6 papers
An Empirical Study of Security Calibration in Large Language Models for Code
Mohammed Latif Siddiq, Md. Nafiu Rahman, Joanna C. S. Santos
Large Language Models (LLMs) are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do models know when their generated c…
An Empirical Study on Remote Code Execution in Machine Learning Model Hosting Ecosystems
Mohammed Latif Siddiq, Tanzim Hossain Romel, Natalie Sekerak +2
Model-sharing platforms, such as Hugging Face, ModelScope, and OpenCSG, have become central to modern machine learning development, enabling developers to share, load, and fine-tun…
Security in the Age of AI Teammates: An Empirical Study of Agentic Pull Requests on GitHub
Mohammed Latif Siddiq, Xinye Zhao, Vinicius Carvalho Lopes +2
Autonomous coding agents are increasingly deployed as AI teammates in modern software engineering, independently authoring pull requests (PRs) that modify production code at scale.…
Assessing the Software Security Comprehension of Large Language Models
Mohammed Latif Siddiq, Natalie Sekerak, Antonio Karam +3
Large language models (LLMs) are increasingly used in software development, but their level of software security expertise remains unclear. This work systematically evaluates the s…
Large Language Models for Software Engineering: A Reproducibility Crisis
Mohammed Latif Siddiq, Arvin Islam-Gomes, Natalie Sekerak +1
Reproducibility is a cornerstone of scientific progress, yet its state in large language model (LLM)-based software engineering (SE) research remains poorly understood. This paper…
Large Language Models in Computer Science Education: A Systematic Literature Review
Nishat Raihan, Mohammed Latif Siddiq, Joanna C. S. Santos +1
Large language models (LLMs) are becoming increasingly better at a wide range of Natural Language Processing tasks (NLP), such as text generation and understanding. Recently, these…