Lessons from Penetration Tests on Large-Scale Agent Systems
As AI systems gain increasing autonomy and execution capability, the number of discovered security vulnerabilities continues to rise. However, many of these vulnerabilities are not fundamentally novel, but instead reflect recurring classes of weaknesses long observed in prior computing systems. Execution-capable AI agents are effectively unbounded, self-modifying programs that interact extensively with multiple layers of the computing stack. This broad interaction surface imposes a significant security burden on developers, who must reason about and secure complex cross-layer behaviors. Prior research has primarily focused on vulnerabilities in open-source agents and agent frameworks. In ...
| Authors | Kevin Eykholt, Dhilung Kirat, Xiaokui Shu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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