arXiv subjects

Shaoxiong Guo

Publications and source records attributed to Shaoxiong Guo.

4 recordsLinked to original sources

arXiv2026

The Plot Twist: Jailbreaking Unified Multimodal Models with a Three-Act NarrativeAttack

Unified Multimodal Understanding and Generation Models (UMMs) increasingly combine visual understanding and image generation within a single interactive workflow, making generated visual content available as later reasoning context. However, existing jailbreak evaluations mostly study text rewriting or isolated visual prompts, leaving the safety risk of narrative cross-turn visual grounding underexplored. We propose NarrativeAttack, a semantic-preserving visual narrative jailbreak framework. NarrativeAttack employs a three-act narrative structure in which the UMM's own generator produces images for the setup (pre-event) and resolution (post-event) stages, making the full attack workflow self-contained while concealing the malicious event as a hidden climax. The attack concludes with an image-based "guessing game" that embeds the original malicious query among benign candidates, compelling the model to select and answer the most relevant one based on the established narrative context. A dynamic difficulty mechanism further enhances attack stability. Experiments show NarrativeAttack consistently surpasses prior approaches, achieving up to 88.25% ASR on Gemini-2.5-Flash. These results uncover an underdeveloped vulnerability and highlight the urgent need for safety alignment in UMMs.

cs.AI↗

arXiv2026

MemEvoBench: Benchmarking Safety Risks from Memory Misevolution in LLM Agents

Equipping Large Language Models (LLMs) with persistent memory enhances interaction continuity and personalization but introduces new safety risks. Specifically, contaminated or biased memory accumulation can trigger abnormal agent behaviors. Existing evaluation methods have not yet established a standardized framework for measuring memory misevolution. This phenomenon refers to the gradual behavioral drift resulting from repeated exposure to misleading information. To address this gap, we introduce MemEvoBench, the first benchmark evaluating long-horizon memory safety in LLM agents against adversarial memory injection, noisy tool outputs, and biased feedback. The framework consists of QA-style tasks across 7 domains and 36 risk types, complemented by workflow-style tasks adapted from 20 Agent-SafetyBench environments with noisy tool returns. Both settings employ mixed benign and misleading memory pools within multi-round interactions to simulate memory evolution. Experiments on representative models reveal substantial safety degradation under biased memory updates. Our analysis suggests that memory evolution is a significant contributor to these failures. Furthermore, static prompt-based defenses prove insufficient, underscoring the urgency of securing memory evolution in LLM agents.

cs.CL↗

arXiv2026

DeepSight: An All-in-One LM Safety Toolkit

As the development of Large Models (LMs) progresses rapidly, their safety is also a priority. In current Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) safety workflow, evaluation, diagnosis, and alignment are often handled by separate tools. Specifically, safety evaluation can only locate external behavioral risks but cannot figure out internal root causes. Meanwhile, safety diagnosis often drifts from concrete risk scenarios and remains at the explainable level. In this way, safety alignment lack dedicated explanations of changes in internal mechanisms, potentially degrading general capabilities. To systematically address these issues, we propose an open-source project, namely DeepSight, to practice a new safety evaluation-diagnosis integrated paradigm. DeepSight is low-cost, reproducible, efficient, and highly scalable large-scale model safety evaluation project consisting of a evaluation toolkit DeepSafe and a diagnosis toolkit DeepScan. By unifying task and data protocols, we build a connection between the two stages and transform safety evaluation from black-box to white-box insight. Besides, DeepSight is the first open source toolkit that support the frontier AI risk evaluation and joint safety evaluation and diagnosis.

cs.CL↗

arXiv2024

VGA: Vision GUI Assistant -- Minimizing Hallucinations through Image-Centric Fine-Tuning

Recent advances in Large Vision-Language Models (LVLMs) have significantly improve performance in image comprehension tasks, such as formatted charts and rich-content images. Yet, Graphical User Interface (GUI) pose a greater challenge due to their structured format and detailed textual information. Existing LVLMs often overly depend on internal knowledge and neglect image content, resulting in hallucinations and incorrect responses in GUI comprehension. To address these issues, we introduce VGA, a fine-tuned model designed for comprehensive GUI understanding. Our model aims to enhance the interpretation of visual data of GUI and reduce hallucinations. We first construct a Vision Question Answering (VQA) dataset of 63.8k high-quality examples with our propose Referent Method, which ensures the model's responses are highly depend on visual content within the image. We then design a two-stage fine-tuning method called Foundation and Advanced Comprehension (FAC) to enhance both the model's ability to extract information from image content and alignment with human intent. Experiments show that our approach enhances the model's ability to extract information from images and achieves state-of-the-art results in GUI understanding tasks. Our dataset and fine-tuning script will be released soon.

cs.CV↗