arXiv AI
Security in LLM-Assisted Coding: A Systematic Literature Review
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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.
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.
arXiv AI
Jul 28
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.
arXiv Computation and Language
Aug 24
Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Tasks
The paper introduces SUSVIBES, a benchmark of 186 real‑world software engineering tasks where human programmers have committed vulnerable code. It evaluates 12 popular coding‑agent settings on these tasks and finds that all agents perform poorly in terms of security, with only 11.8% of solutions from SWE‑Agent with Claude 4 Sonnet being secure despite 57% being functionally correct. Attempts to mitigate security issues by adding vulnerability hints to the prompts do not improve results.
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.
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.