Security teams should make the agent’s effective permissions the intersection of the requester’s entitlements and the agent’s own scope. If the human cannot perform the action, the agent should not be able to do it on their behalf. That approach blocks confused-deputy escalation and keeps delegated access bounded by the original requester.
Why This Matters for Security Teams
The core risk is not just access misuse, but delegated access turning into an unintended privilege bridge. When a channel member asks an AI agent to act, the agent can become a confused deputy if it is allowed to use its own broader scope instead of the requester’s actual entitlements. That is why security teams should treat agent-mediated actions as bounded delegation, not as a shortcut around access controls.
This issue is already visible in real deployments. NHIMG’s AI LLM hijack breach research shows how prompt and tool abuse can redirect an agent into actions that were never intended by the human operator. OWASP’s OWASP Top 10 for Agentic Applications 2026 similarly flags over-broad tool use and authorization gaps as structural risks, not edge cases.
In practice, many security teams encounter the breakout only after an agent has already fetched data, chained tools, or crossed a boundary that the user could never reach directly.
How It Works in Practice
The practical control is to evaluate every agent action against two gates at the same time: what the human requester is allowed to do and what the agent is allowed to do. The effective permission is the intersection of both. If either side lacks authorization, the request fails. This prevents the agent from acting as a privilege amplifier while still allowing legitimate delegation.
That model works best when it is enforced at runtime, not inferred from static roles. Static RBAC is too blunt for autonomous or semi-autonomous workflows because the agent may choose a path that was not predeclared. Current guidance suggests using policy checks that can inspect requester identity, task context, resource sensitivity, and tool intent on each call. NIST’s NIST AI Risk Management Framework is useful here because it emphasizes governance, mapping, and monitoring rather than assuming fixed behaviour.
A strong implementation usually includes:
- Per-request authorization on every tool call, not just at login.
- Short-lived, task-bound credentials for the agent, revoked when the task ends.
- Workload identity for the agent so the system knows what the agent is, not just what secret it holds.
- Policy-as-code so the decision is made with full context, including user entitlements and resource classification.
NHIMG’s State of Non-Human Identity Security research underscores why this matters operationally: over-privileged accounts and poor monitoring remain common attack drivers, which is exactly the condition that lets delegated access drift into unauthorized reach. These controls tend to break down when an agent can combine multiple low-risk tools into a higher-risk workflow because the policy engine only checks each step in isolation.
Common Variations and Edge Cases
Tighter delegation control often increases friction, requiring organisations to balance user convenience against abuse resistance. That tradeoff is especially visible in chat platforms, internal copilots, and workflow automations where users expect the agent to “just do it” across multiple systems.
One common edge case is shared channel context. A channel member may appear to have conversational visibility, but that does not mean the agent may act on content as though every participant has the same permissions. Another is service-to-service chaining, where an agent can legally call one internal API and then use the result to infer or reach a second resource that should remain hidden. There is no universal standard for this yet, so best practice is evolving toward explicit intent-based authorization and strong resource scoping.
For agentic environments, the safer pattern is to bind the agent’s workload identity to narrowly scoped, ephemeral credentials and to enforce denial by default when the requester cannot directly access the target resource. OWASP’s OWASP Agentic Applications Top 10 and CSA’s CSA MAESTRO agentic AI threat modeling framework both support this direction by focusing on runtime trust, tool constraints, and abuse-resistant orchestration. The model becomes less reliable when agents are allowed to inherit broad human session tokens or when downstream services trust the agent more than the original requester.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A1 | Authorization gaps and tool abuse are core agentic-ai risks. |
| CSA MAESTRO | T3 | MAESTRO addresses agent orchestration and privilege boundaries. |
| NIST AI RMF | GOVERN | AI RMF governance supports accountability for delegated agent actions. |
Constrain each tool call by requester entitlements and agent scope at runtime.
