Look for three signals. First, the original request and response shapes should remain intact, including streaming behaviour and correlation headers. Second, authentication and authorization should happen before forwarding, with no credential exposure to the workload layer. Third, traces and cost records should resolve back to the model, user, and team without manual reconciliation.
Why This Matters for Security Teams
ai gateway passthrough only works if it preserves the provider’s native behaviour while still enforcing local control. That balance matters because teams often adopt passthrough to avoid breaking streaming, tool calls, or provider-specific headers, yet they still need authentication, authorisation, logging, and cost attribution. When passthrough is too “transparent,” it becomes a blind spot; when it is too opinionated, it breaks the model workflow the business is paying for. That tension is why NIST’s Cybersecurity Framework 2.0 remains useful as a control lens, even when the implementation is AI-specific.
For NHI and agentic AI teams, the real question is not whether the gateway forwards traffic. It is whether it still preserves identity boundaries, request integrity, and operational traceability after forwarding. The same lesson appears in incidents like DeepSeek breach and the JetBrains GitHub plugin token exposure, where control failure was not just about access, but about how secrets and telemetry moved through the system. In practice, many security teams discover passthrough weaknesses only after a provider outage, a billing dispute, or a leakage event has already exposed the gap.
How It Works in Practice
Teams usually validate passthrough in three layers: protocol fidelity, policy enforcement, and observability. Protocol fidelity means the gateway must preserve the original request and response shape, including streaming behaviour, tool call framing, and any correlation or idempotency headers the provider expects. Policy enforcement means authentication and authorisation happen at the gateway before the request reaches the workload layer, so no raw provider credential ever lands in application code or agent runtime. Observability means every request can still be traced back to the user, workload, model, and team without manual spreadsheet work.
That control pattern is consistent with the direction of NHIMG’s standards guidance for NHI governance, and it aligns with runtime control ideas in the NIST Cybersecurity Framework 2.0. In practice, security teams test passthrough with a few checks:
- Compare upstream and downstream payloads to confirm no schema drift, dropped headers, or broken streaming chunks.
- Verify the gateway can deny or throttle a request before any provider call is made.
- Confirm the workload uses short-lived identity or delegated tokens, not long-lived static API keys.
- Check that logs, traces, and cost events join cleanly on the same request identity.
For higher-risk environments, current guidance suggests pairing passthrough with secret minimisation and rapid revocation workflows, especially where credential misuse can spread quickly, as seen in LLMjacking: How Attackers Hijack AI Using Compromised NHIs. These controls tend to break down when multiple gateways, custom proxies, and provider-specific streaming quirks all sit between the user and the model, because attribution and header integrity become inconsistent across hops.
Common Variations and Edge Cases
Tighter gateway control often increases integration overhead, requiring organisations to balance provider fidelity against governance depth. That tradeoff is especially visible when teams use multiple model providers, custom tool chains, or assistant workflows that depend on provider-specific response formats. There is no universal standard for passthrough parity yet, so best practice is evolving rather than settled.
One common edge case is partial passthrough: the gateway forwards the model call unchanged, but rewrites headers, strips metadata, or normalises streaming responses. That may be acceptable if the provider contract is narrow, but it can break downstream observability or cache behaviour. Another is delegated authentication, where the gateway authenticates the user but then substitutes a shared service credential. That may preserve functionality, but it weakens accountability unless request-level identity is retained end to end.
Teams should also test billing and quota attribution separately from request success. A pass-through flow that returns the right answer can still fail operationally if cost records cannot be tied back to the initiating user or workload. For that reason, many practitioners treat telemetry fidelity as a control objective, not an afterthought. The State of Secrets in AppSec is a useful reminder that fragmented secret handling and weak developer practices often show up first in operational cracks, not in policy documents.
Related resources from NHI Mgmt Group
- How do security teams know whether an AI gateway is becoming a control plane risk?
- How do teams know whether an AI gateway is actually improving control?
- How do IAM teams know whether agentic AI is actually under control?
- How do teams know whether AI-assisted administration is staying within control boundaries?
