mlsanityData to 7 Oct 2026

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

arXiv 2607.11348 ↗First posted 13 Jul 2026cs.SE cs.AIcited by 0

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Abstract (arXiv)

AI code assistants are transforming software development, but their implications for software security remain a major concern, particularly in the context of security APIs. These APIs are critical for safeguarding software systems, yet their complexity often leads to incorrect use and serious vulnerabilities. Developing an evidence-based understanding of how AI assistants influence developers' use of these APIs is therefore essential for informing effective mitigation strategies. While a few user studies have examined the broader impact of AI assistants on software vulnerabilities, the use of security APIs remains unexplored from a developer-centered perspective. This study addresses this gap by presenting the first empirical investigation into how AI code assistants affect professional developers' use of security APIs. We conducted a study with 44 developers who completed security API programming tasks with and without GitHub Copilot assistance. Our findings show that, while Copilot improves functional correctness and marginally reduces certain insecure patterns, it does not significantly improve secure API usage. We also found that developers rarely raised security concerns when engaging with Copilot, and many did not recognize that their final implementations remained insecure. Finally, we offer recommendations for enhancing security awareness among developers and propose future research directions to support safer AI-assisted software development.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 62 references that are in the map. First posted on arXiv 13 Jul 2026.

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References · 23

  1. 2021Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributionscited by 417
  2. 2022Do Users Write More Insecure Code with AI Assistants?cited by 186
  3. 2023Large Language Models for Code: Security Hardening and Adversarial Testingcited by 149
  4. 2022Productivity Assessment of Neural Code Completioncited by 97
  5. 2022Lost at C: A User Study on the Security Implications of Large Language Model Code Assistantscited by 87
  6. 2023How Secure is Code Generated by ChatGPT?cited by 77
  7. 2022Is GitHub's Copilot as Bad as Humans at Introducing Vulnerabilities in Code?cited by 63
  8. 2020Big Code != Big Vocabulary: Open-Vocabulary Models for Source Codecited by 53
  9. 2023No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPTcited by 45
  10. 2023Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Studycited by 36
  11. 2024Constrained Decoding for Secure Code Generationcited by 35
  12. 2024Using AI Assistants in Software Development: A Qualitative Study on Security Practices and Concernscited by 27
11 more
  1. 2023A User-centered Security Evaluation of Copilotcited by 14
  2. 2025Benchmarking Prompt Engineering Techniques for Secure Code Generation with GPT Modelscited by 12
  3. 2024An Investigation into Misuse of Java Security APIs by Large Language Modelscited by 11
  4. 2025CASTLE: Benchmarking Dataset for Static Code Analyzers and LLMs towards CWE Detectioncited by 10
  5. 2018CryptoGuard: High Precision Detection of Cryptographic Vulnerabilities in Massive-sized Java Projectscited by 8
  6. 2025Do LLMs Consider Security? An Empirical Study on Responses to Programming Questionscited by 8
  7. 2024Systematic Literature Review on Application of Learning-based Approaches in Continuous Integrationcited by 6
  8. 2023SoK: Machine Learning for Continuous Integrationcited by 4
  9. 2023Detecting Misuse of Security APIs: A Systematic Reviewcited by 4
  10. 2024A Qualitative Study on Using ChatGPT for Software Security: Perception vs. Practicalitycited by 2
  11. 2026Policy Gradient Primal-Dual Method for Safe Reinforcement Learning from Human Feedbackcited by 2

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