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This benchmark reveals a critical gap in AI coding agents, showing they can generate functional code but fail to embed security practices when not explicitly prompted, with success rates dropping below 26% even for top models. The study highlights that current frontier LLMs struggle significantly against real-world vulnerability scenarios derived from the CWE Top 25 and KEV weaknesses without direct security instructions. This finding underscores an urgent need for improved alignment between functional generation and secure coding practices in automated development workflows.
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Last Secure Code Benchmark: Is the Code You Vibe Secure?

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2610.14766v1 Announce Type: cross Abstract: AI coding agents now write much of the code that ships, yet whether that code is secure remains unclear. We introduce the Last Secure Code Benchmark, a comprehensive evaluation of secure code generation by coding agents. Built from 150 real vulnerabilities, its 450 tasks span seven widely used languages (Java, C, C++, JavaScript, Python, PHP, and Go), 24 CWEs including 21 of the 2025 CWE Top 25 and 8 of the Top 10 KEV weaknesses, more than any benchmark we compare with, and three vibe-coding scenarios: filling in a function, writing a file, and building a repository's source tree from scratch. Every task asks only for functionality, through a specification that never mentions security, and hidden functional and security tests grade the result. Across six frontier agents, 73.6% to 91.3% of tasks pass functionally, yet even the strongest, Claude Fable 5, produces code that is both functional and secure for only 25.1% of tasks, and the others for 16.4% to 24.7%. The best model only passes 12% of the vulnerability scenarios across all vibe coding setting, posing strong concern on the security of the code written by vibe coding.

Title:Last Secure Code Benchmark: Is the Code You Vibe Secure?

Abstract:AI coding agents now write much of the code that ships, yet whether that code is secure remains unclear. We introduce the Last Secure Code Benchmark, a comprehensive evaluation of secure code generation by coding agents. Built from 150 real vulnerabilities, its 450 tasks span seven widely used languages (Java, C, C++, JavaScript, Python, PHP, and Go), 24 CWEs including 21 of the 2025 CWE Top 25 and 8 of the Top 10 KEV weaknesses, more than any benchmark we compare with, and three vibe-coding scenarios: filling in a function, writing a file, and building a repository's source tree from scratch. Every task asks only for functionality, through a specification that never mentions security, and hidden functional and security tests grade the result. Across six frontier agents, 73.6% to 91.3% of tasks pass functionally, yet even the strongest, Claude Fable 5, produces code that is both functional and secure for only 25.1% of tasks, and the others for 16.4% to 24.7%. The best model only passes 12% of the vulnerability scenarios across all vibe coding setting, posing strong concern on the security of the code written by vibe coding.

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

arXiv:2610.14766 [cs.CR]

(or arXiv:2610.14766v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2610.14766 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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