Aware but Unprepared: Measuring the Security Awareness-Behavior Gap in Student Use of LLM-Generated Code with Bifr"ost

arXiv:2511.20878 · cs.CR · Submitted 2025-11-25 · Read on arXiv

cs.CR

Submitted: 2025-11-25

Updated: 2026-09-20

Comments: 7 pages, Accepted at SIGCSE TS 2027

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Artificial Intelligence — 2× in this paper
• Artificial Intelligence (AI) — 2× in this paper · explained in 2 episodes
• IEEE Symposium on Security and Privacy — 2× in this paper
• Selection Bias — 2× in this paper · explained in 4 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Conference on Computer and Communications Security — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Nadia — 1× in this paper · explained in 2 episodes

Sources

• Evaluating Large Language Models Trained on Code
• StarCoder: may the source be with you!
• LLaMA: Open and Efficient Foundation Language Models

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