Who is accountable when a compromised AI gateway injects tool calls into agents?

Why This Matters for Security Teams

A compromised ai gateway changes the trust model from “who sent the request” to “which intermediary can alter what the agent does next.” That matters because agents often execute with delegated authority, so injected tool calls can turn a single gateway compromise into data exposure, unauthorized actions, or privilege escalation across multiple systems. This is a control-plane problem, not just an application bug. Guidance from the OWASP Agentic AI Top 10 and NHIMG’s 52 NHI Breaches Analysis both point to the same operational reality: intermediaries that can shape agent behavior must be treated as privileged security assets, not neutral plumbing.

Accountability is usually split because the failure crosses team boundaries. Platform owners control the gateway, application teams choose whether the agent trusts it, and security teams own the policy and revocation response when abuse is detected. That split is where incidents linger. The practical question is not only who is blamed after the fact, but who has authority to disable the gateway, invalidate its credentials, and prove which tool call was injected versus legitimately requested. In practice, many security teams encounter this only after an agent has already executed a malicious tool action rather than through intentional gateway hardening.

How It Works in Practice

For agentic systems, the gateway should be modeled as a privileged identity broker with its own workload identity, logging obligations, and emergency shutdown path. That means the gateway does not merely forward requests; it influences authorization context, tool selection, or execution sequencing. If an attacker compromises it, they can inject tool calls that appear valid to downstream agents unless the system enforces runtime policy checks outside the gateway’s control. This is why static role assignments are insufficient when the intermediary itself is the attack surface.

Current guidance suggests three layers of control. First, bind the gateway to a cryptographic workload identity, such as SPIFFE-style identity or OIDC-based service identity, so the agent can verify what the intermediary is before accepting instructions. Second, evaluate tool invocation policy at request time using policy-as-code rather than trusting pre-approved routes. Third, issue short-lived credentials per task so a compromised gateway cannot reuse standing secrets for long. The NIST AI Risk Management Framework is useful here because it frames accountability, governance, and monitoring as continuous functions, not one-time configuration.

• Define the gateway owner, the agent owner, and the incident revocation owner separately.
• Log tool-call intent, transformation, and execution so injected actions can be distinguished from original agent intent.
• Restrict the gateway to the minimum set of tools and scopes required for its role.
• Revoke gateway tokens automatically when behavior deviates from expected patterns.

NHIMG’s OWASP NHI Top 10 research and the CSA MAESTRO agentic AI threat modeling framework both reinforce that tool mediation layers need explicit trust boundaries, not implied trust. These controls tend to break down in multi-tenant agent platforms where several teams share one gateway because blast radius and ownership become ambiguous.

Common Variations and Edge Cases

Tighter gateway control often increases operational overhead, requiring organisations to balance faster agent execution against stronger containment and clearer ownership. There is no universal standard for this yet, especially for teams that want the gateway to enrich prompts, normalize tool schemas, and broker permissions at the same time. That flexibility is useful, but it also makes the gateway a high-value target. The right answer depends on how much execution authority the agent has, how much the gateway can rewrite, and whether downstream systems trust the gateway’s assertions without revalidation.

One common edge case is delegated administration, where the gateway is run by a platform team but the agent is owned by a product team. In that model, accountability must be shared but not blurred: the platform team owns gateway integrity, while the product team owns the decision to trust injected instructions. Another edge case is human-in-the-loop workflows, where the gateway may queue or annotate tool calls before approval. If the human reviews only the final request, injected steps can still slip through unless the full call chain is preserved. The Anthropic AI-orchestrated cyber espionage report is a useful reminder that agentic abuse often chains small steps into a large compromise.

For practitioners, the key exception is when the gateway is only a transport relay and does not alter tool intent. In that case, accountability shifts more toward downstream policy enforcement. But if the gateway can inject, modify, or prioritize tool calls, it is part of the trusted computing base and should be governed accordingly.

Related resources from NHI Mgmt Group

• How should security teams govern AI gateway traffic that carries prompts and tool calls?
• How should teams reduce the risk of exposed AI credentials being abused?
• What steps should security teams take to prevent Shadow AI risks?
• What is the difference between prompt injection risk and identity abuse in agents?