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20232026
most citedLarge Language Models for Software Engineering: A Systematic Literature Review

119 citations · 200 across the 54 of their papers we have counts for

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18 papers · 1 filter

cs.CR2026

MalTotal: Cost-Effective and Language-Agnostic Malicious Code Poisoning Detection for Millions of Repositories

Jian Zhao, Shenao Wang, Qingyang Wu +3

The widespread adoption of open source software (OSS) has introduced significant security risks, with malicious code poisoning attacks increasingly targeting public package registr…

cs.CR2026

MalSkills: Detecting Malicious Skills in the Agentic Supply Chain via Neuro-symbolic Reasoning

Shenao Wang, Junjie He, Yanjie Zhao +3

Skills are increasingly used to extend LLM agents by packaging prompts, code, and configurations into reusable modules. As public registries and marketplaces expand, they form an e…

cs.CR2026

Evaluating and Enhancing the Vulnerability Reasoning Capabilities of Large Language Models

Li Lu, Yanjie Zhao, Hongzhou Rao +2

Large Language Models (LLMs) have demonstrated remarkable proficiency in vulnerability detection. However, a critical reliability gap persists: models frequently yield correct dete…

cs.CR2026

SMCP: Secure Model Context Protocol

Xinyi Hou, Shenao Wang, Yifan Zhang +4

Agentic AI systems built around large language models (LLMs) are moving away from closed, single-model frameworks and toward open ecosystems that connect a variety of agents, exter…

cs.CR2025

Demystifying Cookie Sharing Risks in WebView-based Mobile App-in-app Ecosystems

Miao Zhang, Shenao Wang, Guilin Zheng +2

Mini-programs, an emerging mobile application paradigm within super-apps, offer a seamless and installation-free experience. However, the adoption of the web-view component has dis…

cs.CR2025

"Your AI, My Shell": Demystifying Prompt Injection Attacks on Agentic AI Coding Editors

Yue Liu, Yanjie Zhao, Yunbo Lyu +3

Agentic AI coding editors driven by large language models have recently become more popular due to their ability to improve developer productivity during software development. Mode…

cs.CR2025★ 1 cited

From Assistants to Adversaries: Exploring the Security Risks of Mobile LLM Agents

Liangxuan Wu, Chao Wang, Tianming Liu +2

The growing adoption of large language models (LLMs) has led to a new paradigm in mobile computing--LLM-powered mobile AI agents--capable of decomposing and automating complex task…

cs.CR2025★ 1 cited

Directed Greybox Fuzzing via Large Language Model

Hanxiang Xu, Yanjie Zhao, Haoyu Wang

Directed greybox fuzzing (DGF) focuses on efficiently reaching specific program locations or triggering particular behaviors, making it essential for tasks like vulnerability detec…

cs.CR2025★ 1 cited

Unveiling the Landscape of LLM Deployment in the Wild: An Empirical Study

Xinyi Hou, Jiahao Han, Yanjie Zhao +1

Large language models (LLMs) are increasingly deployed through open-source and commercial frameworks, enabling individuals and organizations to self-host advanced LLM capabilities.…

cs.CR2025

Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions

Xinyi Hou, Yanjie Zhao, Shenao Wang +1

The Model Context Protocol (MCP) is an emerging open standard that defines a unified, bi-directional communication and dynamic discovery protocol between AI models and external too…

cs.CR2025★ 1 cited

Demystifying LLM Supply Chain Vulnerabilities in the Wild: Distribution, Root Cause, and Real-World Impact

Shenao Wang, Yanjie Zhao, Zhao Liu +2

LLMs are rapidly transitioning from research prototypes to core components in production systems across industries such as finance and healthcare. These deployments rely on a growi…

cs.CR2024

PathSeeker: Exploring LLM Security Vulnerabilities with a Reinforcement Learning-Based Jailbreak Approach

Zhihao Lin, Wei Ma, Mingyi Zhou +5

In recent years, Large Language Models (LLMs) have gained widespread use, raising concerns about their security. Traditional jailbreak attacks, which often rely on the model intern…

cs.CR2024★ 9 cited

Models Are Codes: Towards Measuring Malicious Code Poisoning Attacks on Pre-trained Model Hubs

Jian Zhao, Shenao Wang, Yanjie Zhao +6

The proliferation of pre-trained models (PTMs) and datasets has led to the emergence of centralized model hubs like Hugging Face, which facilitate collaborative development and reu…

cs.CR2024★ 13 cited

Towards Robust Detection of Open Source Software Supply Chain Poisoning Attacks in Industry Environments

Xinyi Zheng, Chen Wei, Shenao Wang +5

The exponential growth of open-source package ecosystems, particularly NPM and PyPI, has led to an alarming increase in software supply chain poisoning attacks. Existing static ana…

cs.CR2024★ 4 cited

On the (In)Security of LLM App Stores

Xinyi Hou, Yanjie Zhao, Haoyu Wang

LLM app stores have seen rapid growth, leading to the proliferation of numerous custom LLM apps. However, this expansion raises security concerns. In this study, we propose a three…

cs.CR2024★ 1 cited

Seeing is (Not) Believing: Practical Phishing Attacks Targeting Social Media Sharing Cards

Wangchenlu Huang, Shenao Wang, Yanjie Zhao +2

In the digital era, Online Social Networks (OSNs) play a crucial role in information dissemination, with sharing cards for link previews emerging as a key feature. These cards offe…

cs.CR2024★ 27 cited

Large Language Models for Cyber Security: A Systematic Literature Review

Hanxiang Xu, Shenao Wang, Ningke Li +6

The rapid advancement of Large Language Models (LLMs) has opened up new opportunities for leveraging artificial intelligence in a variety of application domains, including cybersec…

cs.CR2024★ 1 cited

MiniScope: Automated UI Exploration and Privacy Inconsistency Detection of MiniApps via Two-phase Iterative Hybrid Analysis

Shenao Wang, Yuekang Li, Kailong Wang +4

The advent of MiniApps, operating within larger SuperApps, has revolutionized user experiences by offering a wide range of services without the need for individual app downloads. H…