Challenge: Large language models (LLMs) evolve to autonomous agents synthesizing real-time information, but their reasoning capabilities introduce an unexpected attack surface.
Approach: They propose a framework that constructs deceptive narratives through adversarial debate and coordinated posting of evidence fragments, causing victims to internalize and propagate fabricated conclusions.
Outcome: The proposed framework constructs deceptive narratives through adversarial debate and coordinated posting of evidence fragments, causing victims to internalize and propagate fabricated conclusions.

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Challenge: Existing research has shown that large language models have difficulty discerning the veracity of their intrinsic answers.
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To Lie or Not to Lie? Investigating The Biased Spread of Global Lies by LLMs (2026.acl-long)

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Challenge: Misinformation is on the rise, and the strong writing capabilities of LLMs lower the barrier for malicious actors to produce and disseminate false information.
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Hidden in Plain Sight: Evaluation of the Deception Detection Capabilities of LLMs in Multimodal Settings (2025.acl-long)

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Challenge: Detecting deception in an increasingly digital world is a critical and challenging task.
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Boosting LLM Agents with Recursive Contemplation for Effective Deception Handling (2024.findings-acl)

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Challenge: Recent advances in large language models (LLMs) have led to significant success in using LLMs as agents.
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AI-LieDar : Examine the Trade-off Between Utility and Truthfulness in LLM Agents (2025.naacl-long)

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Challenge: LieDar is a framework to study how LLM-based agents navigate these scenarios in a multi-turn interactive setting.
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An Empirical Study of Group Conformity in Multi-Agent Systems (2025.findings-acl)

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Challenge: Recent advances in Large Language Models (LLMs) have enabled multi-agent systems that simulate real-world interactions with near-human reasoning.
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BadScientist: Can a Research Agent Write Convincing but Unsound Papers that Fool LLM Reviewers? (2026.acl-long)

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Challenge: Existing evidence suggests that LLMs are not able to detect scientifically unsound work from malicious or poorly designed research agents.
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MultiAgent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations via Debate (2024.findings-emnlp)

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Challenge: Large Language Models (LLMs) have shown exceptional results when working individually, and have reduced parameter size and inference times.
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MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data (2026.findings-acl)

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Challenge: Existing models for tabular data generation require large amounts of data to train effectively.
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Quantized but Deceptive? A Multi-Dimensional Truthfulness Evaluation of Quantized LLMs (2025.emnlp-main)

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Challenge: Quantization enables efficient deployment of large language models in resource-constrained environments . but impact on truthfulness remains largely unexplored .
Approach: They propose a framework to assess the truthfulness of quantized large language models . they find quantized models retain internally truthful representations but produce false outputs .
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