LLM Credential Governance

LLM credential governance is the set of controls used to manage how large language model credentials are issued, stored, used, rotated, and revoked. It focuses on reducing leakage risk, limiting standing access, and ensuring authentication paths are auditable across agents, gateways, and enterprise identity systems.

Expanded Definition

LLM credential governance is the control layer that determines who or what can obtain, present, and refresh credentials used by large language models, tool-using agents, and orchestration gateways. It goes beyond simple secret storage by tying issuance and revocation to identity context, workload purpose, and auditability. In practice, this means treating model API keys, service tokens, OAuth grants, and delegated access as governed NHI assets rather than static configuration values. Guidance across vendors is still evolving, but the common pattern aligns with the OWASP Non-Human Identity Top 10 and the OWASP Top 10 for Agentic Applications 2026, which both emphasise limiting standing access and preventing credential abuse inside autonomous workflows. It also aligns with the identity assurance discipline in the NIST AI Risk Management Framework. The most common misapplication is treating an LLM credential as a one-time deployment secret, which occurs when teams hard-code tokens into prompts, notebooks, CI pipelines, or agent configs without rotation or scope limits.

Examples and Use Cases

Implementing LLM credential governance rigorously often introduces more provisioning and review overhead, requiring organisations to weigh fast automation against tighter control of tool access and leakage risk.

• An enterprise AI gateway issues short-lived tokens to each agent session and revokes them when the task ends, instead of letting the model reuse a long-lived key.
• A platform team stores model-provider API keys in a secrets manager, rotates them on schedule, and binds each key to a specific environment and workload identity.
• A security team reviews the access paths described in the AI Agents: The New Attack Surface report to detect when agents can access data outside their intended scope.
• A developer workflow replaces embedded credentials with federated access and policy checks, following the risk patterns highlighted in the LLMjacking: How Attackers Hijack AI Using Compromised NHIs research.
• A red-team exercise tests whether an agent can inherit a user session and escalate into broader enterprise systems without explicit reauthorization, reflecting controls discussed in the OWASP Agentic AI Top 10.

These use cases are most effective when paired with identity-aware boundaries from NIST Cybersecurity Framework 2.0 and with the NHI-specific controls described in the OWASP NHI Top 10.

Why It Matters in NHI Security

LLM credentials are high-value attack targets because they often unlock inference services, tool execution, data retrieval, and downstream automation. When governance is weak, a stolen token can become an entry point for model abuse, data exfiltration, and silent lateral movement across agent workflows. NHIMG research shows how quickly exposed credentials can be abused: in the LLMjacking findings, attackers attempted access to exposed AWS credentials in an average of 17 minutes. That urgency is why credential lifetime, scope, and telemetry matter as much as storage location. The same logic appears in the Moltbook AI agent keys breach, where exposed keys created direct operational risk, and in the DeepSeek breach, where sensitive records and credentials were exposed at scale. Organisations typically encounter the operational cost of poor LLM credential governance only after an exposed key, rogue agent action, or compliance investigation forces emergency rotation and access reconstruction.

Controls for this term also map cleanly to NIST AI 600-1 Generative AI Profile and the CSA MAESTRO agentic AI threat modeling framework, both of which reinforce traceable, bounded, and revocable access.

Related resources from NHI Mgmt Group

• Cross-Environment Governance
• Service Account Governance
• Should organisations prioritise external exposure or internal credential governance first?
• How can organisations apply credential stuffing lessons to NHI governance?