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New research reveals privacy risks in multi-modal and adapted LLMs

By PulseAugur Editorial · [3 sources] ·

Two new research papers explore the privacy vulnerabilities of large language models (LLMs). One paper introduces a dataset and evaluation framework to identify privacy risks in multi-modal LLMs, highlighting how these models can leak sensitive information from images and memory. The other paper benchmarks the effectiveness of differential privacy (DP) in adapting LLMs, finding that data distribution shifts significantly impact privacy risks and that parameter-efficient fine-tuning methods like LoRA offer better protection for out-of-distribution data. AI

IMPACT Highlights critical vulnerabilities in LLM privacy, urging developers to implement robust safeguards for multi-modal and adapted models.

RANK_REASON Two academic papers published on arXiv detailing privacy risks and mitigation strategies for LLMs.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Tiejin Chen, Pingzhi Li, Kaixiong Zhou, Tianlong Chen, Hua Wei ·

    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 imag…

  2. arXiv cs.LG TIER_1 English(EN) · Bart{\l}omiej Marek, Lorenzo Rossi, Vincent Hanke, Xun Wang, Michael Backes, Franziska Boenisch, Adam Dziedzic ·

    Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models

    arXiv:2606.09401v1 Announce Type: new Abstract: Recent work has applied differential privacy (DP) to adapt large language models (LLMs) for sensitive applications, offering theoretical guarantees. However, its practical effectiveness remains unclear, partly due to LLM pretraining…

  3. arXiv cs.LG TIER_1 English(EN) · Adam Dziedzic ·

    Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models

    Recent work has applied differential privacy (DP) to adapt large language models (LLMs) for sensitive applications, offering theoretical guarantees. However, its practical effectiveness remains unclear, partly due to LLM pretraining, where overlaps and interdependencies with adap…

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