Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges (2025.findings-acl)
Copied to clipboard
| Challenge: | Privacy risks in text-only Large Language Models are well-documented, especially their tendency to memorize and leak sensitive information. |
| Approach: | They propose a dataset to assess privacy risks across multi-modal tasks and scenarios . they demonstrate how models leak sensitive data across various tasks . |
| Outcome: | The proposed model can leak sensitive data embedded in images or stored in memory, exposing privacy risks. |
Similar Papers
MLLM-Protector: Ensuring MLLM’s Safety without Hurting Performance (2024.emnlp-main)
Copied to clipboard
Renjie Pi, Tianyang Han, Jianshu Zhang, Yueqi Xie, Rui Pan, Qing Lian, Hanze Dong, Jipeng Zhang, Tong Zhang
| Challenge: | MLLMs are deployed on limited image-text pairs, which makes them more vulnerable to catastrophic forgetting of their original abilities during safety fine-tuning. |
| Approach: | They propose a plug-and-play strategy that detects harmful visual inputs and transforms harmful ones into harmless ones. |
| Outcome: | The proposed approach mitigates the risks posed by malicious visual inputs without compromising the original performance of MLLMs. |
Combating Security and Privacy Issues in the Era of Large Language Models (2024.naacl-tutorials)
Copied to clipboard
| Challenge: | a tutorial aims to provide a summary of risks and vulnerabilities in large language models . a number of studies have focused on security, privacy and copyright aspects of LLMs . |
| Approach: | This tutorial seeks to provide a systematic summary of risks and vulnerabilities in large language models . authors will discuss security, privacy and copyright aspects of LLMs . |
| Outcome: | This tutorial aims to provide a systematic summary of risks and vulnerabilities in large language models . it will also outline emerging challenges in security, privacy and reliability of LLMs . |
Both Text and Images Leaked! A Systematic Analysis of Data Contamination in Multimodal LLM (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for unimodal large language models are inadequate for MLLMs due to multimodal data complexity and multi-phase training. |
| Approach: | MM-DETECT analyzes data contamination using a framework that defines two contamination categories - unimodal and cross-modal . |
| Outcome: | The proposed framework quantifies contamination severity across multiple-choice and caption-based Visual Question Answering tasks. |
The Model’s Language Matters: A Comparative Privacy Analysis of LLMs (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large language models are increasingly deployed in multilingual settings that process sensitive data . prior privacy evaluations focused on English, but new research shows that language matters for privacy leakage . |
| Approach: | They quantify six corpus-level linguistic indicators and evaluate vulnerability under three attack families. |
| Outcome: | The results show that language matters for privacy leakage in large language models . Italian exhibits the strongest exposure, while English and French are more resilient . |
From LLMs to MLLMs: Exploring the Landscape of Multimodal Jailbreaking (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable performance across various tasks, effectively following instructions to meet diverse user needs. |
| Approach: | They propose a framework for evaluation benchmarks and attack techniques for LLMs and MLLMs to enhance their security. |
| Outcome: | The proposed frameworks have been exploited to exploit the weaknesses of LLMs and MLLMs. |
USB: A COMPREHENSIVE AND UNIFIED SAFETY EVALUATION BENCHMARK FOR MULTIMODAL LARGE LANGUAGE MODELS (2026.acl-long)
Copied to clipboard
Baolin Zheng, Guanlin Chen, Qingyang Teng, Hongqiong Zhong, Yingshui Tan, Zhendong Liu, Weixun Wang, Jiaheng Liu, Jian Yang, Huiyun Jing, Jincheng Wei, Wenbo Su, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang
| Challenge: | Existing safety benchmarks fail to provide reliable assessments due to limited risk coverage, insufficient scale and the oversight of complex modality combinations. |
| Approach: | They propose a framework that covers 61 risk categories across four modality interactions to address this gap. |
| Outcome: | The proposed framework covers 61 risk categories across four distinct modality interactions. |
Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench (2025.naacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) and Multimodal Large Language models (MLLMs) trained on vast web corpora can memorize and disclose individuals’ confidential and private data, raising legal and ethical concerns. |
| Approach: | They propose a benchmark to assess unlearning algorithms from multiple perspectives and provide a baseline for existing generative models. |
| Outcome: | The proposed benchmark consists of 500 fictitious profiles and 153 profiles for public celebrities, evaluated from both multimodal (image+text) and unimodal (text) perspectives. |
Leave My Images Alone: Preventing Multi-Modal Large Language Models from Analyzing Images via Visual Prompt Injection (2026.acl-long)
Copied to clipboard
| Challenge: | Multimodal large language models are powerful tools for analyzing Internet-scale image data. |
| Approach: | They propose a method to protect images from unauthorized analysis by MLLMs . they embed a perturbation that acts as a visual prompt injection attack on MLMLs if a malicious actor downloads and queries an image . |
| Outcome: | The proposed method protects images from unauthorized analysis by MLLMs . it embeds a perturbation that acts as a visual prompt injection attack on MLMLs if a malicious actor downloads and queries the protected image . |
LLMs are Privacy Erasable (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a new study examines the privacy of large language models and their capabilities . the study aims to address the balance between the convenience of LLMs and user privacy concerns . |
| Approach: | They propose a strategy that safeguards user prompt while accessing LLM cloud services . they evaluate the efficacy of their method across prominent LLM benchmarks . |
| Outcome: | The proposed method thwarts reconstruction attacks and improves model performance . it also surpasses the results reported in official model cards . |
Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review (2025.findings-acl)
Copied to clipboard
Pei Fu, Tongkun Guan, Zining Wang, Zhentao Guo, Chen Duan, Hao Sun, Boming Chen, Qianyi Jiang, Jiayao Ma, Kai Zhou, Junfeng Luo
| Challenge: | Recent advances in vision-language models have unified perception and understanding tasks within Visual Question Answering paradigms. |
| Approach: | They propose to outline timeline, architecture, and pipeline of nearly all TIU MLLMs and review their performance on mainstream benchmarks. |
| Outcome: | The proposed models perform well on mainstream benchmarks and are compared with other models. |