PR-Attack: Coordinated Prompt-RAG Attacks on Retrieval-Augmented Generation in Large Language Models via Bilevel Optimization

• URL: http://arxiv.org/abs/2504.07717v2
• Date: Thu, 17 Apr 2025 02:01:42 GMT
• Title: PR-Attack: Coordinated Prompt-RAG Attacks on Retrieval-Augmented Generation in Large Language Models via Bilevel Optimization
• Authors: Yang Jiao, Xiaodong Wang, Kai Yang,
• Abstract summary: Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of applications.<n>They also exhibit inherent limitations, such as outdated knowledge and susceptibility to hallucinations.<n>Recent efforts have focused on the security of RAG-based LLMs, yet existing attack methods face three critical challenges.<n>We propose coordinated Prompt-RAG attack (PR-attack), a novel optimization-driven attack that introduces a small number of poisoned texts into the knowledge database.
• Score: 13.751251342738225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of applications, e.g., medical question-answering, mathematical sciences, and code generation. However, they also exhibit inherent limitations, such as outdated knowledge and susceptibility to hallucinations. Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm to address these issues, but it also introduces new vulnerabilities. Recent efforts have focused on the security of RAG-based LLMs, yet existing attack methods face three critical challenges: (1) their effectiveness declines sharply when only a limited number of poisoned texts can be injected into the knowledge database, (2) they lack sufficient stealth, as the attacks are often detectable by anomaly detection systems, which compromises their effectiveness, and (3) they rely on heuristic approaches to generate poisoned texts, lacking formal optimization frameworks and theoretic guarantees, which limits their effectiveness and applicability. To address these issues, we propose coordinated Prompt-RAG attack (PR-attack), a novel optimization-driven attack that introduces a small number of poisoned texts into the knowledge database while embedding a backdoor trigger within the prompt. When activated, the trigger causes the LLM to generate pre-designed responses to targeted queries, while maintaining normal behavior in other contexts. This ensures both high effectiveness and stealth. We formulate the attack generation process as a bilevel optimization problem leveraging a principled optimization framework to develop optimal poisoned texts and triggers. Extensive experiments across diverse LLMs and datasets demonstrate the effectiveness of PR-Attack, achieving a high attack success rate even with a limited number of poisoned texts and significantly improved stealth compared to existing methods.

Related papers

• Hoist with His Own Petard: Inducing Guardrails to Facilitate Denial-of-Service Attacks on Retrieval-Augmented Generation of LLMs [8.09404178079053]
  Retrieval-Augmented Generation (RAG) integrates Large Language Models (LLMs) with external knowledge bases, improving output quality while introducing new security risks. Existing studies on RAG vulnerabilities typically focus on exploiting the retrieval mechanism to inject erroneous knowledge or malicious texts, inducing incorrect outputs. In this paper, we uncover a novel vulnerability: the safety guardrails of LLMs, while designed for protection, can also be exploited as an attack vector by adversaries. Building on this vulnerability, we propose MutedRAG, a novel denial-of-service attack that reversely leverages the guardrails to undermine the availability of
  arXiv  Detail & Related papers  (2025-04-30T14:18:11Z)
• BadApex: Backdoor Attack Based on Adaptive Optimization Mechanism of Black-box Large Language Models [23.612489763464353]
  Previous insertion-based backdoors have achieved great success in attack efficacy, but they ignore the text quality and semantic consistency between poisoned and clean texts. We propose a novel backdoor attack based on adaptive optimization mechanism of black-box large language models (BadApex) We conduct extensive experiments on three dataset with six backdoor attacks and two defenses.
  arXiv  Detail & Related papers  (2025-04-18T16:22:41Z)
• 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)
• Align in Depth: Defending Jailbreak Attacks via Progressive Answer Detoxification [17.500701903902094]
  Large Language Models (LLMs) are vulnerable to jailbreak attacks, which use crafted prompts to elicit toxic responses.<n>This paper proposes DEEPALIGN, a robust defense framework that fine-tunes LLMs to progressively detoxify generated content.
  arXiv  Detail & Related papers  (2025-03-14T08:32:12Z)
• 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) 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 [109.53357276796655]
  Multimodal large language models (MLLMs) equipped with Retrieval Augmented Generation (RAG) RAG enhances MLLMs by grounding responses in query-relevant external knowledge. This reliance poses a critical yet underexplored safety risk: knowledge poisoning attacks. We propose MM-PoisonRAG, a novel knowledge poisoning attack framework with two attack strategies.
  arXiv  Detail & Related papers  (2025-02-25T04:23:59Z)
• Reasoning-Augmented Conversation for Multi-Turn Jailbreak Attacks on Large Language Models [53.580928907886324]
  Reasoning-Augmented Conversation is a novel multi-turn jailbreak framework.<n>It reformulates harmful queries into benign reasoning tasks.<n>We show that RACE achieves state-of-the-art attack effectiveness in complex conversational scenarios.
  arXiv  Detail & Related papers  (2025-02-16T09:27:44Z)
• Compromising Embodied Agents with Contextual Backdoor Attacks [69.71630408822767]
  Large language models (LLMs) have transformed the development of embodied intelligence. This paper uncovers a significant backdoor security threat within this process. By poisoning just a few contextual demonstrations, attackers can covertly compromise the contextual environment of a black-box LLM.
  arXiv  Detail & Related papers  (2024-08-06T01:20:12Z)
• 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)
• Turning Generative Models Degenerate: The Power of Data Poisoning Attacks [10.36389246679405]
  Malicious actors can introduce backdoors through poisoning attacks to generate undesirable outputs. We conduct an investigation of various poisoning techniques targeting the large language models' fine-tuning phase via the Efficient Fine-Tuning (PEFT) method. Our study presents the first systematic approach to understanding poisoning attacks targeting NLG tasks during fine-tuning via PEFT.
  arXiv  Detail & Related papers  (2024-07-17T03:02:15Z)
• Advancing Generalized Transfer Attack with Initialization Derived Bilevel Optimization and Dynamic Sequence Truncation [49.480978190805125]
  Transfer attacks generate significant interest for black-box applications. Existing works essentially directly optimize the single-level objective w.r.t. surrogate model. We propose a bilevel optimization paradigm, which explicitly reforms the nested relationship between the Upper-Level (UL) pseudo-victim attacker and the Lower-Level (LL) surrogate attacker.
  arXiv  Detail & Related papers  (2024-06-04T07:45:27Z)
• PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models [45.409248316497674]
  Large language models (LLMs) have achieved remarkable success due to their exceptional generative capabilities. Retrieval-Augmented Generation (RAG) is a state-of-the-art technique to mitigate these limitations. We find that the knowledge database in a RAG system introduces a new and practical attack surface. Based on this attack surface, we propose PoisonedRAG, the first knowledge corruption attack to RAG.
  arXiv  Detail & Related papers  (2024-02-12T18:28:36Z)
• A Novel Evaluation Framework for Assessing Resilience Against Prompt Injection Attacks in Large Language Models [0.0]
  This study introduces a novel framework for quantifying the resilience of applications. The framework incorporates innovative techniques designed to ensure representativeness, interpretability, and robustness. Results revealed that Llama2, the newer model exhibited higher resilience compared to ChatGLM.
  arXiv  Detail & Related papers  (2024-01-02T02:06:48Z)

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.