  Due to the current horizontal business model that promotes increasing
reliance on untrusted third-party Intellectual Properties (IPs), CAD tools, and
design facilities, hardware Trojan attacks have become a serious threat to the
semiconductor industry. Development of effective countermeasures against
hardware Trojan attacks requires: (1) fast and reliable exploration of the
viable Trojan attack space for a given design and (2) a suite of high-quality
Trojan-inserted benchmarks that meet specific standards. The latter has become
essential for the development and evaluation of design/verification solutions
to achieve quantifiable assurance against Trojan attacks. While existing static
benchmarks provide a baseline for comparing different countermeasures, they
only enumerate a limited number of handcrafted Trojans from the complete Trojan
design space. To accomplish these dual objectives, in this paper, we present
MIMIC, a novel AI-guided framework for automatic Trojan insertion, which can
create a large population of valid Trojans for a given design by mimicking the
properties of a small set of known Trojans. While there exist tools to
automatically insert Trojan instances using fixed Trojan templates, they cannot
analyze known Trojan attacks for creating new instances that accurately capture
the threat model. MIMIC works in two major steps: (1) it analyzes structural
and functional features of existing Trojan populations in a multi-dimensional
space to train machine learning models and generate a large number of "virtual
Trojans" of the given design, (2) next, it binds them into the design by
matching their functional/structural properties with suitable nets of the
internal logic structure. We have developed a complete tool flow for MIMIC,
extensively evaluated the framework by exploring several use-cases, and
quantified its effectiveness to demonstrate highly promising results.
