Confundo: Learning to Generate Robust Poison for Practical RAG Systems

• URL: http://arxiv.org/abs/2602.06616v1
• Date: Fri, 06 Feb 2026 11:19:49 GMT
• Title: Confundo: Learning to Generate Robust Poison for Practical RAG Systems
• Authors: Haoyang Hu, Zhejun Jiang, Yueming Lyu, Junyuan Zhang, Yi Liu, Ka-Ho Chow,
• Abstract summary: Confundo is a learning-to-poison framework that fine-tunes a large language model as a poison generator to achieve high effectiveness, robustness, and stealthiness.<n>We show how Confundo consistently outperforms a wide range of purpose-built attacks across datasets and RAG configurations.<n>We also present a defensive use case that protects web content from unauthorized incorporation into RAG systems via scraping.
• Score: 19.77771071590713
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-augmented generation (RAG) is increasingly deployed in real-world applications, where its reference-grounded design makes outputs appear trustworthy. This trust has spurred research on poisoning attacks that craft malicious content, inject it into knowledge sources, and manipulate RAG responses. However, when evaluated in practical RAG systems, existing attacks suffer from severely degraded effectiveness. This gap stems from two overlooked realities: (i) content is often processed before use, which can fragment the poison and weaken its effect, and (ii) users often do not issue the exact queries anticipated during attack design. These factors can lead practitioners to underestimate risks and develop a false sense of security. To better characterize the threat to practical systems, we present Confundo, a learning-to-poison framework that fine-tunes a large language model as a poison generator to achieve high effectiveness, robustness, and stealthiness. Confundo provides a unified framework supporting multiple attack objectives, demonstrated by manipulating factual correctness, inducing biased opinions, and triggering hallucinations. By addressing these overlooked challenges, Confundo consistently outperforms a wide range of purpose-built attacks across datasets and RAG configurations by large margins, even in the presence of defenses. Beyond exposing vulnerabilities, we also present a defensive use case that protects web content from unauthorized incorporation into RAG systems via scraping, with no impact on user experience.

Related papers

• Rescuing the Unpoisoned: Efficient Defense against Knowledge Corruption Attacks on RAG Systems [11.812488957698038]
  Large language models (LLMs) are reshaping numerous facets of our daily lives, leading widespread adoption as web-based services.<n>Retrieval-Augmented Generation (RAG) has emerged as a promising direction by generating responses grounded in external knowledge sources.<n>Recent studies demonstrate the vulnerability of RAG, such as knowledge corruption attacks by injecting misleading information.<n>In this work, we introduce RAGDefender, a resource-efficient defense mechanism against knowledge corruption.
  arXiv  Detail & Related papers  (2025-11-03T06:39:58Z)
• Benchmarking Misuse Mitigation Against Covert Adversaries [80.74502950627736]
  Existing language model safety evaluations focus on overt attacks and low-stakes tasks.<n>We develop Benchmarks for Stateful Defenses (BSD), a data generation pipeline that automates evaluations of covert attacks and corresponding defenses.<n>Our evaluations indicate that decomposition attacks are effective misuse enablers, and highlight stateful defenses as a countermeasure.
  arXiv  Detail & Related papers  (2025-06-06T17:33:33Z)
• The Silent Saboteur: Imperceptible Adversarial Attacks against Black-Box Retrieval-Augmented Generation Systems [101.68501850486179]
  We explore adversarial attacks against retrieval-augmented generation (RAG) systems to identify their vulnerabilities.<n>This task aims to find imperceptible perturbations that retrieve a target document, originally excluded from the initial top-$k$ candidate set.<n>We propose ReGENT, a reinforcement learning-based framework that tracks interactions between the attacker and the target RAG.
  arXiv  Detail & Related papers  (2025-05-24T08:19:25Z)
• Benchmarking Poisoning Attacks against Retrieval-Augmented Generation [12.573766276297441]
  Retrieval-Augmented Generation (RAG) has proven effective in mitigating hallucinations in large language models by incorporating external knowledge during inference.<n>We propose the first comprehensive benchmark framework for evaluating poisoning attacks on RAG.
  arXiv  Detail & Related papers  (2025-05-24T06:17:59Z)
• One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems [28.06028279729382]
  Large Language Models (LLMs) enhanced with Retrieval-Augmented Generation (RAG) have shown improved performance in generating accurate responses.<n> dependence on external knowledge bases introduces potential security vulnerabilities.<n>This paper reveals a more realistic knowledge poisoning attack against RAG systems that achieves successful attacks by poisoning only a single document.
  arXiv  Detail & Related papers  (2025-05-15T08:14:58Z)
• POISONCRAFT: Practical Poisoning of Retrieval-Augmented Generation for Large Language Models [4.620537391830117]
  Large language models (LLMs) are susceptible to hallucinations, which can lead to incorrect or misleading outputs.<n>Retrieval-augmented generation (RAG) is a promising approach to mitigate hallucinations by leveraging external knowledge sources.<n>In this paper, we study a poisoning attack on RAG systems named POISONCRAFT, which can mislead the model to refer to fraudulent websites.
  arXiv  Detail & Related papers  (2025-05-10T09:36:28Z)
• Practical Poisoning Attacks against Retrieval-Augmented Generation [9.320227105592917]
  Large language models (LLMs) have demonstrated impressive natural language processing abilities but face challenges such as hallucination and outdated knowledge.<n>Retrieval-Augmented Generation (RAG) has emerged as a state-of-the-art approach to mitigate these issues.<n>We propose CorruptRAG, a practical poisoning attack against RAG systems in which the attacker injects only a single poisoned text.
  arXiv  Detail & Related papers  (2025-04-04T21:49:42Z)
• Poisoned-MRAG: Knowledge Poisoning Attacks to Multimodal Retrieval Augmented Generation [71.32665836294103]
  Multimodal retrieval-augmented generation (RAG) enhances the visual reasoning capability of vision-language models (VLMs)<n>In this work, we introduce textitPoisoned-MRAG, the first knowledge poisoning attack on multimodal RAG systems.
  arXiv  Detail & Related papers  (2025-03-08T15:46:38Z)
• MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Poisoning Attacks [104.50239783909063]
  Multimodal large language models with Retrieval Augmented Generation (RAG) have significantly advanced tasks such as multimodal question answering.<n>This reliance on external knowledge poses a critical yet underexplored safety risk: knowledge poisoning attacks.<n>We propose MM-PoisonRAG, the first framework to systematically design knowledge poisoning in multimodal RAG.
  arXiv  Detail & Related papers  (2025-02-25T04:23:59Z)
• Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attacks [45.07581174558107]
  Retrieval-Augmented Generation (RAG) systems have emerged as a promising solution to mitigate hallucinations.<n>RAG systems are vulnerable to adversarial poisoning attacks, where malicious passages injected into the retrieval corpus can mislead models into producing factually incorrect outputs.<n>We present a rigorously controlled empirical study of how RAG systems behave under such attacks and how their robustness can be improved.
  arXiv  Detail & Related papers  (2024-12-21T17:31:52Z)
• AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases [73.04652687616286]
  We propose AgentPoison, the first backdoor attack targeting generic and RAG-based LLM agents by poisoning their long-term memory or RAG knowledge base. Unlike conventional backdoor attacks, AgentPoison requires no additional model training or fine-tuning. On each agent, AgentPoison achieves an average attack success rate higher than 80% with minimal impact on benign performance.
  arXiv  Detail & Related papers  (2024-07-17T17:59:47Z)
• On the Security Risks of Knowledge Graph Reasoning [71.64027889145261]
  We systematize the security threats to KGR according to the adversary's objectives, knowledge, and attack vectors. We present ROAR, a new class of attacks that instantiate a variety of such threats. We explore potential countermeasures against ROAR, including filtering of potentially poisoning knowledge and training with adversarially augmented queries.
  arXiv  Detail & Related papers  (2023-05-03T18:47:42Z)

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.