Beyond Visual Evidence: Revealing and Mitigating Relational Privacy Leakage in Document MLLMs

• URL: http://arxiv.org/abs/2608.12911v1
• Date: Thu, 13 Aug 2026 07:53:53 GMT
• Title: Beyond Visual Evidence: Revealing and Mitigating Relational Privacy Leakage in Document MLLMs
• Abstract summary: This paper investigates the privacy issues inherent in Key Information Extraction (KIE) tasks.<n>We reveal that when input images lack sufficient visual evidence, these models often rely on memorized field relations to infer missing content.<n>We propose mitigating this risk with the Unlearning Framework (DRUF) which comprises a dynamic update mechanism.
• Score: 13.913126694995668
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While the privacy risks of multimodal large language models (MLLMs) have drawn significant attention, the unique vulnerabilities of domain-specific MLLMs remain largely underexplored. Focusing on document understanding MLLMs for identity document processing, this paper investigates the privacy issues inherent in Key Information Extraction (KIE) tasks. We reveal that when input images lack sufficient visual evidence, these models often rely on memorized field relations from training data to infer missing content, thereby leaking multiple correlated fields containing sensitive personal information. To mitigate this risk, we make three key contributions.First, we propose the Dynamic Relational Unlearning Framework (DRUF) which comprises a Relational Decoupling Unlearning (RDU) module and a dynamic set update mechanism. It suppresses the leakage of high-risk field pairs while preserving KIE performance.Second, we introduce DocPrivacyBench, a novel benchmark to systematically evaluate a model's susceptibility to privacy leakage under conditions of absent or minimal visual evidence.Third, we evaluate three MLLMs and six unlearning methods using this benchmark, assessing both post-unlearning leakage suppression and utility preservation.Our results demonstrate that existing MLLMs consistently exhibit privacy leakage when visual evidence is scarce, particularly on noisier datasets. In contrast, DRUF outperforms the strongest baseline by improving leakage suppression by 4.8 percentage points, effectively mitigating privacy risks while maintaining robust document information extraction performance.

Related papers

• POPS: Recovering Unlearned Multi-Modality Knowledge in MLLMs with Prompt-Optimized Parameter Shaking [56.151482557515955]
  Multi-modality Machine Unlearning (MMU) was proposed as a mitigation that can effectively force MLLMs to forget private information.<n>In this paper, we propose a novel strategy, namely Prompt-d adversarial-POPS, aiming to recover the supposedly unlearned multi-modality knowledge from the MLLMs.<n>Our method elicits the victim MLLMs to generate potential private examples via promptsuffix optimization, and then exploits these synthesized outputs to fine-tune the models so they disclose the true private information.
  arXiv  Detail & Related papers  (2026-07-07T16:28:54Z)
• Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges [55.425628316813174]
  Multi-modal Large Language Models (MLLMs) process both text and images.<n>Compared to text-only models, MLLMs can extract and expose sensitive information embedded in images.<n>Some MLLMs are susceptible to privacy breaches, leaking sensitive data embedded in images or stored in memory.
  arXiv  Detail & Related papers  (2026-06-08T07:19:42Z)
• Seeing Without Exposing: Adaptive Privacy Control for Open-World, Context-Hungry MLLMs [61.80513991207956]
  We propose Anchored Privacy Drifting (APD) to tackle privacy challenges in MLLMs.<n>APD drifts privacy-sensitive elements toward semantically equivalent alternatives while anchoring contextual cues to the source image.<n>We show that our method achieves balanced improvements in both privacy sanitization and content retention.
  arXiv  Detail & Related papers  (2026-06-05T11:41:39Z)
• When Privacy Meets Recovery: The Overlooked Half of Surrogate-Driven Privacy Preservation for MLLM Editing [61.80513991207956]
  This work focuses on the challenge of how to restore surrogate-driven protected data in diverse MLLM scenarios.<n>We first bridge this research gap by contributing the SPPE (Surrogate Privacy Protected Editable) dataset.<n>We introduce a unified approach that reliably reconstructs private content while preserving the fidelity of MLLM-generated edits.
  arXiv  Detail & Related papers  (2025-12-08T04:59:03Z)
• Safe-LLaVA: A Privacy-Preserving Vision-Language Dataset and Benchmark for Biometric Safety [43.17090130312271]
  Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in vision-language tasks.<n>These models often infer and reveal sensitive biometric attributes such as race, gender, age, body weight, and eye color.<n>Despite increasing awareness, no publicly available dataset or benchmark exists to comprehensively evaluate or mitigate biometric leakage in MLLMs.
  arXiv  Detail & Related papers  (2025-08-29T18:54:57Z)
• 1-2-3 Check: Enhancing Contextual Privacy in LLM via Multi-Agent Reasoning [18.751008976082655]
  We introduce a multi-agent framework that decomposes privacy reasoning into specialized subtasks (extraction, classification)<n>We conduct a systematic ablation over information-flow topologies, revealing when and why upstream detection mistakes cascade into downstream leakage.
  arXiv  Detail & Related papers  (2025-08-11T06:34:09Z)
• Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation [88.78166077081912]
  We introduce a multimodal unlearning benchmark, UnLOK-VQA, and an attack-and-defense framework to evaluate methods for deleting specific multimodal knowledge from MLLMs.<n>Our results show multimodal attacks outperform text- or image-only ones, and that the most effective defense removes answer information from internal model states.
  arXiv  Detail & Related papers  (2025-05-01T01:54:00Z)
• Robust Utility-Preserving Text Anonymization Based on Large Language Models [80.5266278002083]
  Anonymizing text that contains sensitive information is crucial for a wide range of applications.<n>Existing techniques face the emerging challenges of the re-identification ability of large language models.<n>We propose a framework composed of three key components: a privacy evaluator, a utility evaluator, and an optimization component.
  arXiv  Detail & Related papers  (2024-07-16T14:28:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.