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

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.

• The paper introduces Rag-n-Roll, a framework that evaluates indirect prompt manipulation attacks in retrieval-augmented generation pipelines.
• The paper demonstrates that attack methods achieve about 40% success, rising to 60% with ambiguous responses considered.
• The paper highlights that defensive tweaks in RAG configurations may reduce functionality, prompting the need for more robust countermeasures.

An Evaluation of Indirect Prompt Manipulations in Retrieval-Augmented Generation Systems

The paper "Rag 'n Roll: An End-to-End Evaluation of Indirect Prompt Manipulations in LLM-based Application Frameworks" authored by Gianluca De Stefano, Lea Schönherr, and Giancarlo Pellegrino from CISPA Helmholtz Center for Information Security explores the security implications of Retrieval-Augmented Generation (RAG) systems. While RAG frameworks are increasingly being adopted to endow LLMs with the ability to handle out-of-distribution knowledge, there is a notable gap in understanding the security risks posed by these systems, specifically with respect to indirect prompt manipulation attacks.

Overview of the Research

The central focus of this paper is to evaluate the security vulnerabilities in RAG systems, stemming from indirect prompt manipulations. RAG systems combine LLMs with external knowledge bases to augment the model's response generation. This entails the risk of adversaries injecting malicious documents into the knowledge base, which might influence the model's responses to user queries.

The authors identify that while the risks associated with indirect prompt injections are theoretically known, their actual impacts on complete RAG application pipelines have not undergone rigorous, systematic evaluation. To bridge this gap, the authors construct a framework named Rag-n-Roll to test and measure the effectiveness of these attacks in varying RAG configurations.

Methodology and Framework

The authors begin by surveying existing RAG frameworks, examining their pipelines, and identifying critical configuration parameters. They then reference prior works to catalog techniques attackers can employ to augment malicious documents, thus boosting their rank during the retrieval phase of RAG systems.

Rag-n-Roll is designed to evaluate complete RAG configurations by exploring the end-to-end susceptibility of these systems to indirect prompt manipulations. This framework implements various attacks and provides a systematic method to evaluate their impact.

Experimental Results

The results highlight that most existing attacks tend to optimize the ranking of malicious documents during the retrieval phase. However, higher rankings do not always translate into successful attacks. The study reveals:

• Most attacks result in a 40% success rate in manipulating responses, which could extend to 60% if ambiguous answers are counted as successes.
• Non-optimized malicious documents, when duplicated, can replicate the success rate of optimized attack documents.
• Configurations of the RAG systems showed limited effectiveness in thwarting attacks; indeed, configurations that most effectively blocked the attacks severely undermined the system's functionality.

Implications of Findings

The paper concludes with several key insights:

1. Attack Reliability: Modern adversarial techniques targeting retrieval phases demonstrate limited success in hijacking final responses reliably, highlighting the need for advancements in attack strategies to more effectively impact downstream components of RAG systems.
2. Optimization Techniques: Traditional optimization methods, such as adding trigger tokens to increase retrievability, are not sufficient by themselves under a comprehensive evaluation framework.
3. Defense Strategies: Simply tweaking configuration parameters like chunk size or padding may not significantly bolster security against these attacks. However, enhancing the redundancy of benign data in the knowledge base shows promise as a mitigative strategy.
4. Future Optimization: The results underscore a need for future work to holistically optimize attacks that can address the RAG pipeline more comprehensively.

Future Directions

Looking forward, the paper suggests that future research should focus explicitly on improving optimization techniques for indirect prompt manipulations. This involves not only enhancing the malicious ranking during retrieval but also effectively manipulating the downstream processes culminating in the model's response generation. Additionally, as more robust adversarial techniques are developed, there is an equal necessity to pursue countermeasures that can dynamically adapt to evolving threats.

Conclusion

This paper provides an essential evaluation of indirect prompt manipulation risks in RAG systems, utilizing an end-to-end methodology to test and measure various attack vectors. The insights derived from the research illustrate the current gaps in defense mechanisms and propose practical pathways to enhance the robustness of RAG frameworks against such attacks. As RAG systems continue to integrate more deeply into various AI applications, addressing these security concerns will be critical to ensuring their safe and effective usage.