Crew Scaler Releases Landmark Agentic AI Security Study for Multi-Agent Systems

Crew Scaler, an AI-first nonprofit focused on safe and secure AI adoption, has released one of the first book-length security analyses of multi-agent “agentic AI” systems. The new paper, “Security Considerations for Multi-Agent Systems,” outlines concrete risks and countermeasures for organizations deploying multiple autonomous AI agents in production environments.

Unlike traditional chatbots, agentic AI systems do not just answer questions — they plan, delegate, use tools, retain memory, and coordinate across workflows. “Agentic AI is where many organizations expect their real productivity gains to come from — but those same systems introduce whole new failure modes,” said Tam Nguyen, CEO of Crew Scaler and a Senior AI and security expert in the U.S. government. “Our goal with this research is to give security teams, architects, and policymakers a practical map of the risks, not just abstract principles, so they can move forward with confidence instead of guesswork.”

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In the study, Crew Scaler researchers evaluated 16 security and risk management frameworks against more than 1,000 distinct multi-agent risk items across nine categories. The findings are clear: significant gaps remain. The conclusion: traditional AI safety checklists are necessary but not sufficient for multi-agent systems.

The paper translates its analysis into practical recommendations for any organization deploying multi-agent systems, including but not limited to: minimal tool authority needed for each task; segmenting memory by workflow, team, or tenant; treating inter-agent messages as untrusted input; monitoring for non-deterministic behavior and unexpected tool chains; preventing data leakage with strict access controls; and combining multiple security frameworks rather than relying on one standard.