AdversarialCoT: Single-Document Retrieval Poisoning for LLM Reasoning

Retrieval-augmented generation (RAG) enhances large language model (LLM) reasoning by retrieving external documents, but also opens up new attack surfaces. We study knowledge-base poisoning attacks in RAG, where an attacker injects malicious content into the retrieval corpus, which is then naturally surfaced by the retriever and consumed by the LLM during reasoning. Unlike prior work that floods the corpus with poisoned documents, we propose AdversarialCoT, a query-specific attack that poisons only a single document in the corpus. AdversarialCoT first extracts the target LLM's reasoning framework to guide the construction of an initial adversarial chain-of-thought (CoT). The adversarial document is iteratively refined through interactions with the LLM, progressively exposing and exploiting critical reasoning vulnerabilities. Experiments on benchmark LLMs show that a single adversarial document can significantly degrade reasoning accuracy, revealing subtle yet impactful weaknesses. This study exposes security risks in RAG systems and provides actionable insights for designing more robust LLM reasoning pipelines.

The Silent Saboteur: Imperceptible Adversarial Attacks against Black-Box Retrieval-Augmented Generation Systems

We explore adversarial attacks against retrieval-augmented generation (RAG) systems to identify their vulnerabilities. We focus on generating human-imperceptible adversarial examples and introduce a novel imperceptible retrieve-to-generate attack against RAG. This task aims to find imperceptible perturbations that retrieve a target document, originally excluded from the initial top-$k$ candidate set, in order to influence the final answer generation. To address this task, we propose ReGENT, a reinforcement learning-based framework that tracks interactions between the attacker and the target RAG and continuously refines attack strategies based on relevance-generation-naturalness rewards. Experiments on newly constructed factual and non-factual question-answering benchmarks demonstrate that ReGENT significantly outperforms existing attack methods in misleading RAG systems with small imperceptible text perturbations.

Chain-of-Thought Poisoning Attacks against R1-based Retrieval-Augmented Generation Systems

Retrieval-augmented generation (RAG) systems can effectively mitigate the hallucination problem of large language models (LLMs),but they also possess inherent vulnerabilities. Identifying these weaknesses before the large-scale real-world deployment of RAG systems is of great importance, as it lays the foundation for building more secure and robust RAG systems in the future. Existing adversarial attack methods typically exploit knowledge base poisoning to probe the vulnerabilities of RAG systems, which can effectively deceive standard RAG models. However, with the rapid advancement of deep reasoning capabilities in modern LLMs, previous approaches that merely inject incorrect knowledge are inadequate when attacking RAG systems equipped with deep reasoning abilities. Inspired by the deep thinking capabilities of LLMs, this paper extracts reasoning process templates from R1-based RAG systems, uses these templates to wrap erroneous knowledge into adversarial documents, and injects them into the knowledge base to attack RAG systems. The key idea of our approach is that adversarial documents, by simulating the chain-of-thought patterns aligned with the model's training signals, may be misinterpreted by the model as authentic historical reasoning processes, thus increasing their likelihood of being referenced. Experiments conducted on the MS MARCO passage ranking dataset demonstrate the effectiveness of our proposed method.