RouteScan: A Non-Intrusive Approach to Auditing MoE LLMs Safety via Expert Routing Telemetry
arXiv:2605.24817v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures have become an increasingly important paradigm for scaling Large Language Models (LLMs). As MoE models are increasingly deployed in real-world services, safety auditing becomes necessary to verify whether these models produce or facilitate harmful behaviors during operation. However, existing content-based auditing methods typically require access to user prompts, model inputs, or generated outputs, potentially exposing sensitive user information and creating a fundamental tension between LLM safety and us
The rapid deployment of MoE LLMs in real-world applications is increasing the urgency for robust, non-intrusive safety auditing mechanisms to address privacy and ethical concerns.
This development allows for auditing of powerful MoE LLMs without compromising sensitive user data, accelerating their safe integration into critical services and potentially removing a key bottleneck to broader adoption.
Safety auditing of MoE LLMs can now be conducted with greater respect for user privacy, potentially fostering more trust and accelerating the deployment of these advanced models.
- · AI developers
- · Cloud service providers
- · Regulatory bodies
- · SaaS companies
- · Intrusive auditing firms
- · Cyber adversaries
Non-intrusive auditing techniques become standard practice for large-scale MoE LLM deployments.
Increased trust in AI systems leads to faster adoption and integration of powerful LLMs into sensitive societal functions.
The definition of AI safety expands to encompass privacy-preserving auditability, influencing future AI development and regulation.
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