activity
20232026
most citedSALLM: Security Assessment of Generated Code

19 citations · 23 across the 9 of their papers we have counts for

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7 papers · 1 filter

cs.SE2026

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…

cs.SE2026

Automated Detection of Configuration-Specific Security Vulnerabilities via Patch Analysis

Felipe de Sant'Anna Paixão, Joanna C. S. Santos, Paulo Anselmo da Mota Silveira Neto +3

We study how security patches in highly configurable C/C++ systems map onto the space of compile-time variants. We formalize the Vulnerability Impact Condition (VIC) - a Boolean pr…

cs.SE2026

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…

cs.SE2025

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…

cs.SE2025

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…

cs.SE2024★ 4 cited

The Fault in our Stars: Quality Assessment of Code Generation Benchmarks

Mohammed Latif Siddiq, Simantika Dristi, Joy Saha +1

Large Language Models (LLMs) are gaining popularity among software engineers. A crucial aspect of developing effective code generation LLMs is to evaluate these models using a robu…

cs.SE2023★ 19 cited

SALLM: Security Assessment of Generated Code

Mohammed Latif Siddiq, Joanna C. S. Santos, Sajith Devareddy +1

With the growing popularity of Large Language Models (LLMs) in software engineers' daily practices, it is important to ensure that the code generated by these tools is not only fun…