Zobrazeno 1 - 10
of 236
pro vyhledávání: '"Chen, Taolue"'
Code Language Models (CLMs), particularly those leveraging deep learning, have achieved significant success in code intelligence domain. However, the issue of security, particularly backdoor attacks, is often overlooked in this process. The previous
Externí odkaz:
http://arxiv.org/abs/2407.08956
Recent studies in neuro-symbolic learning have explored the integration of logical knowledge into deep learning via encoding logical constraints as an additional loss function. However, existing approaches tend to vacuously satisfy logical constraint
Externí odkaz:
http://arxiv.org/abs/2403.00329
Neuro-symbolic learning generally consists of two separated worlds, i.e., neural network training and symbolic constraint solving, whose success hinges on symbol grounding, a fundamental problem in AI. This paper presents a novel, softened symbol gro
Externí odkaz:
http://arxiv.org/abs/2403.00323
Timing side-channel attacks exploit secret-dependent execution time to fully or partially recover secrets of cryptographic implementations, posing a severe threat to software security. Constant-time programming discipline is an effective software-bas
Externí odkaz:
http://arxiv.org/abs/2402.13506
Large Language Models (LLMs) have demonstrated remarkable potential in code generation. The integration of Chain of Thought (CoT) reasoning can further boost their performance. However, current CoT methods often require manual writing or LLMs with ov
Externí odkaz:
http://arxiv.org/abs/2312.05562
Autor:
Huang, Yunpeng, Xu, Jingwei, Lai, Junyu, Jiang, Zixu, Chen, Taolue, Li, Zenan, Yao, Yuan, Ma, Xiaoxing, Yang, Lijuan, Chen, Hao, Li, Shupeng, Zhao, Penghao
Transformer-based Large Language Models (LLMs) have been applied in diverse areas such as knowledge bases, human interfaces, and dynamic agents, and marking a stride towards achieving Artificial General Intelligence (AGI). However, current LLMs are p
Externí odkaz:
http://arxiv.org/abs/2311.12351
Context: Pre-trained models (PTMs) have demonstrated significant potential in automatic code translation. However, the vulnerability of these models in translation tasks, particularly in terms of syntax, has not been extensively investigated. Objecti
Externí odkaz:
http://arxiv.org/abs/2310.18587
Fault injection attacks represent a type of active, physical attack against cryptographic circuits. Various countermeasures have been proposed to thwart such attacks, the design and implementation of which are, however, intricate, error-prone, and la
Externí odkaz:
http://arxiv.org/abs/2307.00561
Masking is a widely-used effective countermeasure against power side-channel attacks for implementing cryptographic algorithms. Surprisingly, few formal verification techniques have addressed a fundamental question, i.e., whether the masked program a
Externí odkaz:
http://arxiv.org/abs/2305.16596
Due to the development of pre-trained language models, automated code generation techniques have shown great promise in recent years. However, the generated code is difficult to meet the syntactic constraints of the target language, especially in the
Externí odkaz:
http://arxiv.org/abs/2303.05061