arXiv AI July 28, 2026 By Ishpuneet Singh, Shreyas Mahajan, Gurjot Singh, Maninder Singh

The Illusion of Secure LLM Code: Closing the Security Gap via Iterative Reprompting

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

llms

arXiv AI

Jul 28

Poster: Rethinking Security in LLM Code Generation through Real-World Risk Scenarios

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

llms benchmarks

arXiv AI

Aug 5

AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection

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

llms agents

arXiv Computation and Language

Sep 15

Toward Secure Code Generation: Bridging Correctness and Security via Task-Adaptive Vulnerability Modeling and Execution-Based Benchmarking

arXiv:2407.02395v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for program synthesis, yet they often generate code that is functionally plausible but ins...

By Jiexin Wang, Liuwen Cao, Xitong Luo, Yang Cao, Zhenghao Li, Yunyi Xiao, Mengchen Zhao, Adam Jatowt, Yi Cai

llms agents benchmarks

arXiv AI

Jul 14

Understanding the Impact of AI Code Assistants on Security API Usage: An Empirical Study

arXiv:2607. 11348v1 Announce Type: cross Abstract: AI code assistants are transforming software development, but their implications for software security remain a major concern, particularly in the context of security APIs.

By Zahra Mousavi, Chadni Islam, M. Ali Babar, Alsharif Abuadbba, Kristen Moore

arXiv AI

Jun 19

Secure Coding Drift in LLM-Assisted Post-Quantum Cryptography Development: A Gamified Fix

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

llms safety

arXiv AI

Jun 29

Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs

arXiv:2601. 21233v2 Announce Type: replace Abstract: Autonomous code agents built on large language models are reshaping software and AI development through tool use, long-horizon reasoning, and self-directed interaction.

By Xiang Zheng, Yutao Wu, Hanxun Huang, Yige Li, Xingjun Ma, Bo Li, Yu-Gang Jiang, Cong Wang

llms agents safety

One email a morning, machine-written

Related stories

Poster: Rethinking Security in LLM Code Generation through Real-World Risk Scenarios

AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection

Toward Secure Code Generation: Bridging Correctness and Security via Task-Adaptive Vulnerability Modeling and Execution-Based Benchmarking

Understanding the Impact of AI Code Assistants on Security API Usage: An Empirical Study

Secure Coding Drift in LLM-Assisted Post-Quantum Cryptography Development: A Gamified Fix

Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs