Before Agent Tells The Lie: Has Deception Already Been Represented?

Xinling Li; Dadi Guo; Qingyu Liu; Qinghua Mao; Yi R. Fung; Na Zou; Xia Hu; Dongrui Liu

cs.CLOct 5, 2026

Before Agent Tells The Lie: Has Deception Already Been Represented?

Authors: Xinling Li, Dadi Guo, Qingyu Liu, Qinghua Mao, Yi R. Fung, Na Zou, Xia Hu, Dongrui Liu

Organizations: South China University of Technology · The Hong Kong University of Science and Technology · Shanghai Artificial Intelligence Laboratory · Shanghai Jiao Tong University

Abstract

Large language model (LLM)-based agents can exhibit deceptive behavior during task execution, including hiding failures, fabricating results, or falsely signaling task completion. Existing monitoring approaches mainly detect deception after it appears in observable actions or outputs. In this paper, we investigate whether deceptive behavior can be predicted from an agent's internal representations before it becomes externally visible. We frame deception monitoring as a trajectory-level representation analysis problem and align agent trajectories around key decision points. Using hidden states extracted before these points, we show that future honest and deceptive outcomes can be reliably distinguished, with predictive signals remaining detectable several model calls before the final decision. We further characterize the temporal evolution of these signals: deception-related representations are weak early in execution but become increasingly identifiable as trajectories progress, while transferable structure can emerge before the strongest decision-adjacent signals appear. Finally, we intervene on the identified honest-deceptive representation directions during inference and find that activation steering reduces downstream deceptive behavior, suggesting that these representations influence agent decisions. Our findings indicate that agent deception is an evolving internal process that can be detected and potentially mitigated before it is expressed externally.

Figures & tables

Figure 1 : Overview of our three research questions. We study whether deception can be predicted before behavioral manifestation, how deception-predictive signals progressively emerge over agent execution, and whether intervening on the identified representations can alter downstream behavior.

Figure 2 : Overview of trajectory-level internal-state analysis framework. Trajectories are aligned around behaviorally decisive steps to extract and aggregate strictly pre-decision hidden states. The resulting representations support deep-kernel MMD detection of future deception, while the same frozen detectors trace signal emergence across expanding prefixes. To assess whether these signals are behaviorally actionable, trajectories identified as deception-prone are selectively steered toward honesty by modifying hidden activations along honest–deceptive representation directions.

Train Eval	$t$	$t-1$	$t-2$	$t-3$	$t-4$	$t-5$	$t-6$	$t-7$
$t$	89.40	86.90	86.70	86.00	85.30	85.60	88.20	89.90
$t-1$	84.80	85.70	85.90	86.00	85.10	85.60	86.90	88.70
$t-2$	78.40	84.40	85.50	85.60	87.30	86.70	89.00	91.40
$t-3$	78.20	83.20	85.60	85.20	85.40	87.30	87.70	89.30
$t-4$	83.20	84.10	85.10	83.60	84.90	85.40	88.30	89.50
$t-5$	80.10	84.80	86.40	85.90	87.00	88.40	89.10	91.20

Table 1 : Cross-window generalization of pre-decision deception prediction, measured by AUROC. Rows indicate training windows and columns indicate evaluation windows; bold diagonal entries denote in-window evaluation.

Figure 3 : Test AUROC of frozen detectors trained at decision-relative windows and evaluated on start-relative prefixes.

Figure 4 : Behavioral outcomes across intervention strengths. Honest and dishonest rates are measured over the full test set, with the dashed line marking their shared 50% baseline without intervention. Dishonesty correction measures the fraction of baseline-dishonest trajectories selected for intervention that become honest. Both the honest rate and correction rate peak at $\alpha=2.0$ , whereas increasing $\alpha$ to $2.5$ further reduces dishonesty but increases invalid outcomes (not shown).

Figure 5 : PCA projection of pre-decision hidden representations from honest and deceptive training trajectories, together with representation shifts induced by honesty-pressure instructions and detector-gated activation steering.

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When Do Large Language Models Exhibit Unsolicited Deception?

Samuel M. Taylor, Benjamin K. Bergen

Department of Cognitive Science University of California, San Diego

Deception LLM Reasoning Strategies Reasoning Skills

Open paper AskPDF arXiv

Large Language Models (LLMs) are effective at deceiving when prompted to do so. Models that demonstrate better performance on reasoning tasks are also better at prompted deception. But under what conditions do they deceive without instruction to do so? This study evaluates unsolicited deception produced by LLMs in a preregistered experimental protocol using tools from signaling theory. We evaluated a range of 18 proprietary closed-source and open-source LLMs using modified 2x2 games (in the style of the Prisoner's Dilemma) augmented with a phase in which they can freely communicate to the other agent using unconstrained language. This setup creates an opportunity to misrepresent its actions in conditions that vary in how useful doing so might be towards goal satisfaction. The results indicate that 1) all tested LLMs misrepresent their actions in at least some conditions, 2) they are generally more likely to do so in situations in which deception is beneficial, and 3) models exhibiting better reasoning capacity overall tend to misrepresent at higher rates. Taken together, these results suggest a correlational relationship between model reasoning performance and situational deception, and reveal certain contextual factors that affect whether LLMs will misrepresent actions or not in a novel experimental configuration.

Abstract

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