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Security of AI Agents

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arxiv 2406.08689 v3 pith:T3GKG2GL submitted 2024-06-12 cs.CR cs.AI

classification cs.CRcs.AI
keywords agentssecurityvulnerabilitiesabilityaccessaddressedaimedaltogether
verification ladder T0 review T1 audit T2 compute T3 formal
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AI agents have been boosted by large language models. AI agents can function as intelligent assistants and complete tasks on behalf of their users with access to tools and the ability to execute commands in their environments. Through studying and experiencing the workflow of typical AI agents, we have raised several concerns regarding their security. These potential vulnerabilities are not addressed by the frameworks used to build the agents, nor by research aimed at improving the agents. In this paper, we identify and describe these vulnerabilities in detail from a system security perspective, emphasizing their causes and severe effects. Furthermore, we introduce defense mechanisms corresponding to each vulnerability with design and experiments to evaluate their viability. Altogether, this paper contextualizes the security issues in the current development of AI agents and delineates methods to make AI agents safer and more reliable.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents

    cs.AI 2025-06 conditional novelty 4.0 of 10

    The paper surveys security risks of LLM agents, organizes them into a five-level autonomy taxonomy, and proposes an untested CMDP-based architecture called R2A2.

  2. A Survey on Agent Workflow -- Status and Future

    cs.AI 2025-08 conditional novelty 3.0 of 10

    A review that classifies 24 agent workflow systems along functional and architectural axes and argues for standardization, optimization, and security work.

  3. Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact

    cs.AI 2025-07 conditional novelty 2.0 of 10

    A broad survey arguing that AGI requires modular, memory-augmented, embodied architectures rather than scaled-up token prediction, with a brief proposal to decompose intelligence into five components.

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