2026/05/26 by Kevin Eykholt, Dhilung Kirat, Xiaokui Shu +3 · 1 voice
Computer Science · #Autonomy #Coding (social sciences) #Information and Cyber Security #Multi-agent system #Penetration (warfare) #Security and Verification in Computing #Security controls #Security testing #Web Application Security Vulnerabilities #cs.AI #cs.CR
paper · pdf · doi:10.48550/arxiv.2605.27042
openalex publication_date 2026/05/26 · arxiv published 2026/05/26 · arxiv updated 2026/05/26 · openalex created_date 2026/05/28 · openalex updated_date 2026/07/28
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.