Large vision-language models (LVLMs) have achieved remarkable advancements in multimodal reasoning tasks. However, their widespread accessibility raises critical concerns about potential copyright infringement. Will LVLMs accurately recognize and comply with copyright regulations when encountering copyrighted content (i.e., user input, retrieved documents) in the context? Failure to comply with copyright regulations may lead to serious legal and ethical consequences, particularly when LVLMs generate responses based on copyrighted materials (e.g., retrieved book experts, news reports). In this paper, we present a comprehensive evaluation of various LVLMs, examining how they handle copyrighted content – such as book excerpts, news articles, music lyrics, and code documentation when they are presented as visual inputs. To systematically measure copyright compliance, we introduce a large-scale benchmark dataset comprising 50,000 multimodal query-content pairs designed to evaluate how effectively LVLMs handle queries that could lead to copyright infringement. Given that real-world copyrighted content may or may not include a copyright notice, the dataset includes query-content pairs in two distinct scenarios: with and without a copyright notice. For the former, we extensively cover four types of copyright notices to account for different cases. Our evaluation reveals that even state-of-the-art closed-source LVLMs exhibit significant deficiencies in recognizing and respecting the copyrighted content, even when presented with the copyright notice. To solve this limitation, we introduce a novel tool-augmented defense framework for copyright compliance, which reduces infringement risks in all scenarios. Our findings underscore the importance of developing copyright-aware LVLMs to ensure the responsible and lawful use of copyrighted content.

Hengyu An, Minxi Li, Jinghuai Zhang, Naen Xu, Chunyi Zhou, Changjiang Li, Xiaogang Xu, Tianyu Du, Shouling Ji. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

High-resolution remote sensing (RS) imagery frequently undergoes multi-scale tiling and resampling, introducing geometric and resolution variances that cause synchronization failure in conventional deep watermarking models reliant on fixed-grid assumptions. We propose an Implicit Neural Watermarking framework that reconceptualizes watermark embedding as a continuous, coordinate-conditioned mapping, inherently achieving resolution-agnostic extraction. Our framework introduces three core innovations: (i) Decentralized Coordinate Encoding (DCE), which ensures translation-robustness across arbitrary tiles via boundary-consistent periodic mapping; (ii) Content-Adaptive Modulation (CAM), which couples watermark synthesis with image semantics through a HyperNetwork to thwart template-estimation attacks; and (iii) Texture-Aware Optimization (TAO), which adaptively modulates embedding intensity based on local structural complexity to balance imperceptibility and decodability. Quantitative evaluations on Sentinel-2 datasets demonstrate superior visual fidelity and state-of-the-art robustness against typical RS-specific distortions, including geometric warping, scaling, and re-tiling, providing a theoretically grounded solution for practical RS copyright protection.

Large language model (LLM) agents are widely deployed in real-world applications, where they leverage tools to retrieve and manipulate external data for complex tasks.However, when interacting with untrusted data sources (e.g., fetching information from public websites), tool responses may contain injected instructions that covertly influence agent behaviors and lead to malicious outcomes, a threat referred to as Indirect Prompt Injection (IPI).Existing defenses typically rely on advanced prompting strategies or auxiliary detection models.While these methods have demonstrated some effectiveness, they fundamentally rely on assumptions about the model's inherent security, which lacks structural constraints on agent behaviors.As a result, agents still retain unrestricted access to tool invocations, leaving them vulnerable to stronger attack vectors that can bypass the security guardrails of the model.To prevent malicious tool invocations at the source, we propose a novel defensive task execution paradigm, called IPIGUARD 1 , which models the agents' task execution process as a traversal over a planned Tool Dependency Graph (TDG).By explicitly decoupling action planning from interaction with external data, IPIGUARD significantly reduces unintended tool invocations triggered by injected instructions, thereby enhancing robustness against IPI attacks.Experiments on the AgentDojo benchmark show that IPIGUARD achieves a superior balance between effectiveness and robustness, paving the way for the development of safer agentic systems in dynamic environments.