Lessons from Penetration Tests on Large-Scale Agent Systems

2026-05-26

AI Summary

This study investigates whether proprietary AI agent systems adhering to rigorous development standards remain vulnerable to cross-layer security flaws akin to those observed in open-source counterparts. Conducting the first systematic penetration tests on two execution-capable proprietary agent platforms released in 2025, the research combines in-depth analysis of their execution behaviors, self-modification mechanisms, and interactions across multi-layered computational stacks. Findings reveal that, despite formal coding standards and review processes, these systems exhibit pervasive security weaknesses consistent with known vulnerability classes. This demonstrates that current engineering practices have yet to effectively mitigate the inherent security risks of large-scale AI agents, underscoring both the universality and urgency of cross-architectural security threats in autonomous agent ecosystems.

Abstract

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 contrast, it remains unclear whether proprietary agent systems -- developed under stricter coding standards and formal review processes -- exhibit similar security weaknesses. In this paper, we present findings from two penetration tests conducted in 2025 against proprietary agent products and evaluate whether the security posture of AI agents has improved since these assessments.

Problem

• AI agent security
• proprietary systems
• penetration testing
• vulnerability assessment
• execution-capable agents

Innovation

• penetration testing
• proprietary agent systems
• cross-layer security
• AI agent vulnerabilities
• execution-capable agents