  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.
