Rag and Roll: An End-to-End Evaluation of Indirect Prompt Manipulations in LLM-based Application Frameworks

• URL: http://arxiv.org/abs/2408.05025v2
• Date: Mon, 12 Aug 2024 13:57:42 GMT
• Title: Rag and Roll: An End-to-End Evaluation of Indirect Prompt Manipulations in LLM-based Application Frameworks
• Authors: Gianluca De Stefano, Lea Schönherr, Giancarlo Pellegrino,
• Abstract summary: Retrieval Augmented Generation (RAG) is a technique commonly used to equip models with out of distribution knowledge. This paper investigates the security of RAG systems against end-to-end indirect prompt manipulations.
• Score: 12.061098193438022
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval Augmented Generation (RAG) is a technique commonly used to equip models with out of distribution knowledge. This process involves collecting, indexing, retrieving, and providing information to an LLM for generating responses. Despite its growing popularity due to its flexibility and low cost, the security implications of RAG have not been extensively studied. The data for such systems are often collected from public sources, providing an attacker a gateway for indirect prompt injections to manipulate the responses of the model. In this paper, we investigate the security of RAG systems against end-to-end indirect prompt manipulations. First, we review existing RAG framework pipelines, deriving a prototypical architecture and identifying critical parameters. We then examine prior works searching for techniques that attackers can use to perform indirect prompt manipulations. Finally, we implemented Rag 'n Roll, a framework to determine the effectiveness of attacks against end-to-end RAG applications. Our results show that existing attacks are mostly optimized to boost the ranking of malicious documents during the retrieval phase. However, a higher rank does not immediately translate into a reliable attack. Most attacks, against various configurations, settle around a 40% success rate, which could rise to 60% when considering ambiguous answers as successful attacks (those that include the expected benign one as well). Additionally, when using unoptimized documents, attackers deploying two of them (or more) for a target query can achieve similar results as those using optimized ones. Finally, exploration of the configuration space of a RAG showed limited impact in thwarting the attacks, where the most successful combination severely undermines functionality.

Related papers

• SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model [17.046058202577985]
  We introduce a benchmark named SafeRAG designed to evaluate the RAG security. First, we classify attack tasks into silver noise, inter-context conflict, soft ad, and white Denial-of-Service. We then utilize the SafeRAG dataset to simulate various attack scenarios that RAG may encounter.
  arXiv  Detail & Related papers  (2025-01-28T17:01:31Z)
• Chain-of-Retrieval Augmented Generation [72.06205327186069]
  This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer. Our proposed method, CoRAG, allows the model to dynamically reformulate the query based on the evolving state.
  arXiv  Detail & Related papers  (2025-01-24T09:12:52Z)
• FlipedRAG: Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models [19.41533176888415]
  Retrieval-Augmented Generation (RAG) addresses hallucination and real-time constraints by dynamically retrieving relevant information from a knowledge database. In this paper, we unveil a more realistic and threatening scenario: opinion manipulation for controversial topics against RAG. We propose a novel RAG black-box attack method, termed FlipedRAG, which is transfer-based.
  arXiv  Detail & Related papers  (2025-01-06T12:24:57Z)
• Attack-in-the-Chain: Bootstrapping Large Language Models for Attacks Against Black-box Neural Ranking Models [111.58315434849047]
  We introduce a novel ranking attack framework named Attack-in-the-Chain. It tracks interactions between large language models (LLMs) and Neural ranking models (NRMs) based on chain-of-thought. Empirical results on two web search benchmarks show the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-12-25T04:03:09Z)
• Evaluating and Improving the Robustness of Security Attack Detectors Generated by LLMs [6.936401700600395]
  Large Language Models (LLMs) are increasingly used in software development to generate functions, such as attack detectors, that implement security requirements. This is most likely due to the LLM lacking knowledge about some existing attacks and to the generated code being not evaluated in real usage scenarios. We propose a novel approach integrating Retrieval Augmented Generation (RAG) and Self-Ranking into the LLM pipeline.
  arXiv  Detail & Related papers  (2024-11-27T10:48:37Z)
• AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning [93.77763753231338]
  Adversarial Contrastive Prompt Tuning (ACPT) is proposed to fine-tune the CLIP image encoder to extract similar embeddings for any two intermediate adversarial queries. We show that ACPT can detect 7 state-of-the-art query-based attacks with $>99%$ detection rate within 5 shots. We also show that ACPT is robust to 3 types of adaptive attacks.
  arXiv  Detail & Related papers  (2024-08-04T09:53:50Z)
• Corpus Poisoning via Approximate Greedy Gradient Descent [48.5847914481222]
  We propose Approximate Greedy Gradient Descent, a new attack on dense retrieval systems based on the widely used HotFlip method for generating adversarial passages. We show that our method achieves a high attack success rate on several datasets and using several retrievers, and can generalize to unseen queries and new domains.
  arXiv  Detail & Related papers  (2024-06-07T17:02:35Z)
• BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models [18.107026036897132]
  Large Language Models (LLMs) are constrained by outdated information and a tendency to generate incorrect data. Retrieval-Augmented Generation (RAG) addresses these limitations by combining the strengths of retrieval-based methods and generative models. RAG introduces a new attack surface for LLMs, particularly because RAG databases are often sourced from public data, such as the web.
  arXiv  Detail & Related papers  (2024-06-03T02:25:33Z)
• Is My Data in Your Retrieval Database? Membership Inference Attacks Against Retrieval Augmented Generation [0.9217021281095907]
  We introduce an efficient and easy-to-use method for conducting a Membership Inference Attack (MIA) against RAG systems. We demonstrate the effectiveness of our attack using two benchmark datasets and multiple generative models. Our findings highlight the importance of implementing security countermeasures in deployed RAG systems.
  arXiv  Detail & Related papers  (2024-05-30T19:46:36Z)
• Learning diverse attacks on large language models for robust red-teaming and safety tuning [126.32539952157083]
  Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe deployment of large language models. We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks. We propose to use GFlowNet fine-tuning, followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts.
  arXiv  Detail & Related papers  (2024-05-28T19:16:17Z)
• 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)

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.