AI Agent Tools and Frameworks
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Hammond et al. catalog multi-agent risks and MAE-STRO provides an operational taxonomy of known threats in deployed systems, whereas this paper provides threat-modeling scaffolding, research-agenda structure, and governance framework needed to anticipate threats that are structurally enabled but not yet observed.
“Where Hammond et al. (2025) catalog multi-agent risks and MAE-STRO (Huang, 2025;Huang & Huang, 2025) provides an operational taxonomy of known threats in deployed systems, we provide the threat-modeling scaffolding, research-agenda structure, and governance framework needed to anticipate threats that are structurally enabled but not yet observed.”
From Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents (2025) · cites this paper as Huang, 2025
The MAESTRO framework provides a valuable practical taxonomy of known operational threats in multi-agent environments, as evidenced by OWASP's multi-agent threat modeling guide.
“The MAESTRO framework (Huang, 2025;Huang & Huang, 2025) provides a valuable practical taxonomy of known operational threats in multi-agent environments, as evidenced by OWASP's multi-agent threat modeling guide (OWASP Foundation, 2024).”
From Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents (2025) · cites this paper as Huang, 2025
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Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents
de Witt, C. S., Krawiecka, K., Krawczuk, I., Hagag, B., Anderson, W. L., Belcak, P., Bucknall, B., Cai, X., Chopra, A., Cohen, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2505.02077 · Cited by 2 · see citing works
- Authors
- Huang, K., & Huang, J.
- Affiliations
- University of Chicago (US)
- Corresponding author
- Huang, Ken
- Venue
- Progress in IS
- Publisher
- Springer Nature Switzerland
- Published
- 2025
- Pages
- 23-50
- ISSN
- 2196-8705 (ISSN-L)
- 2196-8713
- OpenAlex
- W4411151922
- Cited by
- 7
- References
- 0
- Citations by year
- 2026: 3 · 2025: 4
- FWCI
- 14.67
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- Locations
- Progress in IS (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Keywords
- AI agents, AI agent architecture, AI agent frameworks, AutoGen, LangGraph, LlamaIndex, foundation models, AI agent security, AI compliance, scalability, data quality
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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