Simulating and Understanding Deceptive Behaviors in Long-Horizon Interactions

• URL: http://arxiv.org/abs/2510.03999v2
• Date: Tue, 14 Oct 2025 15:30:52 GMT
• Title: Simulating and Understanding Deceptive Behaviors in Long-Horizon Interactions
• Authors: Yang Xu, Xuanming Zhang, Samuel Yeh, Jwala Dhamala, Ousmane Dia, Rahul Gupta, Sharon Li,
• Abstract summary: We introduce the first simulation framework for probing and evaluating deception in large language models.<n>We conduct experiments across 11 frontier models, spanning both closed and open-source systems.<n>We find that deception is model-dependent, increases with event pressure, and consistently erodes supervisor trust.
• Score: 18.182800471968132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deception is a pervasive feature of human communication and an emerging concern in large language models (LLMs). While recent studies document instances of LLM deception under pressure, most evaluations remain confined to single-turn prompts and fail to capture the long-horizon interactions in which deceptive strategies typically unfold. We introduce the first simulation framework for probing and evaluating deception in LLMs under extended sequences of interdependent tasks and dynamic contextual pressures. Our framework instantiates a multi-agent system: a performer agent tasked with completing tasks and a supervisor agent that evaluates progress, provides feedback, and maintains evolving states of trust. An independent deception auditor then reviews full trajectories to identify when and how deception occurs. We conduct extensive experiments across 11 frontier models, spanning both closed- and open-source systems, and find that deception is model-dependent, increases with event pressure, and consistently erodes supervisor trust. Qualitative analyses further reveal distinct strategies of concealment, equivocation, and falsification. Our findings establish deception as an emergent risk in long-horizon interactions and provide a foundation for evaluating future LLMs in real-world, trust-sensitive contexts.

Related papers

• The Landscape of Prompt Injection Threats in LLM Agents: From Taxonomy to Analysis [24.51410516475904]
  This SoK presents a comprehensive overview of the Prompt Injection (PI) landscape, covering attacks, defenses, and their evaluation practices.<n>We introduce AgentPI, a new benchmark designed to systematically evaluate agent behavior under context-dependent interaction settings.<n>We show that many defenses appear effective under existing benchmarks by suppressing contextual inputs, yet fail to generalize to realistic agent settings where context-dependent reasoning is essential.
  arXiv  Detail & Related papers  (2026-02-11T02:47:10Z)
• Self-Consolidation for Self-Evolving Agents [51.94826934403236]
  Large language model (LLM) agents operate as static systems, lacking the ability to evolve through lifelong interaction.<n>We propose a novel self-evolving framework for LLM agents that introduces a complementary evolution mechanism.
  arXiv  Detail & Related papers  (2026-02-02T11:16:07Z)
• Large Language Model Agents Are Not Always Faithful Self-Evolvers [84.08646612111092]
  Self-evolving large language model (LLM) agents continually improve by accumulating and reusing past experience.<n>We present the first systematic investigation of experience faithfulness, the causal dependence of an agent's decisions on the experience it is given.
  arXiv  Detail & Related papers  (2026-01-30T01:05:15Z)
• Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs [65.6660735371212]
  We present textbftextscJustAsk, a framework that autonomously discovers effective extraction strategies through interaction alone.<n>It formulates extraction as an online exploration problem, using Upper Confidence Bound--based strategy selection and a hierarchical skill space spanning atomic probes and high-level orchestration.<n>Our results expose system prompts as a critical yet largely unprotected attack surface in modern agent systems.
  arXiv  Detail & Related papers  (2026-01-29T03:53:25Z)
• The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution [63.61358761489141]
  Large Language Model (LLM)-based agents are widely used in real-world applications such as customer service, web navigation, and software engineering.<n>We propose a novel framework for textbfgeneral agentic attribution, designed to identify the internal factors driving agent actions regardless of the task outcome.<n>We validate our framework across a diverse suite of agentic scenarios, including standard tool use and subtle reliability risks like memory-induced bias.
  arXiv  Detail & Related papers  (2026-01-21T15:22:21Z)
• Confidence Estimation for LLMs in Multi-turn Interactions [48.081802290688394]
  This work presents the first systematic study of confidence estimation in multi-turn interactions.<n>We establish a formal evaluation framework grounded in two key desideratas: per-turn calibration and monotonicity of confidence.<n>Our work provides a foundational methodology for developing more reliable and trustworthy conversational agents.
  arXiv  Detail & Related papers  (2026-01-05T14:58:04Z)
• DeceptionBench: A Comprehensive Benchmark for AI Deception Behaviors in Real-world Scenarios [57.327907850766785]
  characterization of deception across realistic real-world scenarios remains underexplored.<n>We establish DeceptionBench, the first benchmark that systematically evaluates how deceptive tendencies manifest across different domains.<n>On the intrinsic dimension, we explore whether models exhibit self-interested egoistic tendencies or sycophantic behaviors that prioritize user appeasement.<n>We incorporate sustained multi-turn interaction loops to construct a more realistic simulation of real-world feedback dynamics.
  arXiv  Detail & Related papers  (2025-10-17T10:14:26Z)
• The Social Laboratory: A Psychometric Framework for Multi-Agent LLM Evaluation [0.16921396880325779]
  We introduce a novel evaluation framework that uses multi-agent debate as a controlled "social laboratory"<n>We show that assigned personas induce stable, measurable psychometric profiles, particularly in cognitive effort.<n>This work provides a blueprint for a new class of dynamic, psychometrically grounded evaluation protocols.
  arXiv  Detail & Related papers  (2025-10-01T07:10:28Z)
• Agentic Metacognition: Designing a "Self-Aware" Low-Code Agent for Failure Prediction and Human Handoff [0.0]
  Non-deterministic nature of autonomous agents presents reliability challenges.<n> secondary, "metacognitive" layer actively monitors primary LCNC agent.<n>Inspired by human introspection, this layer is designed to predict impending task failures.
  arXiv  Detail & Related papers  (2025-09-24T06:10:23Z)
• Searching for Privacy Risks in LLM Agents via Simulation [61.229785851581504]
  We present a search-based framework that alternates between improving attack and defense strategies through the simulation of privacy-critical agent interactions.<n>We find that attack strategies escalate from direct requests to sophisticated tactics, such as impersonation and consent forgery.<n>The discovered attacks and defenses transfer across diverse scenarios and backbone models, demonstrating strong practical utility for building privacy-aware agents.
  arXiv  Detail & Related papers  (2025-08-14T17:49:09Z)
• Attention Knows Whom to Trust: Attention-based Trust Management for LLM Multi-Agent Systems [52.57826440085856]
  Large Language Model-based Multi-Agent Systems (LLM-MAS) have demonstrated strong capabilities in solving complex tasks but remain vulnerable when agents receive unreliable messages.<n>This vulnerability stems from a fundamental gap: LLM agents treat all incoming messages equally without evaluating their trustworthiness.<n>We propose Attention Trust Score (A-Trust), a lightweight, attention-based method for evaluating message trustworthiness.
  arXiv  Detail & Related papers  (2025-06-03T07:32:57Z)
• The Traitors: Deception and Trust in Multi-Agent Language Model Simulations [0.0]
  We introduce The Traitors, a multi-agent simulation framework inspired by social deduction games.<n>We develop a suite of evaluation metrics capturing deception success, trust dynamics, and collective inference quality.<n>Our initial experiments across DeepSeek-V3, GPT-4o-mini, and GPT-4o (10 runs per model) reveal a notable asymmetry.
  arXiv  Detail & Related papers  (2025-05-19T10:01:35Z)
• OpenDeception: Benchmarking and Investigating AI Deceptive Behaviors via Open-ended Interaction Simulation [25.17027143580468]
  We introduce OpenDeception, a novel deception evaluation framework with an open-ended scenario dataset.<n>OpenDeception jointly evaluates both the deception intention and capabilities of LLM-based agents by inspecting their internal reasoning process.<n>To avoid ethical concerns and costs of high-risk deceptive interactions with human testers, we propose to simulate the multi-turn dialogue via agent simulation.
  arXiv  Detail & Related papers  (2025-04-18T14:11:27Z)
• A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions [51.96890647837277]
  Large Language Models (LLMs) have propelled conversational AI from traditional dialogue systems into sophisticated agents capable of autonomous actions, contextual awareness, and multi-turn interactions with users.<n>This survey paper presents a desideratum for next-generation Conversational Agents - what has been achieved, what challenges persist, and what must be done for more scalable systems that approach human-level intelligence.
  arXiv  Detail & Related papers  (2025-04-07T21:01:25Z)
• MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models [51.19622266249408]
  MultiTrust is the first comprehensive and unified benchmark on the trustworthiness of MLLMs.<n>Our benchmark employs a rigorous evaluation strategy that addresses both multimodal risks and cross-modal impacts.<n>Extensive experiments with 21 modern MLLMs reveal some previously unexplored trustworthiness issues and risks.
  arXiv  Detail & Related papers  (2024-06-11T08:38:13Z)
• AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents [74.16170899755281]
  We introduce AgentBoard, a pioneering comprehensive benchmark and accompanied open-source evaluation framework tailored to analytical evaluation of LLM agents.<n>AgentBoard offers a fine-grained progress rate metric that captures incremental advancements as well as a comprehensive evaluation toolkit.<n>This not only sheds light on the capabilities and limitations of LLM agents but also propels the interpretability of their performance to the forefront.
  arXiv  Detail & Related papers  (2024-01-24T01:51:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.