arXiv subjects

Chunhua Su

Publications and source records attributed to Chunhua Su.

3 recordsLinked to original sources

arXiv2026

FusionVul: A Multimodal Feature Fusion Framework for Source Code Vulnerability Detection

Source code vulnerability detection remains a long-standing challenge due to the increasing scale, structural complexity, and semantic diversity of modern codebases. Conventional static-analysis or rule-based approaches often fail to capture subtle execution dependencies, while single-modality learning models tend to overlook critical structural information embedded beyond the lexical surface of source code. To improve robustness across heterogeneous code patterns, we propose FusionVul, a joint representation learning framework that integrates sequential syntactic representations extracted by a pretrained Transformer encoder with structural semantics propagated through a graph neural network. The framework further incorporates a cross-attention-based feature fusion network to enable fine-grained cross-modal interaction and employs a sample-aware weighting mechanism to integrate multiple predictive branches. Experimental results on four datasets demonstrate that FusionVul achieves superior F1 scores on datasets with highly dispersed function size distributions and broader vulnerability-type coverage, such as SVulD and DiverseVul, reflecting its capability to capture complex and diverse vulnerability patterns.

cs.SE↗

arXiv2025

Prompt-in-Content Attacks: Exploiting Uploaded Inputs to Hijack LLM Behavior

Large Language Models (LLMs) are widely deployed in applications that accept user-submitted content, such as uploaded documents or pasted text, for tasks like summarization and question answering. In this paper, we identify a new class of attacks, prompt in content injection, where adversarial instructions are embedded in seemingly benign inputs. When processed by the LLM, these hidden prompts can manipulate outputs without user awareness or system compromise, leading to biased summaries, fabricated claims, or misleading suggestions. We demonstrate the feasibility of such attacks across popular platforms, analyze their root causes including prompt concatenation and insufficient input isolation, and discuss mitigation strategies. Our findings reveal a subtle yet practical threat in real-world LLM workflows.

cs.CR↗

arXiv2021

WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacy

For data isolated islands and privacy issues, federated learning has been extensively invoking much interest since it allows clients to collaborate on training a global model using their local data without sharing any with a third party. However, the existing federated learning frameworks always need sophisticated condition configurations (e.g., sophisticated driver configuration of standalone graphics card like NVIDIA, compile environment) that bring much inconvenience for large-scale development and deployment. To facilitate the deployment of federated learning and the implementation of related applications, we innovatively propose WebFed, a novel browser-based federated learning framework that takes advantage of the browser's features (e.g., Cross-platform, JavaScript Programming Features) and enhances the privacy protection via local differential privacy mechanism. Finally, We conduct experiments on heterogeneous devices to evaluate the performance of the proposed WebFed framework.

cs.CR↗