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24/07/2026 4:09 pm
TL;DR: Multi-agent systems create a trust gap at handoff points, where downstream agents cannot reliably verify whether instructions came from a legitimate peer or a compromised orchestrator, according to Noma Security. The missing control is not just least privilege for tasks, but verifiable delegation, because trust assumptions collapse when agents act through chains rather than direct human authorization.
NHIMG editorial — based on content published by Noma Security: inter-agent trust in multi-agent AI systems
Questions worth separating out
Q: How should security teams govern AI agents that move across multiple trust boundaries?
A: They need runtime controls that follow the agent rather than staying attached to one platform.
Q: Why do multi agent systems create more identity risk than single AI assistants?
A: Multi agent systems create more identity risk because they combine delegation, shared context, and external communication across multiple execution steps.
Q: What do teams get wrong about least privilege for AI agents?
A: They often stop at permission scope and ignore behavioural scope.
Practitioner guidance
- Map every agent delegation chain Document which orchestrators can task which sub-agents, under what identity, and with what inherited scope.
- Issue task-bound credentials for sub-agents Do not copy orchestrator permissions into downstream agents.
- Require delegation-aware audit logging Make sure logs show the sender, receiver, scoped credential, task ID, and whether the instruction was verified or trusted.
What's in the full article
Noma Security's full post covers the operational detail this analysis intentionally leaves for the source:
- A deeper walkthrough of multi-agent delegation patterns, including orchestrator and sub-agent roles in practice.
- The specific inter-agent trust protocol concepts and implementation examples discussed by the vendor.
- More detail on how current frameworks such as LangGraph, CrewAI, Copilot Studio, and AgentForce differ in default trust handling.
- A fuller discussion of runtime guardrails, including scoped delegation and audit trail design for agent chains.
👉 Read Noma Security's analysis of inter-agent trust in multi-agent AI systems →
Multi-agent trust boundaries in AI systems: what breaks first?
Explore further
24/07/2026 4:14 pm
Inter-agent trust is the new identity control plane: Multi-agent systems do not fail only because an agent is over-permissioned. They fail when downstream agents accept upstream instructions without being able to validate their legitimacy, which turns delegation into an unverified trust channel. That is a governance problem, not just a prompt-security problem. The implication is that agent identity must be enforced at the handoff, not only at the session boundary.
A few things that frame the scale:
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
- Only 44% of organisations have implemented any policies to manage their AI agents, even though 92% agree that governing AI agents is critical to enterprise security.
A question worth separating out:
Q: Who is accountable when a compromised AI agent misuses delegated access?
A: Accountability usually spans the business owner of the workflow, the team that issued or approved the credential, and the vendor if a third-party integration was involved. The critical governance question is not who logged in, but who allowed the delegation chain to exist and remain valid. That chain must be documented before incidents occur.
👉 Read our full editorial: Inter-agent trust is the missing control in multi-agent AI systems
