Farzin Gholamrezae | Searcharxiv

arXiv2026

Analysis of Commit Signing on Github

Securing the open-source software supply chain requires verifying the provenance of every code contribution. While end-to-end (E2E) cryptographic commit signing is widely promoted to achieve this, little is known about how developers actually use it at scale. We fill this gap by analyzing 2,737,649 GitHub accounts, identifying 71,694 active contributors, and examining 16,112,439 commits across 874,198 repositories to characterize their commit-signing and key-management practices throughout GitHub's history. We demonstrate that the vast majority of signed activity is generated automatically by GitHub's web interface rather than by individual developers. Genuine E2E commit signing is exceptionally rare, and the few developers who adopt it practice it erratically, frequently abandoning it over time or leaving expired keys unrevoked. Ultimately, we show that manual key management creates a false sense of security across the open-source community, and we outline structural platform interventions to resolve this failure.

cs.SE↗

arXiv2025

Ocassionally Secure: A Comparative Analysis of Code Generation Assistants

$ $Large Language Models (LLMs) are being increasingly utilized in various applications, with code generations being a notable example. While previous research has shown that LLMs have the capability to generate both secure and insecure code, the literature does not take into account what factors help generate secure and effective code. Therefore in this paper we focus on identifying and understanding the conditions and contexts in which LLMs can be effectively and safely deployed in real-world scenarios to generate quality code. We conducted a comparative analysis of four advanced LLMs--GPT-3.5 and GPT-4 using ChatGPT and Bard and Gemini from Google--using 9 separate tasks to assess each model's code generation capabilities. We contextualized our study to represent the typical use cases of a real-life developer employing LLMs for everyday tasks as work. Additionally, we place an emphasis on security awareness which is represented through the use of two distinct versions of our developer persona. In total, we collected 61 code outputs and analyzed them across several aspects: functionality, security, performance, complexity, and reliability. These insights are crucial for understanding the models' capabilities and limitations, guiding future development and practical applications in the field of automated code generation.

cs.CR↗