Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous Attacks

TL;DR

This paper reveals that commercial and open-source LLM-based agents are highly vulnerable to simple, practical attacks that can compromise security and privacy, highlighting urgent need for improved defenses.

Contribution

It provides a comprehensive taxonomy of vulnerabilities in LLM agents and demonstrates that these attacks are easy to execute without ML expertise.

Findings

Attacks on LLM agents are trivial to implement.

Vulnerabilities exist across various agent components.

Practical attacks can compromise privacy and security.

Abstract

A high volume of recent ML security literature focuses on attacks against aligned large language models (LLMs). These attacks may extract private information or coerce the model into producing harmful outputs. In real-world deployments, LLMs are often part of a larger agentic pipeline including memory systems, retrieval, web access, and API calling. Such additional components introduce vulnerabilities that make these LLM-powered agents much easier to attack than isolated LLMs, yet relatively little work focuses on the security of LLM agents. In this paper, we analyze security and privacy vulnerabilities that are unique to LLM agents. We first provide a taxonomy of attacks categorized by threat actors, objectives, entry points, attacker observability, attack strategies, and inherent vulnerabilities of agent pipelines. We then conduct a series of illustrative attacks on popular open-source and commercial agents, demonstrating the immediate practical implications of their vulnerabilities. Notably, our attacks are trivial to implement and require no understanding of machine learning.