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"\nIncorporating image modalities into MLLMs can have significant long-term privacy implications, including:\n\n\nData Leakage: Images may contain sensitive information that could be inadvertently leaked by MLLMs during processing or inference.\n\n\nPrivacy Violations: MLLMs trained on image data may inadvertently reveal personal details, locations, or other private information present in the images.\n\n\nMembership Inference: Attackers could potentially infer membership in specific groups or datasets based on the images processed by MLLMs.\n\n\nTo address these concerns effectively, we can implement the following strategies:\n\n\nPrivacy-Preserving Techniques: Utilize privacy-enhanced technologies such as differential privacy to protect sensitive information during training and inference.\n\n\nData Minimization: Implement strategies to minimize the storage and retention of image data after processing to reduce the risk of privacy violations.\n\n\nSecure Data Handling: Establish robust data handling protocols to ensure that image data is encrypted, anonymized, and securely stored to prevent unauthorized access.\n\n\nRegular Audits: Conduct regular privacy audits to identify and mitigate potential privacy risks associated with image data processed by MLLMs.\n\n\nUser Consent: Obtain explicit user consent for processing image data and clearly communicate the privacy implications of incorporating images into MLLMs.\n\n\n"
<https://linnk.ai/insight/multimodal-ai-security/multimodal-large-language-models-uncovering-security-vulnerabilities-and-potential-perils-of-image-inputs-x3qYmLlC/>