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Security Considerations for Multi-agent Systems

arXiv cs.AI / 3/11/2026

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Key Points

  • Multi-agent artificial intelligence systems (MAS) consist of autonomous agents that communicate and share memory, leading to unique security vulnerabilities distinct from single AI models.
  • This study evaluates 16 existing AI security frameworks against MAS-specific cybersecurity threats, finding that none comprehensively cover all risk categories.
  • The research identifies 193 distinct security risks across nine categories, with Non-Determinism and Data Leakage being the most under-addressed issues.
  • OWASP Agentic Security Initiative and CDAO Generative AI Responsible AI Toolkit show the highest coverage in design and operational phases, respectively.
  • The study provides the first empirical cross-framework comparison for MAS security and delivers evidence-based recommendations for selecting appropriate security frameworks for multi-agent systems.

Computer Science > Cryptography and Security

arXiv:2603.09002 (cs)
[Submitted on 9 Mar 2026]

Title:Security Considerations for Multi-agent Systems

View a PDF of the paper titled Security Considerations for Multi-agent Systems, by Tam Nguyen and 2 other authors
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Abstract:Multi-agent artificial intelligence systems or MAS are systems of autonomous agents that exercise delegated tool authority, share persistent memory, and coordinate via inter-agent communication. MAS introduces qualitatively distinct security vulnerabilities from those documented for singular AI models. Existing security and governance frameworks were not designed for these emerging attack surfaces. This study systematically characterizes the threat landscape of MAS and quantitatively evaluates 16 security frameworks for AI against it. A four-phase methodology is proposed: constructing a deep technical knowledge base of production multi-agent architectures; conducting generative AI-assisted threat modeling scoped to MAS cybersecurity risks and validated by domain experts; structuring survey plans at individual-threat granularity; and scoring each framework on a three-point scale against the cybersecurity risks. The risks were organized into 193 distinct main threat items across nine risk categories. The expected minimal average score is 2. No reviewed framework achieves majority coverage of any single category. Non-Determinism (mean score 1.231 across all 16 frameworks) and Data Leakage (1.340) are the most under-addressed domains. The OWASP Agentic Security Initiative leads overall at 65.3\% coverage and in the design phase; the CDAO Generative AI Responsible AI Toolkit leads in development and operational coverage. These results provide the first empirical cross-framework comparison for MAS security and offer evidence-based guidance for framework selection.
Comments:
Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
ACM classes: I.2.11; K.6.5; I.2.7; C.2.4; D.4.6
Cite as: arXiv:2603.09002 [cs.CR]
  (or arXiv:2603.09002v1 [cs.CR] for this version)
  https://doi.org/10.48550/arXiv.2603.09002
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arXiv-issued DOI via DataCite

Submission history

From: Tam N Nguyen [view email]
[v1] Mon, 9 Mar 2026 22:46:27 UTC (304 KB)
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