SCPrompt: Semantic Compression and Prompt-Guided LLM Reasoning for RTL Trojan Detection

The increasing scale and complexity of integrated circuit (IC) designs present significant challenges to hardware security. Hardware Trojans (HTs)-stealthy, malicious alterations to hardware logic-are especially difficult to detect at the register-transfer level (RTL), where both structural and semantic understanding are essential. While recent advances have explored the use of large language models (LLMs) for RTL analysis, their performance is often constrained by token limits and a lack of targeted semantic abstraction. To overcome these limitations, we propose SCPrompt, a novel and extensible framework that, for the first time, integrates RTL instance-level compression with prompt-guided LLM reasoning for hardware Trojan detection. SCPrompt compresses RTL inputs by over 90%, significantly reducing prompt length while retaining critical control and data flow semantics. This enables LLMs to perform accurate Trojan analysis without taskspecific fine-tuning. Unlike prior binary classification approaches, SCPrompt supports instance-grained localization of suspicious logic components, identifying both trigger and payload modules within a design. Evaluated on 51 RTL designs with hardware Trojans inserted, our method achieves up to 100% precision, 90% recall, and an F1 score of 94.74%, demonstrating strong generalization across diverse circuits. These results validate SCPrompt as the first effective and scalable framework to unify RTL feature engineering with general-purpose language model reasoning for advanced hardware security analysis.