Defeating Cerberus: Concept-Guided Privacy-Leakage Mitigation in Multimodal Language Models
This addresses privacy concerns for users of multimodal language models, though it is incremental as it builds on existing single-modal research.
The paper tackles the problem of Personally Identifiable Information (PII) leakage in vision language models by introducing a concept-guided mitigation approach that modifies internal states to refuse PII-sensitive tasks, achieving an average refusal rate of 93.3% with minimal impact on unrelated performances.
Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in processing and reasoning over diverse modalities, but their advanced abilities also raise significant privacy concerns, particularly regarding Personally Identifiable Information (PII) leakage. While relevant research has been conducted on single-modal language models to some extent, the vulnerabilities in the multimodal setting have yet to be fully investigated. In this work, we investigate these emerging risks with a focus on vision language models (VLMs), a representative subclass of MLLMs that covers the two modalities most relevant for PII leakage, vision and text. We introduce a concept-guided mitigation approach that identifies and modifies the model's internal states associated with PII-related content. Our method guides VLMs to refuse PII-sensitive tasks effectively and efficiently, without requiring re-training or fine-tuning. We also address the current lack of multimodal PII datasets by constructing various ones that simulate real-world scenarios. Experimental results demonstrate that the method can achieve an average refusal rate of 93.3% for various PII-related tasks with minimal impact on unrelated model performances. We further examine the mitigation's performance under various conditions to show the adaptability of our proposed method.