Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges
arXiv:2606.09125v1 Announce Type: cross Abstract: Privacy risks in text-only Large Language Models (LLMs) are well studied, particularly their tendency to memorize and leak sensitive information. However, Multi-modal Large Language Models (MLLMs), which process both text and images, introduce unique privacy challenges that remain underexplored. Compared to text-only models, MLLMs can extract and expose sensitive information embedded in images, posing new privacy risks. We reveal that some MLLMs are susceptible to privacy breaches, leaking sensitive data embedded in images or stored in memory.
The rapid advancement and deployment of Multi-modal Large Language Models are exposing critical, previously underexplored, privacy vulnerabilities that are becoming more apparent with increased usage in real-world scenarios.
This highlights fundamental privacy flaws in cutting-edge AI, necessitating immediate attention from developers, regulators, and users to prevent widespread data leakage and maintain public trust.
The understanding of AI privacy risks expands beyond text-only models to encompass complex visual data within MLLMs, requiring new security protocols and regulatory frameworks.
- · Cybersecurity firms specializing in AI
- · Privacy-preserving AI researchers
- · Open-source AI foundations prioritizing security
- · Companies deploying MLLMs without robust privacy safeguards
- · Individuals whose sensitive data is leaked
- · Cloud providers with vulnerable MLLM offerings
Increased scrutiny and demand for privacy-by-design principles in MLLM development.
Potential for new regulations specifically addressing MLLM data handling and ethical deployment.
A possible slowdown in MLLM adoption or a public backlash if privacy breaches become frequent and severe.
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Read at arXiv cs.AI