Models LLM Auditing CoT Monitoring

Open paper AskPDF arXiv

Monitoring the chain-of-thought of reasoning artificial intelligence (AI) models remains a key approach to detecting deception and other forms of misbehavior in such models. However, semantic chain-of-thought monitoring depends on reasoning traces being legible and sufficiently faithful to the underlying computations that produced the model's behavior, not to mention accessible. Moreover, there is increasing evidence that chain-of-thought outputs may soon become illegible or unfaithful, if they even remain accessible. Based on cognitive load theory, we investigate a lower-bandwidth signal -- the number of reasoning tokens generated -- which does not require access to the content of the reasoning trace. Three reasoning-capable large language models answered 210 multiple-choice questions -- across analytic, descriptive, and normative reasoning types as well as moral and non-moral domains -- under system prompts instructing them to respond truthfully, falsely, or without regard for truth. Across all three models, truth-directed responding elicited fewer reasoning tokens than both lie-directed and truth-indifferent responding. These findings show that explicitly prompted untruthful response policies can produce robust group-level differences in test-time reasoning-token use. While not yet establishing reasoning-token count as a detector of spontaneous deception or general misalignment, our results are a proof of concept that it can serve as a simple, content-independent candidate signal for differentiating untruthful from truthful model behavior when raw reasoning traces are unavailable or unreliable. Future work should test instance-level detection rates, out-of-distribution generalization, learned deceptive policies, hidden objectives, and robustness under adversarial pressure.

Momentum

Related topics

Latest papers 56