A Quasi-Experimental Developer Study of Security Training in LLM-Assisted Web Application Development
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arXiv
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| Format: | Preprint |
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2026
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| author | Kharma, Mohammed Sabbah, Ahmed Jarrar, Radi Zain, Samer Alkhanafseh, Mohammad Mohaisen, David |
| author_facet | Kharma, Mohammed Sabbah, Ahmed Jarrar, Radi Zain, Samer Alkhanafseh, Mohammad Mohaisen, David |
| contents | This paper presents a controlled quasi-experimental developer study examining whether a layer-based security training package is associated with improved security quality in LLM-assisted implementation of an identity-centric Java Spring Boot backend. The study uses a mixed design with a within-subject pre-training versus post-training comparison and an exploratory between-subject expertise factor. Twelve developers completed matched runs under a common interface, fixed model configuration, counterbalanced task sets, and a shared starter project. Security outcomes were assessed via independent manual validation of submitted repositories by the first and second authors. The primary participant-level endpoint was a severity-weighted validated-weakness score. The post-training condition showed a significant paired reduction under an exact Wilcoxon signed-rank test ($p = 0.0059$). In aggregate, validated weaknesses decreased from 162 to 111 (31.5\%), the severity-weighted burden decreased from 432 to 267 (38.2\%), and critical findings decreased from 24 to 5 (79.2\%). The largest reductions were in authorization and object access (53.3\%) and in authentication, credential policy, and recovery weaknesses (44.7\%). Session and browser trust-boundary issues showed minimal change, while sensitive-data and cryptographic weaknesses showed only marginal improvement.
These results suggest that, under the tested conditions, post-training runs reduce validated security burden in LLM-assisted backend development without modifying the model. They do not support replacing secure defaults, static analysis, expert review, or operational hardening. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17763 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Quasi-Experimental Developer Study of Security Training in LLM-Assisted Web Application Development Kharma, Mohammed Sabbah, Ahmed Jarrar, Radi Zain, Samer Alkhanafseh, Mohammad Mohaisen, David Cryptography and Security Machine Learning This paper presents a controlled quasi-experimental developer study examining whether a layer-based security training package is associated with improved security quality in LLM-assisted implementation of an identity-centric Java Spring Boot backend. The study uses a mixed design with a within-subject pre-training versus post-training comparison and an exploratory between-subject expertise factor. Twelve developers completed matched runs under a common interface, fixed model configuration, counterbalanced task sets, and a shared starter project. Security outcomes were assessed via independent manual validation of submitted repositories by the first and second authors. The primary participant-level endpoint was a severity-weighted validated-weakness score. The post-training condition showed a significant paired reduction under an exact Wilcoxon signed-rank test ($p = 0.0059$). In aggregate, validated weaknesses decreased from 162 to 111 (31.5\%), the severity-weighted burden decreased from 432 to 267 (38.2\%), and critical findings decreased from 24 to 5 (79.2\%). The largest reductions were in authorization and object access (53.3\%) and in authentication, credential policy, and recovery weaknesses (44.7\%). Session and browser trust-boundary issues showed minimal change, while sensitive-data and cryptographic weaknesses showed only marginal improvement. These results suggest that, under the tested conditions, post-training runs reduce validated security burden in LLM-assisted backend development without modifying the model. They do not support replacing secure defaults, static analysis, expert review, or operational hardening. |
| title | A Quasi-Experimental Developer Study of Security Training in LLM-Assisted Web Application Development |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2604.17763 |