SENTAUR: Security EnhaNced Trojan Assessment Using LLMs Against Undesirable Revisions

• URL: http://arxiv.org/abs/2407.12352v1
• Date: Wed, 17 Jul 2024 07:13:06 GMT
• Title: SENTAUR: Security EnhaNced Trojan Assessment Using LLMs Against Undesirable Revisions
• Authors: Jitendra Bhandari, Rajat Sadhukhan, Prashanth Krishnamurthy, Farshad Khorrami, Ramesh Karri,
• Abstract summary: Hardware Trojan (HT) can introduce stealthy behavior, prevent an IC work as intended, or leak sensitive data via side channels. To counter HTs, rapidly examining HT scenarios is a key requirement. We propose a large language model (LLM) framework to generate a suite of legitimate HTs for a Register Transfer Level (RTL) design.
• Score: 17.21926121783922
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A globally distributed IC supply chain brings risks due to untrusted third parties. The risks span inadvertent use of hardware Trojan (HT), inserted Intellectual Property (3P-IP) or Electronic Design Automation (EDA) flows. HT can introduce stealthy HT behavior, prevent an IC work as intended, or leak sensitive data via side channels. To counter HTs, rapidly examining HT scenarios is a key requirement. While Trust-Hub benchmarks are a good starting point to assess defenses, they encompass a small subset of manually created HTs within the expanse of HT designs. Further, the HTs may disappear during synthesis. We propose a large language model (LLM) framework SENTAUR to generate a suite of legitimate HTs for a Register Transfer Level (RTL) design by learning its specifications, descriptions, and natural language descriptions of HT effects. Existing tools and benchmarks are limited; they need a learning period to construct an ML model to mimic the threat model and are difficult to reproduce. SENTAUR can swiftly produce HT instances by leveraging LLMs without any learning period and sanitizing the HTs facilitating their rapid assessment. Evaluation of SENTAUR involved generating effective, synthesizable, and practical HTs from TrustHub and elsewhere, investigating impacts of payloads/triggers at the RTL. While our evaluation focused on HT insertion, SENTAUR can generalize to automatically transform an RTL code to have defined functional modifications.

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