Targeting the Core: A Simple and Effective Method to Attack RAG-based Agents via Direct LLM Manipulation

TL;DR

This paper reveals a critical vulnerability in LLM-powered AI agents where simple adversarial prompts can bypass safeguards, causing dangerous outputs, highlighting the need for improved security measures.

Contribution

It demonstrates the effectiveness of straightforward adversarial prefixes in attacking LLM-based agents, exposing a significant security weakness in current defenses.

Findings

High attack success rate with simple prompts

Existing defenses are fragile against adversarial prefixes

Highlights urgent need for robust security measures

Abstract

AI agents, powered by large language models (LLMs), have transformed human-computer interactions by enabling seamless, natural, and context-aware communication. While these advancements offer immense utility, they also inherit and amplify inherent safety risks such as bias, fairness, hallucinations, privacy breaches, and a lack of transparency. This paper investigates a critical vulnerability: adversarial attacks targeting the LLM core within AI agents. Specifically, we test the hypothesis that a deceptively simple adversarial prefix, such as \textit{Ignore the document}, can compel LLMs to produce dangerous or unintended outputs by bypassing their contextual safeguards. Through experimentation, we demonstrate a high attack success rate (ASR), revealing the fragility of existing LLM defenses. These findings emphasize the urgent need for robust, multi-layered security measures tailored to mitigate vulnerabilities at the LLM level and within broader agent-based architectures.