RAG-targeted Adversarial Attack on LLM-based Threat Detection and Mitigation Framework

I'm finding and summarising interesting AI research papers every day so you don't have to trawl through them all. Today's paper is titled "RAG-targeted Adversarial Attack on LLM-based Threat Detection and Mitigation Framework" by Seif Ikbarieh, Kshitiz Aryal, and Maanak Gupta.

This paper investigates the vulnerabilities of Large Language Model (LLM)-based intrusion detection and mitigation systems in the context of the rapidly growing Internet of Things (IoT). As IoT devices proliferate, they introduce significant security challenges, and leveraging AI for threat detection has become crucial. However, the authors highlight that integrating LLMs into cybersecurity frameworks may inadvertently increase their attack surface, introducing new forms of adversarial risks such as data poisoning and prompt injection.

Key findings from the paper include:

1. Data Poisoning Strategy: The authors constructed an attack description dataset and executed a targeted data poisoning attack on the Retrieval-Augmented Generation (RAG) knowledge base of an LLM-based threat detection framework, demonstrating how subtle and meaning-preserving word-level perturbations could dramatically affect model outputs.

2. Performance Degradation: The study showed that these minimal perturbations degraded the performance of ChatGPT-5 Thinking, resulting in weakened connections between network traffic features and attack behavior while also diminishing the specificity and practicality of the mitigation suggestions provided.

3. Comparative Evaluation: By comparing pre-attack and post-attack responses, the researchers established a quantitative framework to assess the impact of adversarial attacks, finding that the system's recommendation quality significantly declined following the introduction of perturbed descriptions.

4. Real-world Implications: The results underline the importance of evaluating the robustness of LLM-driven systems in real-world deployments, especially as they pertain to resource-constrained environments typical of many IoT applications.

5. Future Research Directions: The authors advocate for further exploration of coordinated attacks that combine RAG data poisoning with manipulations to network traffic features, aiming to enhance understanding of adversarial dynamics in such frameworks.

This research emphasizes a critical need for improved defenses against adversarial techniques in LLM applications, particularly within sensitive deployments like IoT networks.

You can catch the full breakdown here: Here
You can catch the full and original research paper here: Original Paper