AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents (2505.05849v4)

Abstract: The strong planning and reasoning capabilities of LLMs have fostered the development of agent-based systems capable of leveraging external tools and interacting with increasingly complex environments. However, these powerful features also introduce a critical security risk: indirect prompt injection, a sophisticated attack vector that compromises the core of these agents, the LLM, by manipulating contextual information rather than direct user prompts. In this work, we propose a generic black-box fuzzing framework, AgentVigil, designed to automatically discover and exploit indirect prompt injection vulnerabilities across diverse LLM agents. Our approach starts by constructing a high-quality initial seed corpus, then employs a seed selection algorithm based on Monte Carlo Tree Search (MCTS) to iteratively refine inputs, thereby maximizing the likelihood of uncovering agent weaknesses. We evaluate AgentVigil on two public benchmarks, AgentDojo and VWA-adv, where it achieves 71% and 70% success rates against agents based on o3-mini and GPT-4o, respectively, nearly doubling the performance of baseline attacks. Moreover, AgentVigil exhibits strong transferability across unseen tasks and internal LLMs, as well as promising results against defenses. Beyond benchmark evaluations, we apply our attacks in real-world environments, successfully misleading agents to navigate to arbitrary URLs, including malicious sites.

• The paper introduces AgentXploit, an automated black-box fuzzing framework that uses a genetic approach to discover indirect prompt injection vulnerabilities in LLM agents.
• Experiments show AgentXploit achieved high success rates on benchmarks, significantly outperforming baselines and demonstrating strong attack transferability.
• This work highlights the need for more sophisticated defenses against automated indirect prompt injection attacks and informs the development of resilient AI systems.

An Analytical Review of "AgentXploit: End-to-End Redteaming of Black-Box AI Agents"

The research paper titled "AgentXploit: End-to-End Redteaming of Black-Box AI Agents" introduces a novel approach to assessing vulnerabilities in AI agents that utilize LLMs. The paper addresses a critical security concern known as indirect prompt injection, which threatens the integrity of agent systems by leveraging external data to manipulate LLMs. The paper outlines a framework, dubbed AgentXploit, that systematically discovers and exploits these vulnerabilities in black-box settings.

Framework Overview

AgentXploit operates as a fuzzing framework designed to automate the identification of indirect prompt injection vulnerabilities in LLM agents. It is structured around a classical fuzzing methodology, employing a genetic approach to refine input prompts continually. The framework comprises several key components:

1. Initial Seed Corpus: This is a foundation of high-quality prompts, collected from various sources, serving as a starting point for the fuzzing process.
2. Seed Selection and Mutation: Utilizing Monte Carlo Tree Search (MCTS), the framework dynamically selects potent seeds and applies mutations to explore diverse input variations, enhancing both exploration and exploitation capabilities.
3. Scoring Strategies: The framework employs an adaptive scoring mechanism that considers the success rate of attack attempts and coverage expansion over new vulnerabilities.

Experimental Evaluation

The effectiveness of AgentXploit is evaluated through its application to two benchmarks: AgentDojo and VWA-adv. In both cases, the framework demonstrates significant improvements over baseline methods, evidenced by:

• Achieving success rates of 71% on AgentDojo and 70% on VWA-adv, which nearly double the baseline performance.
• Illustrating strong transferability of its adversarial prompts across different tasks and LLMs, maintaining robust success rates even with unseen tasks and internal models such as o3-mini and GPT-4o.
• Highlighting its efficacy against standard defenses, suggesting potential inadequacies in the current protective mechanisms employed by LLM agents.

Implications and Future Directions

AgentXploit provides substantial insights into the vulnerabilities inherent within LLM agents, specifically underlining the threats posed by indirect prompt injections. This work has crucial practical implications, highlighting the need for developing more sophisticated defenses that can withstand these automated, adaptive attacks.

From a theoretical perspective, AgentXploit serves as a pivotal tool for future research in AI security. It encourages the exploration of more robust modeling techniques and enhanced defensive architectures capable of mitigating indirect prompt injections. As LLM applications become increasingly prevalent, the methodologies advanced by AgentXploit will likely inform the development of more resilient AI systems, ensuring the safe deployment of LLM agents in various domains.

The paper opens avenues for future developments in AI security, particularly the refinement of red teaming methodologies and their application to a broader spectrum of AI models. Further research could expand on the adaptability of AgentXploit to other AI frameworks, providing a comprehensive toolkit for preemptively addressing security vulnerabilities in AI technologies.