32 citations · 71 across the 10 of their papers we have counts for
11 papers
When Code Crosses Borders: A Security-Centric Study of LLM-based Code Translation
Hailong Chang, Guozhu Meng, Shuhui Xiao +3
Code translation is crucial for cross-language codebase migration, and large language models (LLMs) have emerged as a promising technique to automate this process. However, the sec…
The Art of Hide and Seek: Making Pickle-Based Model Supply Chain Poisoning Stealthy Again
Tong Liu, Guozhu Meng, Peng Zhou +3
Pickle deserialization vulnerabilities have persisted throughout Python's history, remaining widely recognized yet unresolved. Due to its ability to transparently save and restore…
Bleeding Pathways: Vanishing Discriminability in LLM Hidden States Fuels Jailbreak Attacks
Yingjie Zhang, Tong Liu, Zhe Zhao +2
LLMs remain vulnerable to jailbreak attacks that exploit adversarial prompts to circumvent safety measures. Current safety fine-tuning approaches face two critical limitations. Fir…
A Survey on Physical Adversarial Attacks against Face Recognition Systems
Mingsi Wang, Jiachen Zhou, Tianlin Li +2
As Face Recognition (FR) technology becomes increasingly prevalent in finance, the military, public safety, and everyday life, security concerns have grown substantially. Physical…
AuthNet: Neural Network with Integrated Authentication Logic
Yuling Cai, Fan Xiang, Guozhu Meng +2
Model stealing, i.e., unauthorized access and exfiltration of deep learning models, has become one of the major threats. Proprietary models may be protected by access controls and…
Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Reconstruction
Tong Liu, Yingjie Zhang, Zhe Zhao +3
In recent years, large language models (LLMs) have demonstrated notable success across various tasks, but the trustworthiness of LLMs is still an open problem. One specific threat…
DataElixir: Purifying Poisoned Dataset to Mitigate Backdoor Attacks via Diffusion Models
Jiachen Zhou, Peizhuo Lv, Yibing Lan +3
Dataset sanitization is a widely adopted proactive defense against poisoning-based backdoor attacks, aimed at filtering out and removing poisoned samples from training datasets. Ho…
Good-looking but Lacking Faithfulness: Understanding Local Explanation Methods through Trend-based Testing
Jinwen He, Kai Chen, Guozhu Meng +2
While enjoying the great achievements brought by deep learning (DL), people are also worried about the decision made by DL models, since the high degree of non-linearity of DL mode…
Demystifying RCE Vulnerabilities in LLM-Integrated Apps
Tong Liu, Zizhuang Deng, Guozhu Meng +2
LLMs show promise in transforming software development, with a growing interest in integrating them into more intelligent apps. Frameworks like LangChain aid LLM-integrated app dev…
ConFL: Constraint-guided Fuzzing for Machine Learning Framework
Zhao Liu, Quanchen Zou, Tian Yu +4
As machine learning gains prominence in various sectors of society for automated decision-making, concerns have risen regarding potential vulnerabilities in machine learning (ML) f…
Understanding Real-world Threats to Deep Learning Models in Android Apps
Zizhuang Deng, Kai Chen, Guozhu Meng +3
Famous for its superior performance, deep learning (DL) has been popularly used within many applications, which also at the same time attracts various threats to the models. One pr…