11 papers
Correctness isnt Efficiency: Runtime Memory Divergence in LLM-Generated Code
Prateek Rajput, Yewei Song, Abdoul Aziz Bonkoungou +4
Large language models (LLMs) can generate programs that pass unit tests, but passing tests does not guarantee reliable runtime behavior. We find that different correct solutions to…
How Secure is Secure Code Generation? Adversarial Prompts Put LLM Defenses to the Test
Melissa Tessa, Iyiola E. Olatunji, Aicha War +2
Recent secure code generation methods, using vulnerability-aware fine-tuning, prefix-tuning, and prompt optimization, claim to prevent LLMs from producing insecure code. However, t…
Characterizing Build Compromises Through Vulnerability Disclosure Analysis
Maimouna Tamah Diao, Moustapha Awwalou Diouf, Iyiola Emmanuel Olatunji +4
The software build process transforms source code into deployable artifacts, representing a critical yet vulnerable stage in software development. Build infrastructure security pos…
Beyond Real Faces: Synthetic Datasets Can Achieve Reliable Recognition Performance without Privacy Compromise
Paweł Borsukiewicz, Fadi Boutros, Iyiola E. Olatunji +4
The deployment of facial recognition systems has created an ethical dilemma: achieving high accuracy requires massive datasets of real faces collected without consent, leading to d…
Human-Aligned Code Readability Assessment with Large Language Models
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +6
Code readability is crucial for software comprehension and maintenance, yet difficult to assess at scale. Traditional static metrics often fail to capture the subjective, context-s…
MalLoc: Toward Fine-grained Android Malicious Payload Localization via LLMs
Tiezhu Sun, Marco Alecci, Aleksandr Pilgun +5
The rapid evolution of Android malware poses significant challenges to the maintenance and security of mobile applications (apps). Traditional detection techniques often struggle t…