What is the difference between storing OAuth tokens yourself and using a relay proxy for agent API calls?

Why This Matters for Security Teams

The choice between self-storing OAuth tokens and using a relay proxy is really a choice about secret custody, blast radius, and auditability. If an agent or application ever receives a durable token, that system becomes part of the credential trust boundary and must be defended like any other secret-bearing service. That increases the impact of prompt injection, insecure logging, memory scraping, misconfiguration, and downstream reuse of the token outside its intended scope. Guidance from the OWASP Agentic AI Top 10 is useful here because agentic systems often fail at the boundary between tool execution and credential handling, not in the model itself.

A relay proxy reduces exposure by keeping the token out of agent code, but it does not remove risk. It shifts trust to the proxy, its policy enforcement, its logging path, and its service-to-service authentication. That matters when teams assume “the proxy has the token, therefore the system is safe” and stop reviewing identity, scopes, revocation, and egress controls. In practice, many security teams encounter token misuse only after an agent has already overreached its intended action scope or the secret has appeared in logs, retries, or support traces.

How It Works in Practice

When a system stores OAuth tokens itself, the application or agent is responsible for the full lifecycle: initial authorization, secure storage, refresh, rotation, revocation handling, and protection in memory and at rest. That can be acceptable in tightly controlled services, but it expands the number of places where secrets can leak. Any component that can call the API may also be able to replay the token unless scope, audience, and expiry are tightly constrained.

A relay proxy changes the architecture. The agent sends an API request to the proxy, the proxy authenticates the caller, attaches the OAuth credential server side, and forwards the request to the target service. This is often a better fit for agentic AI because the agent never needs direct access to the bearer token. The proxy can enforce policy, rate limits, per-action allowlists, and identity-to-action mapping before the request leaves the environment.

• Use self-storage only when the workload can safely manage secret handling, rotation, and audit logging.
• Use a relay proxy when you want to reduce secret exposure inside agent runtime, tool code, or browser-like automation paths.
• Bind proxy access to short-lived service identity, not a generic shared account.
• Log request intent and policy decisions, but avoid logging the token or full credential-bearing headers.
• Separate authorization of the caller from authorization of the downstream API action.

For governance and risk treatment, the NIST AI Risk Management Framework is a useful reference point because it pushes teams to document accountability, monitor misuse, and validate controls around AI-enabled actions rather than assuming the model layer is the only risk. These controls tend to break down when legacy applications require user-delegated OAuth flows but were never designed to separate end-user identity from machine action identity.

Common Variations and Edge Cases

Tighter secret separation often increases operational overhead, requiring organisations to balance reduced credential exposure against added proxy complexity and latency. That tradeoff is especially visible when the API provider uses long-lived refresh tokens, nonstandard consent flows, or fine-grained delegation that the proxy must reproduce faithfully.

There is no universal standard for every agent architecture yet. In some environments, a relay proxy is the safer default because it centralises policy, simplifies revocation, and prevents token sprawl. In others, direct token storage may be unavoidable for offline jobs, embedded clients, or systems that must impersonate the user across multiple services. Current guidance suggests treating those cases as exceptions that need explicit approval, stronger monitoring, and narrow scopes.

The edge cases are usually operational rather than theoretical. Shared proxies can become a single point of failure if they are over-permissioned or under-instrumented. Self-stored tokens can also be acceptable for low-risk read-only access, but only if the storage layer is hardened, refresh is isolated, and the token is never exposed to prompt text, telemetry, or analyst workflows. For broader threat modelling, the MITRE ATLAS adversarial AI threat matrix helps teams think about how an attacker might turn agent behaviour, tool access, and downstream credentials into abuse paths.

Related resources from NHI Mgmt Group

• What are the implications of using OAuth tokens in third-party integrations?
• What is the difference between OAuth tokens and API keys from a security perspective?
• What is the difference between API keys and OAuth for AI agent access?
• What is the difference between refresh token rotation and a grace window in OAuth providers?