LeakyHDL: Investigating Security Vulnerabilities in LLM-Generated HDL
Conference
Awal, MS, Di Campli, L, Bakchy, SC et al. (2026). LeakyHDL: Investigating Security Vulnerabilities in LLM-Generated HDL
. Proceedings of the IEEE Dallas Circuits and Systems Conference Dcas, (2026), 10.1109/DCAS69364.2026.11544362
Awal, MS, Di Campli, L, Bakchy, SC et al. (2026). LeakyHDL: Investigating Security Vulnerabilities in LLM-Generated HDL
. Proceedings of the IEEE Dallas Circuits and Systems Conference Dcas, (2026), 10.1109/DCAS69364.2026.11544362
Large Language Models (LLMs) are increasingly used to generate hardware code for design and verification workflows, including security-sensitive cryptographic modules. However, the security properties of such code remain insufficiently examined. This paper presents a systematic empirical study of hardware description language (HDL) generated by three widely used LLMs (ChatGPT, Gemini, Claude) for three cryptographic primitives: AES, RSA modular exponentiation, and equality comparator circuits. For each primitive and prompting regime, we assess functional correctness and side-channel security using a pattern-based vulnerability analysis with cross-model code verification. Across LLMs and prompts, the generated HDL codes are generally functionally correct but frequently contain implementation patterns known to enable timing and power sidechannel leakage, including secret-dependent branching, earlytermination logic, and lookup-table based substitutions. The results highlight a persistent gap between functional correctness and security in current LLM-generated HDL, as well as the risks of deploying such code in security-critical hardware without additional analysis and verification.
