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arXiv cs.AI· Hangyi Zhao, Bonan Zhang, Fusen Guo, Jiaheng Wei, Chao Chen, Tianqing Zhu·· 2 小时前AI 评分42

MONOLOGUE:通过逻辑劫持实现 RAG 单文档投毒攻击

Logic Hijacking of RAG: Single-Document Poisoning via Local Optimization

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一篇 arXiv 论文提出 MONOLOGUE,一种针对 RAG 系统的单文档语料投毒攻击框架。该方法不直接反驳事实,而是先承认原文为历史基线,再构造“记录更正”叙事把反事实目标包装成最新更新,并通过离线的多维门控优化生成载荷。在三个基准、三个检索后端和四个开源权重生成器上的评测显示,它在所评单文档投毒攻击中攻击效果最强。

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Abstract:Single-document corpus poisoning under black-box access poses a severe threat to Retrieval-Augmented Generation (RAG). However, existing attacks predominantly rely on explicit factual refutations or imperative instruction overrides. Modern aligned LLMs often arbitrate such conflicts conservatively, rejecting overt contradictions in favor of parametric memory or clean retrieved context while triggering input guardrails. We propose MONOLOGUE, a single-document poisoning framework based on Logic Hijacking without explicit refutation. Rather than disputing the ground truth, our method concedes it as an authoritative historical baseline before introducing the update, weaving a grounded record-correction narrative (e.g., dated institutional reattributions) that presents the counterfactual target as the most recent update. To prevent suspicious live writes, payloads are refined entirely offline via local multi-dimensional gated optimization, coupling automated text quality audits with surrogate behavioral verification. Evaluations across three benchmarks, three retrieval backends, and four open-weight generator regimes show that our method achieves the strongest overall attack efficacy among the evaluated single-document poisoning attacks.
Comments: 22 pages, 3 figures, 14 tables
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.34829 [cs.AI]
  (or arXiv:2610.34829v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2610.34829

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zhao Hangyi [view email]
[v1] Mon, 26 Oct 2026 04:23:01 UTC (309 KB)

来源:arXiv cs.AI · arxiv.org