When Your LLM Router Turns Against You: The Hidden Security Risk in AI Agents

LLM routers and gateways help AI applications use multiple model providers through a unified interface, but they also become security-critical intermediaries because they can observe and potentially alter requests and responses between agents and model providers. Research cited in the text examined 428 paid and free routers and found instances of malicious code injection, adaptive evasion, and abuse of researcher-controlled credentials, showing that a compromised router could modify tool calls, collect sensitive data, or participate in wider attacks without the underlying model being compromised. This risk differs from prompt injection because it occurs in transit after a model generates an otherwise legitimate response, and it is especially consequential for autonomous agents that can execute tools, access files, use credentials, or interact with external systems. Multiple routing layers create a weakest-link problem, while delayed or targeted malicious behavior can make basic testing insufficient. Recommended mitigations include least-privilege credentials, isolated environments, policy checks and approvals for sensitive actions, anomaly detection, detailed logging, and stronger auditing of routing decisions, while authenticated response provenance could eventually allow agents to verify that high-impact model outputs have not been altered.

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