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21/08/2026 4:45 pm
TL;DR: Multi-agent systems create a governance gap because downstream agents often trust instructions that cannot be independently verified, according to Noma Security's analysis. The real failure is not prompt injection alone but the absence of cryptographic identity, scoped delegation, and auditability across agent-to-agent handoffs, which leaves standing privilege moving through the chain.
NHIMG editorial — based on content published by Noma Security: Your Agents Are Trusting Each Other - Should They?
By the numbers:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security.
- 69% of security leaders agree identity management must fundamentally shift to address agentic 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 security risk than single-agent systems?
A: They introduce more trust relationships, more message paths, and more shared state that can be poisoned or misused.
Q: What do organisations get wrong about audit logs for agentic workflows?
A: They often log the final action but not the delegation chain that led to it.
Practitioner guidance
- Map delegation chains before scaling multi-agent workloads Document which agents can pass tasks to which other agents, under what identity, and with what inherited scope.
- Issue task-scoped credentials for every handoff Replace copied session permissions with just-in-time, expiring credentials bound to a single delegated task.
- Require provenance-rich audit logging Log the delegating agent identity, receiving agent identity, scoped credential, correlation ID, and verification state for every inter-agent action.
What's in the full article
Noma Security's full blog post covers the operational detail this post intentionally leaves for the source:
- Step-by-step examples of how inter-agent trust breaks across orchestration and sub-agent handoffs
- The specific threat model behind poisoned instructions, trust propagation, and chain-wide blast radius
- Architectural guidance on cryptographic agent identity and dynamic trust scoring
- Implementation context for multi-agent frameworks that do not enforce inter-agent authentication by default
👉 Read Noma Security's analysis of inter-agent trust in multi-agent systems →
Multi-agent trust boundaries: are your controls keeping up?
Explore further
21/08/2026 5:05 pm
Inter-agent trust is now an identity problem, not just an AI architecture problem. The security failure is not simply that agents can do useful work. It is that a downstream agent may accept an instruction without any independent proof that the upstream agent was authorised to issue it. That breaks the core IAM assumption that authorization can be validated at the point of action. Practitioners should treat delegation trust as a first-class governance domain.
A few things that frame the scale:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to The 2026 Infrastructure Identity Survey.
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to The 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: Who is accountable when an AI agent delegation chain causes an unauthorised action?
A: Accountability depends on whether the organisation can reconstruct the chain. If logs capture sender identity, recipient identity, task ID, delegation depth, and validation results, investigators can pinpoint the failing handoff. Without that trail, responsibility becomes ambiguous across the orchestration layer and the individual agents.
👉 Read our full editorial: Inter-agent trust is the missing control in multi-agent governance
