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Poisoning Retrieval Corpora by Injecting Adversarial Passages

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arxiv 2310.19156 v1 pith:HQJAD6FE submitted 2023-10-29 cs.CL cs.IR

Zexuan Zhong , Ziqing Huang , Alexander Wettig , Danqi Chen This is my paper
classification cs.CLcs.IR
keywords passagesretrievaladversarialattackdensequeriessystemscorpora
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Dense retrievers have achieved state-of-the-art performance in various information retrieval tasks, but to what extent can they be safely deployed in real-world applications? In this work, we propose a novel attack for dense retrieval systems in which a malicious user generates a small number of adversarial passages by perturbing discrete tokens to maximize similarity with a provided set of training queries. When these adversarial passages are inserted into a large retrieval corpus, we show that this attack is highly effective in fooling these systems to retrieve them for queries that were not seen by the attacker. More surprisingly, these adversarial passages can directly generalize to out-of-domain queries and corpora with a high success attack rate -- for instance, we find that 50 generated passages optimized on Natural Questions can mislead >94% of questions posed in financial documents or online forums. We also benchmark and compare a range of state-of-the-art dense retrievers, both unsupervised and supervised. Although different systems exhibit varying levels of vulnerability, we show they can all be successfully attacked by injecting up to 500 passages, a small fraction compared to a retrieval corpus of millions of passages.

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses

    cs.CR 2026-07 conditional novelty 7.0 of 10

    FARMA forges and self-amplifies an agent's reasoning history with evasive language to induce unsafe skips; SENTINEL's Reasoning Guard reduces ASR to 0% across tested agents and models.

  2. Replicating Belief, Not Bits: Epistemic State Replication for Agentic Systems

    cs.AI 2026-07 conditional novelty 7.0 of 10

    ESR separates an immutable evidence log from a stochastic belief lineage so agent replicas stay semantically compatible without bitwise state equality.

  3. DisarmRAG: Stealthy Retriever-Centric Poisoning to Disable Self-Correction in Retrieval-Augmented Generation (Extended Version)

    cs.CR 2025-08 conditional novelty 7.0 of 10

    DisarmRAG compromises the retriever to inject anti-self-correction instructions, achieving over 90% attack success across six LLMs while evading basic detection.

  4. Agent Security Needs Redefinition through a Holistic Framework

    cs.CR 2026-07 conditional novelty 6.0 of 10

    Agent security should be redefined around four contextual authorization properties instead of the content of the action performed.

  5. ImportSnare: Directed "Code Manual" Hijacking in Retrieval-Augmented Code Generation

    cs.CR 2025-09 conditional novelty 6.0 of 10

    Documentation poisoning with hidden ranking and suggestion sequences can make RAG-based code generators confidently recommend malicious dependencies, even at 0.01% poisoning ratios.

  6. Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMs

    cs.LG 2025-06 reject novelty 6.0 of 10

    A retrieval-augmented generation system can be poisoned with reward-optimized biased documents and vector-space manipulation to substantially increase biased LLM outputs.

  7. Spa-VLM: Stealthy Poisoning Attacks on RAG-based VLM

    cs.CR 2025-05 conditional novelty 6.0 of 10

    Spa-VLM crafts paired adversarial images and misleading texts to poison RAG-based VLM knowledge bases, reaching attack success rates above 0.8 with just five injected entries.

  8. CPA-RAG:Covert Poisoning Attacks on Retrieval-Augmented Generation in Large Language Models

    cs.CR 2025-05 conditional novelty 6.0 of 10

    A black-box poisoning framework, CPA-RAG, generates fluent fake documents that steer retrieval-augmented language models toward attacker-chosen wrong answers, achieving over 90% success in the reported experiments.

  9. We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent Systems

    cs.LG 2025-06 conditional novelty 5.0 of 10

    MCP-powered LLM agents are vulnerable to prompt injection from third-party services, and simple detection or filtering defenses do not reliably stop these attacks.

  10. Benchmarking Poisoning Attacks against Retrieval-Augmented Generation

    cs.CR 2025-05 conditional novelty 5.0 of 10

    A unified benchmark evaluation finds that existing RAG poisoning attacks remain effective on standard QA datasets, drop on expanded knowledge bases, and are only partially mitigated by current defenses.

  11. Scalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval

    cs.CR 2025-05 conditional novelty 5.0 of 10

    SCR uses retrieval-augmented generation to fetch refusal examples that block jailbreak attacks, but the reported advantages are partly overstated.

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