An Empirical Security Evaluation of LLM-Generated Cryptographic Rust Code
Mohamed Elsayed; Kenneth Fulton; Jeong Yang · 2026 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Developers and organizations are using Large Language Models (LLMs) to generate security-critical code more frequently than ever, including cryptographic solutions for their products. This study presents an empirical evaluation of cryptographic security in 240 Rust code samples for two crypto algorithms (AES-256-GCM and ChaCha20-Poly1305) generated by three LLMs (Gemini 2.5 Pro, GPT-4o, and DeepSeek Coder) using four different prompt strategies. For each successfully compiled code sample, CodeQL static analysis and our rule-based crypto-specific analyzer were used to detect vulnerabilities, wh
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Metadata source: arXiv