When Delegation Goes Wrong: The Hidden Vulnerabilities of Autonomous AI Agents

Abstract: Autonomous AI agents—language-model–powered systems with tool access, persistent memory, and multi-channel communication—represent a fundamental shift from assistive chatbots to systems that execute real-world actions. This article examines emerging security, privacy, and governance vulnerabilities revealed through a two-week adversarial evaluation involving twenty AI researchers interacting with deployed agents in laboratory conditions. Observed failure modes include unauthorized compliance with non-owner instructions, disproportionate responses to benign requests, sensitive information disclosure, denial-of-service vulnerabilities, identity spoofing across communication channels, and cross-agent propagation of unsafe behaviors. These patterns expose systemic limitations in current agentic architectures: the absence of robust stakeholder models, insufficient self-monitoring capabilities, and failures of social coherence when agents must navigate competing authorities and contextual privacy boundaries. Drawing on cybersecurity red-teaming methodologies, alignment research, and behavioral ethics frameworks, this analysis identifies both contingent engineering gaps and fundamental architectural challenges. The findings establish urgent priorities for practitioners deploying autonomous systems and highlight unresolved questions regarding accountability, delegated authority, and responsibility assignment when AI agents cause downstream harm.

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