They sit inside trusted networks and are built to make outbound requests on behalf of users. If an attacker can influence the destination, the gateway can be turned toward metadata services, internal APIs, or private hosts. The service account and host environment then become part of the attacker path, not just the user session.
Why This Matters for Security Teams
LLM gateways become credential exposure risks because they are trusted brokers: they receive a user request, then make outbound calls with the network position, service account, and secrets of the gateway itself. That changes the attacker’s target from a prompt injection problem to a cloud control-plane problem. Guidance from the OWASP Agentic AI Top 10 and NIST’s NIST AI Risk Management Framework both point to the same issue: autonomous or tool-using systems need context-aware controls, not just perimeter trust.
This is not a theoretical risk. NHIMG’s LLMjacking: How Attackers Hijack AI Using Compromised NHIs research notes that when AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes, and as quickly as 9 minutes in some cases. That speed matters because a gateway can turn a single malicious destination into a route toward metadata services, internal APIs, or private hosts before defenders notice. In practice, many security teams discover this only after the gateway account has already been used as the bridge into cloud resources rather than through intentional testing.
How It Works in Practice
The exposure usually starts with design, not exploitation. An LLM gateway is built to simplify orchestration, so it often has broad egress, reusable API keys, and access to internal services that normal users never see. If the gateway accepts attacker-influenced URLs, tool calls, function arguments, or retrieval targets, the attacker can steer its outbound requests toward sensitive endpoints. Once that happens, the gateway may fetch instance metadata, hit an internal API, or relay secrets into an external system.
Security teams should think in terms of containment and runtime control. The most effective patterns are:
- Use separate workload identities for the gateway and each tool it invokes, rather than one shared service account.
- Prefer short-lived, just-in-time credentials over static cloud keys, and revoke them when the task ends.
- Restrict egress with allowlists for domains, IP ranges, and cloud metadata protections.
- Evaluate each outbound action at request time using policy-as-code and full context.
- Monitor for prompt injection, URL manipulation, and tool chaining that changes the request destination.
NHIMG’s Ultimate Guide to NHIs — Static vs Dynamic Secrets aligns with this approach: dynamic secrets reduce the value of stolen credentials, especially in systems that can be steered into unintended network paths. The same theme appears in the NIST Cybersecurity Framework 2.0, where asset visibility, protective controls, and continuous monitoring are treated as complementary. These controls tend to break down in legacy cloud deployments where a gateway runs with one powerful role, broad VPC access, and no enforced egress policy because there is no clean boundary between model orchestration and infrastructure privilege.
Common Variations and Edge Cases
Tighter gateway controls often increase latency and operational overhead, requiring organisations to balance safer routing against developer convenience. That tradeoff is especially visible in multi-tenant platforms, where shared gateways serve many teams and one badly scoped connector can expose everyone’s secrets. Current guidance suggests isolating tenants, but there is no universal standard for this yet, so implementation details matter.
Some environments create extra risk even when the gateway is well built. Serverless gateways may inherit broad cloud permissions from their execution role. Kubernetes-based gateways may leak tokens through misconfigured service account mounts. Retrieval-augmented systems may pass attacker-controlled text into downstream tools that make network calls. In each case, the control failure is not just prompt safety, but overbroad trust in the identity that makes the request.
NHIMG’s 52 NHI Breaches Analysis and 230M AWS environment compromise both reinforce the same operational lesson: once a non-human identity can be redirected, standing privileges become an attacker’s multiplier. For teams adopting agentic workflows, the safer pattern is to limit what the gateway can reach, issue credentials only for the exact task, and assume that any tool path exposed to model output may be abused. That is the posture recommended by CSA MAESTRO agentic AI threat modeling framework and the NIST AI 600-1 Generative AI Profile.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10, CSA MAESTRO and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A2 | Covers prompt/tool abuse that can redirect gateway egress and expose secrets. |
| CSA MAESTRO | TRD-03 | Addresses agent and tool trust boundaries that gateways often blur. |
| NIST AI RMF | GOVERN | Requires accountability and oversight for AI systems with real operational impact. |
| OWASP Non-Human Identity Top 10 | NHI-03 | Static cloud credentials in gateways increase the blast radius of compromise. |
| NIST CSF 2.0 | PR.AC-4 | Least privilege is central when a gateway can reach internal and cloud resources. |
Constrain tool use and validate every outbound action before the gateway can execute it.
