Detecting RAG Extraction Attack via Dual-Path Runtime Integrity Game

• URL: http://arxiv.org/abs/2604.10717v1
• Date: Sun, 12 Apr 2026 16:28:09 GMT
• Title: Detecting RAG Extraction Attack via Dual-Path Runtime Integrity Game
• Abstract summary: Retrieval-Augmented Generation (RAG) systems augment large language models with external knowledge, yet introduce a critical security vulnerability RAG Knowledge Base Leakage.<n>We propose CanaryRAG, a runtime defense mechanism inspired by stack canaries in software security.<n>CanaryRAG embeds carefully designed canary tokens into retrieved chunks and reformulates RAG extraction defense as a dual-path runtime integrity game.
• Score: 23.794446184684755
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) systems augment large language models with external knowledge, yet introduce a critical security vulnerability: RAG Knowledge Base Leakage, wherein adversarial prompts can induce the model to divulge retrieved proprietary content. Recent studies reveal that such leakage can be executed through adaptive and iterative attack strategies (named RAG extraction attack), while effective countermeasures remain notably lacking. To bridge this gap, we propose CanaryRAG, a runtime defense mechanism inspired by stack canaries in software security. CanaryRAG embeds carefully designed canary tokens into retrieved chunks and reformulates RAG extraction defense as a dual-path runtime integrity game. Leakage is detected in real time whenever either the target or oracle path violates its expected canary behavior, including under adaptive suppression and obfuscation. Extensive evaluations against existing attacks demonstrate that CanaryRAG provides robust defense, achieving substantially lower chunk recovery rates than state-of-the-art baselines while imposing negligible impact on task performance and inference latency. Moreover, as a plug-and-play solution, CanaryRAG can be seamlessly integrated into arbitrary RAG pipelines without requiring retraining or structural modifications, offering a practical and scalable safeguard for proprietary data.

Related papers

• TwinGate: Stateful Defense against Decompositional Jailbreaks in Untraceable Traffic via Asymmetric Contrastive Learning [60.68349524623048]
  Decompositional jailbreaks pose a critical threat to large language models.<n>We present TwinGate, a stateful dual-encoder defense framework.<n>We construct a comprehensive dataset of over 3.62 million instructions spanning 8,600 distinct malicious intents.
  arXiv  Detail & Related papers  (2026-04-30T13:44:01Z)
• Adaptive Defense Orchestration for RAG: A Sentinel-Strategist Architecture against Multi-Vector Attacks [0.0]
  Retrieval-augmented generation systems are increasingly deployed in sensitive domains such as healthcare and law.<n>This capability introduces significant security risks, including membership inference, data poisoning, and unintended content leakage.<n>We propose the Sentinel-Strategist architecture, a context-aware framework for risk analysis and defense selection.
  arXiv  Detail & Related papers  (2026-04-22T11:17:10Z)
• Confundo: Learning to Generate Robust Poison for Practical RAG Systems [19.77771071590713]
  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.
  arXiv  Detail & Related papers  (2026-02-06T11:19:49Z)
• Connect the Dots: Knowledge Graph-Guided Crawler Attack on Retrieval-Augmented Generation Systems [14.028345839891855]
  Retrieval-augmented generation (RAG) systems integrate document retrieval with large language models.<n>RAG introduces a new privacy risk: adversaries can issue carefully crafted queries to exfiltrate sensitive content gradually.<n>We introduce RAGCRAWLER, which builds a knowledge graph to represent revealed information, and plans queries in semantic space that target unretrieved regions.
  arXiv  Detail & Related papers  (2026-01-22T05:59:42Z)
• RAGPart & RAGMask: Retrieval-Stage Defenses Against Corpus Poisoning in Retrieval-Augmented Generation [43.85099769473328]
  Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm to enhance large language models.<n>Recent studies have exposed a critical vulnerability in RAG pipelines corpus poisoning where adversaries inject malicious documents into the retrieval corpus to manipulate model outputs.<n>We propose two complementary retrieval-stage defenses: RAGPart and RAGMask.
  arXiv  Detail & Related papers  (2025-12-30T14:43:57Z)
• 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)
• Disabling Self-Correction in Retrieval-Augmented Generation via Stealthy Retriever Poisoning [14.419943772894754]
  Retrieval-Augmented Generation (RAG) has become a standard approach for improving the reliability of large language models (LLMs)<n>This paper uncovers that such attacks could be mitigated by the strong textitself-correction ability (SCA) of modern LLMs.<n>We introduce textscDisarmRAG, a new poisoning paradigm that compromises the retriever itself to suppress the SCA and enforce attacker-chosen outputs.
  arXiv  Detail & Related papers  (2025-08-27T17:49:28Z)
• 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)
• Certifiably Robust RAG against Retrieval Corruption [58.677292678310934]
  Retrieval-augmented generation (RAG) has been shown vulnerable to retrieval corruption attacks. In this paper, we propose RobustRAG as the first defense framework against retrieval corruption attacks.
  arXiv  Detail & Related papers  (2024-05-24T13:44:25Z)
• On Trace of PGD-Like Adversarial Attacks [77.75152218980605]
  Adversarial attacks pose safety and security concerns for deep learning applications. We construct Adrial Response Characteristics (ARC) features to reflect the model's gradient consistency. Our method is intuitive, light-weighted, non-intrusive, and data-undemanding.
  arXiv  Detail & Related papers  (2022-05-19T14:26:50Z)
• Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning [71.17774313301753]
  We explore the robustness of self-supervised learned high-level representations by using them in the defense against adversarial attacks. Experimental results on the ASVspoof 2019 dataset demonstrate that high-level representations extracted by Mockingjay can prevent the transferability of adversarial examples.
  arXiv  Detail & Related papers  (2020-06-05T03:03:06Z)

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.