Lying with the truth: open-channel multi-agent collusion manipulates beliefs via Generative Montage
Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montage
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The paper proposes a cognitive collusion attack where multiple agents collaboratively publish only fragments of true information on public channels, inducing victim agents to form false beliefs. The authors propose the Generative Montage framework, where three roles—Writer, Editor, Director—generate and rank evidence fragments through adversarial debate, then distribute them via Sybil accounts. On the CoPHEME dataset built from PHEME, the attack success rate across 14 model families reaches 74.4% (closed-source) and 70.6% (open-weight); reasoning-enhanced open-weight models are more susceptible than base models, while closed-source models show the opposite trend, with explicit CoT prompting increasing ASR by 3.1% and 4.7%, respectively. The downstream judge's deception rate exceeds 60%, and ablation studies show that degrading to a single agent reduces ASR to 26.8%.
- LLM 智能体在实时合成信息时，其推理能力反而成为攻击面。作者提出一种新型威胁：多个串通智能体仅用散布在公开渠道的真实证据片段，不靠隐蔽通信、后门或伪造文件，就能操纵受害者的信念。
- 作者形式化了首个认知串通攻击（Cognitive Collusion Attack），并提出 Generative Montage：由 Writer、Editor、Director 三个角色通过对抗辩论构造叙事、优化片段排序并验证欺骗效果，再经 Sybil 账号分散发布；同时构建了源自真实谣言事件的 CoPHEME 数据集进行模拟。
- 在 14 个 LLM 家族上攻击成功率约 74.4%（闭源）和 70.6%（开源）；推理能力更强的模型反而更易受害，Chain-of-Thought 提示使 ASR 上升；下游多数投票与 AI Judge 的欺骗率仍超 60%；消融显示单智能体化使 ASR 下降约 50.2%，证据序列长度呈倒 U 型，11-15 条时最有效。
- 作者指出 CoPHEME 仅限模拟环境中的文本谣言，未涵盖多模态、真实平台动态与自然反驳；工作刻画了漏洞但未提出具体防御机制，未来需研究原则性缓解措施与更广的认知级基准。
论文提出仅用真实信息碎片的多智能体合谋攻击，在14个模型家族上给出攻击成功率，并发现推理能力越强越易受影响。