This research reveals how LLM agents are vulnerable to attacks through their supporting components rather than just the core language models.

• Expanded attack surface: Components like memory systems, retrieval mechanisms, and API access create new vulnerabilities
• Practical demonstrations: Researchers successfully executed attacks against commercial and open-source LLM agents
• Novel taxonomy: Categorizes attack vectors specific to agent architectures beyond traditional prompt injection
• Real-world implications: Shows how attackers can compromise agent security with simpler methods than those needed against isolated LLMs

This work highlights urgent security concerns as organizations rapidly deploy LLM agents without adequate security measures, requiring immediate attention from both developers and enterprise security teams.

Commercial LLM Agents Are Already Vulnerable to Simple Yet Dangerous Attacks