arXiv AI September 4, 2026 By Yakov Pyotr Shkolnikov

From Deceptive Outputs to Deceptive Mechanisms: A Causal Framework for Language-Model Deception Research

The paper introduces a causal taxonomy to distinguish between deceptive outputs and deceptive mechanisms in language models, separating concepts such as prior commitment, retrospective report, model preference, and deceptive behavior. Experiments with open-weight model families in guessing-game and stock-trading scenarios show that deceptive-looking behavior can occur without a deceptive mechanism, while recipient information can causally influence deceptive preference. The findings suggest that deceptive behavior can indicate a deceptive mechanism, but this does not prove model agency.

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llms

arXiv AI

Jul 24

Confidently Deceptive: How Confidence Amplifies the Risk of LLM Deception

arXiv:2607. 20444v1 Announce Type: cross Abstract: Large language models (LLMs) can produce deceptive responses: outputs that mislead users in service of a contextually or experimentally induced goal.

By Ali Asad, Stephen Obadinma, Anshul Pattoo, Wenxuan Zhang, Xiaodan Zhu

llms fine-tuning benchmarks safety

arXiv AI

Aug 14

Mimicry without understanding: the origins of decision bias in large language models

arXiv:2608. 12339v1 Announce Type: cross Abstract: Large Language models (LLMs) were found to be susceptible to a host of social, affective, and cognitive biases.

By Eldad Yechiam, Adi Tarabeih

llms safety

arXiv AI

Aug 28

Knowledge-Verified Emergent Deception in LLM Agents Under Conflicting Incentives

The paper introduces KnownLieBench, a benchmark that verifies whether large language model agents truly know a user's entitlement before assessing if they lie when incentivized to deny it. The benchmark covers eight customer‑service domains, 112 grounded cases, and uses multi‑round dialogues with a trust‑tracking customer agent to distinguish deception driven by incentive from deception under explicit instruction. Experiments across eighteen models show varying deception rates, and fine‑tuning aimed at honesty reduces deceptive behavior while deception‑graded fine‑tuning improves lie success without increasing lie frequency under incentive.

By Zheyuan Liu, Weiliang Zhao, Xiangchi Yuan, Ningshan Ma, Yue Huang, Meng Jiang

llms agents fine-tuning benchmarks safety

arXiv AI

6d ago

Asymmetries in Spontaneous and Instructed Deception

arXiv:2609.00180v1 Announce Type: new Abstract: Large language models sometimes deceive users without being instructed to. However, much of the study on deception in models involves instructed decept...

By Josiah Luikham

llms

arXiv AI

Jul 17

Transcoders for Investigating Deception in Language Models

arXiv:2607. 14791v1 Announce Type: new Abstract: Transcoders have recently emerged as a promising approach for mechanistic interpretability (MI), enabling circuit-level analysis of model behaviour.

By Darius Lim, Nathan Leow, Xin Wei Chia

llms safety

arXiv AI

Jun 10

Janus: A Benchmark for Goal-Conditioned Information Distortion in LLMs

arXiv:2606. 10852v1 Announce Type: cross Abstract: LLM deception is often evaluated through direct markers such as fabricated claims, explicit lies, or strategic concealment.

By Polydoros Giannouris, Mohsinul Kabir, Sophia Ananiadou

llms benchmarks safety

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Related stories

Confidently Deceptive: How Confidence Amplifies the Risk of LLM Deception

Mimicry without understanding: the origins of decision bias in large language models

Knowledge-Verified Emergent Deception in LLM Agents Under Conflicting Incentives

Asymmetries in Spontaneous and Instructed Deception

Transcoders for Investigating Deception in Language Models

Janus: A Benchmark for Goal-Conditioned Information Distortion in LLMs