arXiv:2607. 23710v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly integrated into software development workflows, yet their ability to autonomously generate secure authentication code remains uncertain.
By Ishpuneet Singh, Shreyas Mahajan, Gurjot Singh, Maninder Singh
The study investigates how software developers, architects, and AI practitioners select and integrate Large Language Models (LLMs) into modern software systems. Interviews with 22 professionals reveal that functional criteria—such as performance, accuracy, cost, and specific features—dominate model choice, while security concerns are rarely considered. The research highlights a pervasive neglect of established software supply‑chain security lessons, leading to vulnerabilities like malicious components, data leakage, and unintended behavior, and offers actionable recommendations for a proactive, security‑by‑design approach.
By Mahzabin Tamanna, Elizabeth Lin, Sparsha Gowda, Laurie Williams, Dominik Wermke
arXiv:2607. 23088v1 Announce Type: cross Abstract: Large Language Models (LLMs) are widely used for code generation, yet their security behavior in realistic development workflows remains underexplored.
By Lixun Ma, Ruolong Ma, Bei Wang, Feng Wei, Zhenguang Liu, Lorenzo Cavallaro, Wentao Chen
arXiv:2607. 16660v1 Announce Type: cross Abstract: The increasing adoption of Large Language Models (LLMs) as AI components in modern software systems introduces distinct security risks to the software supply chain.
By Mahzabin Tamanna, Elizabeth Lin, Sparsha Gowda, Laurie Williams, Dominik Wermke
arXiv:2606. 19474v1 Announce Type: cross Abstract: The transition to Post Quantum Cryptography (PQC) introduces considerable implementation complexity, requiring strict adherence to constant-time execution, side channel resistance, and precise parametrisation.
By R. D. N. Shakya, C. P. Wijesiriwardana, S. M. Vidanagamachchi, Nalin A. G. Arachchilage
arXiv:2601. 19138v2 Announce Type: replace-cross Abstract: Secure code review is critical during pre-integration, where Atlassian developers rely on lightweight analysis tools, while deep security assessment is deferred to later stages, delaying feedback and increasing remediation costs.
By Wachiraphan Charoenwet, Kla Tantithamthavorn, Patanamon Thongtanunam, Hong Yi Lin, Minwoo Jeong, Ming Wu