- The paper presents a vulnerability in MoE models where malicious queries manipulate batching to compromise the output of benign inputs.
- It details integrity and availability attacks through expert buffer saturation, with empirical results on a toy model.
- The authors propose mitigation strategies like randomizing batch orders and increasing buffer limits to secure model performance.
Introduction
Mixture of Experts (MoE) models have emerged as a powerful architectural choice for developing scalable and efficient AI systems. These models dynamically route inputs to a subset of expert networks, allowing for specialization and increased capacity without a linear increase in computation for every input. However, the integration of MoE into large-scale models introduces a new vector for potential vulnerabilities.
The Vulnerability
This paper presents a thorough investigation into a specific vulnerability within the MoE framework, particularly focusing on models incorporating expert routing strategies with cross-batch dependencies. The crux of the vulnerability lies in the ability of an adversary to manipulate the model's output on benign queries by injecting malicious queries into the same batch. This manipulation exploits the routing mechanism's dependency on batch composition, whereby malicious inputs can saturate the capacities of targeted experts, causing benign inputs to be either dropped or rerouted to suboptimal experts.
Technical Insights
The exposition on MoE provides foundational knowledge, elucidating how inputs are assigned to experts based on gating functions. Notably, the exploration highlights that most MoE implementations predefine a buffer capacity limit for each expert to manage computational resources effectively.
Two primary attack vectors were identified and demonstrated: integrity attacks, aiming to cause deterministic fault injection, and availability attacks, targeting the denial of service by overwhelming expert buffers. Both attacks leverage the aforementioned vulnerability, effectively illustrating the feasibility of manipulating model outputs in a controlled setting.
Further, the paper explores the attack mechanism, providing a step-by-step account of how adversarial inputs are formulated and injected to compromise the integrity or availability of the model's predictions. A notable contribution is the empirical demonstration of these attacks on a toy MoE model, underscoring the practicality of the described vulnerability.
Mitigation Strategies
In response to these findings, several mitigation strategies are proposed. Among them, randomizing batch orders and increasing expert buffer capacity limits are highlighted as effective immediate measures. More sophisticated methods include sampling from gating functions to introduce randomness in expert assignments, thereby reducing the predictability exploited by the attack.
Discussion and Implications
The discussion underscores the broader implications of this vulnerability, especially in shared, multi-tenant AI serving environments where models process inputs from multiple sources simultaneously. The necessity for a security-minded approach in designing and deploying MoE models is emphasized, suggesting that efficiency optimizations should not compromise the model's integrity.
Conclusion
The paper concludes with a call to action for the AI research community to prioritize securing MoE models against such vulnerabilities. It advocates for a balance between performance optimization and security, suggesting that future research should explore robust, attack-resistant routing mechanisms.
This investigation into MoE vulnerabilities opens up a new dimension in the security of AI systems, highlighting the need for comprehensive security assessments and mitigation strategies in the design of scalable AI architectures. Through a blend of technical detail and suggested countermeasures, the paper significantly contributes to our understanding of potential weaknesses in MoE models and sets the stage for future research in securing AI against adversarial manipulations.