Why do borrowed credentials make AI agents a higher-risk identity class?

Why borrowed credentials change the identity model for AI agents

Borrowed credentials turn an agent from a system that acts under its own bounded access into one that can inherit someone else’s authority. That matters because the credential is now the trust boundary, not the agent process itself. If the token is reused across tasks, the agent can keep acting long after the approving human has moved on, which weakens containment and attribution.

That is why delegated access for agents needs tighter rules than ordinary application sessions. A normal session is usually expected to represent one user journey, but an agent often executes several steps, follows up on failures, and reuses whatever authority it already has. The result is a broader blast radius when the borrowed credential is too powerful, too persistent, or too easy to pass between tools.

Borrowed credentials also change the identity lifecycle. They may be issued for one intent, consumed in another context, and then remain usable without a clean offboarding event. In practice, that creates a mismatch between the agent’s operating span and the credential’s lifespan, especially when the agent can refresh, cache, or forward the token as part of its workflow.

Why the risk is larger than with a normal application session

Borrowed credentials expand the risk because the agent can act repeatedly without fresh human review, and each additional action inherits the original grant. The exposure is highest when the credential is long-lived, broadly scoped, or usable in environments the approver never intended. In that state, a single approval can quietly become a multi-action delegation chain.

The practical difference is that an agent does not just use access, it can operationalize it. That means one compromised token can support data access, administrative changes, API calls, or chained tool use before anyone notices. When the control layer cannot see each delegated action clearly, the organisation loses both least privilege and reliable accountability.

This is also why borrowed credentials are more dangerous when they cross boundaries between systems. If a token issued in one context can reach another tool, workspace, or service, the agent may be able to act far outside the original approval context. AI Agent Authorisation Guide is useful here because it frames the need for per-action policy, task-scoped access, and explicit approval gates.

What good control looks like for delegated agent access

The control objective is not to ban borrowed credentials, it is to constrain them so the agent cannot silently become a proxy for durable human privilege. Good designs tie the token to a narrow task, a short lifetime, and a clear policy decision for each meaningful action. They also make sure the agent can be observed as an actor in its own right, rather than disappearing behind an opaque user session.

For practitioners, the most important design choice is whether access is granted once or checked continuously. If the token can be reused across many actions, you should treat that as a higher-risk delegation pattern and require stronger scoping, shorter expiry, and stronger logging. Zero Trust for AI Agents supports that operating model by emphasising verification, no standing privilege, and policy per action.

Borrowed credentials also demand sharper lifecycle discipline. If the agent can retain a credential beyond the original task, then revocation, expiry, and traceability become as important as initial approval. Agentic AI Identity Guide is relevant because it treats delegation, registration, and retirement as part of the identity lifecycle, not as afterthoughts.

Risk and Threat Considerations

Borrowed credentials create a trust-abuse problem as much as an access problem. If an attacker, malicious prompt, or misrouted workflow can reach the delegated token, the agent may inherit authority that is hard to distinguish from legitimate use. That makes token theft, token replay, and over-broad delegation especially damaging in agentic environments.

Failure mechanism: The credential outlives the approval intent, is reusable across multiple actions, or is shared into a tool path that was never meant to hold that level of authority.

Impact: The agent can perform unauthorized actions at speed, expand the blast radius of a single compromise, and make post-incident attribution harder because the borrowed authority looks like legitimate delegated use.

Practitioner Guidance

What to verify: Check whether the agent’s credential is task-bound, short-lived, and non-transferable across tools or sessions. If you cannot point to a specific expiry, scope boundary, and revocation path, treat the delegation as too permissive for production use.

Common mistake: Teams often review the initial approval but not the actions that follow. For agents, the risk is usually in reuse, so the important question is whether each sensitive action still requires fresh policy enforcement or whether one borrowed token can silently carry the whole workflow.

Practitioner takeaway: The key control question is not whether an agent may borrow a credential, but whether that borrowed authority stays narrow, observable, and easy to kill once the original task is complete.

Related resources from NHI Mgmt Group

• Why do AI agents make non-human identity governance harder?
• Why do AI agents create new risk in non-human identity management?
• Why do AI agents increase non-human identity risk in existing IAM programmes?
• Why do AI agents create more risk when they reuse existing credentials?