Why do AI gateways create more risk than ordinary application proxies?

Why This Matters for Security Teams

AI gateways are not just traffic brokers. They often become the control point for model routing, prompt mediation, API key storage, and policy enforcement across multiple providers. That concentration of trust makes them materially different from an ordinary application proxy, which usually forwards requests without holding broad downstream authority. NIST’s NIST Cybersecurity Framework 2.0 treats identity, access, and governance as core security outcomes, and AI gateways sit directly inside that blast radius.

The risk is not theoretical. NHIMG research on LLMjacking shows attackers actively target exposed credentials and NHI pathways because they can move fast once a trust anchor is found. In practice, the gateway can become the shortest path from one compromised secret to many connected services, especially when it stores master credentials or can mint tokens on demand. That is why gateway compromise is often a platform event, not a single-application incident. In practice, many security teams discover gateway abuse only after outbound requests, billing spikes, or provider-side alerts reveal that the proxy itself has become the attacker’s identity broker.

How It Works in Practice

An ordinary proxy usually forwards authenticated traffic and may terminate TLS, but it rarely needs to hold broad application authority. An AI gateway, by contrast, often acts as an identity and policy broker for model calls, retrieval tools, and external APIs. That means it can see prompts, attach provider-specific headers, inject secrets, and route requests across multiple downstream systems. If it is compromised, the attacker may inherit the gateway’s trust relationships rather than just intercepting one session.

This is why current guidance from OWASP Top 10 for Large Language Model Applications and the emerging agentic AI security community emphasizes minimizing standing privilege, isolating secrets, and evaluating access at runtime. A safer gateway design uses short-lived tokens, workload identity, and policy-as-code so the gateway does not need durable access to every provider all the time. Where possible, separate routing from secret storage, and separate user authorization from model-provider authorization. NHIMG’s Top 10 NHI Issues highlights the same operational pattern: the more identities and secrets a platform centralizes, the more valuable that platform becomes to attackers.

• Use ephemeral credentials per provider call instead of long-lived master keys.
• Store secrets outside the gateway process and fetch them just in time.
• Bind requests to workload identity so the gateway proves what it is, not just what it knows.
• Log routing, token minting, and provider selection as distinct security events.

These controls tend to break down in high-throughput multitenant environments because teams optimize for latency and reuse, then quietly reintroduce durable secrets and broad admin scopes.

Common Variations and Edge Cases

Tighter gateway controls often increase operational overhead, so organisations must balance blast-radius reduction against performance, cost, and developer friction. There is no universal standard for how much authority an AI gateway should hold yet, especially when it handles tool use, retrieval, and multi-model failover. Best practice is evolving, but the direction is clear: reduce standing privilege, make authorisation contextual, and avoid treating the gateway as an all-purpose super-user.

Some environments justify a more capable gateway, such as regulated deployments that need strict auditability, content filtering, or routing between jurisdictions. Even there, the gateway should not become a secret warehouse. Where the gateway also orchestrates agents, the risk increases further because the component can chain actions across tools and providers much faster than a human operator. That is why the OWASP NHI Top 10 and the Ultimate Guide to NHIs — Why NHI Security Matters Now both frame centralised identity control as a structural risk, not just a configuration issue. The edge case to watch is any gateway that can both authenticate to providers and modify prompts or tool calls, because that combination turns a proxy into a high-impact execution surface.

Related resources from NHI Mgmt Group

• Why do shadow AI tools create more identity risk than ordinary application sprawl?
• Why do non-human identities create more risk than many human accounts?
• Why do non-human identities create more remediation risk than many human accounts?
• How should teams reduce the risk of exposed AI credentials being abused?