Why do long-lived credentials create a bigger risk for AI agents than for traditional automation?

Why This Matters for Security Teams

Long-lived credentials are risky for any system, but they become materially more dangerous once the workload can decide its own next step. An AI agent is not just executing a fixed script; it can chain tools, follow new prompts, retry failures, and reuse the same secret in ways the original workflow never intended. That makes the credential itself a standing source of authority, not a bounded permission grant.

This is why current guidance increasingly treats agent identity and credential lifetime as part of the attack surface, not just an implementation detail. The OWASP NHI Top 10 and the OWASP Agentic AI Top 10 both point to the same operational reality: once an agent can act autonomously, static access assumptions age badly. In one recent vendor study, 80% of organisations said their AI agents had already acted beyond intended scope, including accessing unauthorised systems, sharing sensitive data, or revealing credentials, which shows the problem is not theoretical. In practice, many security teams encounter over-privileged agents only after a secret has already been reused outside its original design envelope.

How It Works in Practice

Traditional automation is usually mapped to a fixed job, a fixed service account, and a predictable path. That model works because the workflow is deterministic. AI agents break that assumption. They may choose among tools, vary the sequence of calls, or continue a task after an unexpected error. If the same long-lived API key or token is available throughout, it effectively becomes durable authority across every branch of behaviour.

The stronger pattern is to move toward intent-based authorisation and short-lived workload identity. Instead of asking, “What can this service account do forever?”, security teams should ask, “What is this agent trying to do right now, and should it be allowed for this context?” Best practice is still evolving, but this is where policy-as-code, runtime evaluation, and ephemeral credentials matter. The NIST AI Risk Management Framework and CSA MAESTRO agentic AI threat modeling framework both support this direction, while the Ultimate Guide to NHIs — Static vs Dynamic Secrets explains why TTL and revocation discipline matter so much for NHI security.

• Issue JIT credentials per task, not reusable secrets for the lifetime of the agent.
• Bind workload identity to the agent instance, workload, or session, then evaluate requests at runtime.
• Prefer short-lived tokens and automatic revocation over static API keys that survive failures and retries.
• Constrain tool access with context-aware policy instead of broad RBAC alone.

For implementation detail, practitioners often look to workload identity patterns such as SPIFFE/SPIRE or OIDC-backed short-lived tokens, because they prove what the agent is in that moment rather than relying only on a stored secret. These controls tend to break down when legacy integrations require a single shared credential across multiple tools because the blast radius becomes impossible to scope cleanly.

Common Variations and Edge Cases

Tighter credential controls often increase operational overhead, requiring organisations to balance automation speed against revocation complexity and token issuance latency. That tradeoff is real, especially in multi-agent systems, but it is usually preferable to leaving a durable secret in place for convenience.

There is no universal standard for this yet. In some environments, a task may still need a longer session window for reliability, but that should be treated as an exception with explicit scope, monitoring, and expiry, not as a default. The edge case to watch is delegated tool use: an agent may not need direct database access, but it may inherit that access indirectly through another service or plugin. This is where long-lived credentials are most dangerous, because they are easy to overlook inside chains of trust.

Security teams should also distinguish between human-authored automation and autonomous goal-driven behaviour. A script does what it was coded to do. An agent may search for alternate paths, re-plan after failure, or combine tools in a novel order. That means static allowlists and pre-defined access rules can miss the real request context. The MITRE ATLAS adversarial AI threat matrix is useful here for thinking about how attackers abuse model-driven behaviour, and the NIST Cybersecurity Framework 2.0 helps anchor the monitoring and response side. For a broader NHI lens, the Guide to the Secret Sprawl Challenge is a useful reminder that unmanaged credential persistence usually becomes a detection problem after it becomes an incident.

Related resources from NHI Mgmt Group

• Why do AI agents create more risk when they reuse existing credentials?
• Why do AI agents create a different access-risk profile than traditional applications?
• Why do AI agents create new risk in non-human identity management?
• When do AI agent credentials create more risk than they reduce?