Stealthy hardware Trojans (HTs) inserted during the fabrication of integrated
circuits can bypass the security of critical infrastructures. Although
researchers have proposed many techniques to detect HTs, several limitations
exist, including: (i) a low success rate, (ii) high algorithmic complexity, and
(iii) a large number of test patterns. Furthermore, the most pertinent drawback
of prior detection techniques stems from an incorrect evaluation methodology,
i.e., they assume that an adversary inserts HTs randomly. Such inappropriate
adversarial assumptions enable detection techniques to claim high HT detection
accuracy, leading to a "false sense of security." Unfortunately, to the best of
our knowledge, despite more than a decade of research on detecting HTs inserted
during fabrication, there have been no concerted efforts to perform a
systematic evaluation of HT detection techniques.
In this paper, we play the role of a realistic adversary and question the
efficacy of HT detection techniques by developing an automated, scalable, and
practical attack framework, ATTRITION, using reinforcement learning (RL).
ATTRITION evades eight detection techniques across two HT detection categories,
showcasing its agnostic behavior. ATTRITION achieves average attack success
rates of $47\times$ and $211\times$ compared to randomly inserted HTs against
state-of-the-art HT detection techniques. We demonstrate ATTRITION's ability to
evade detection techniques by evaluating designs ranging from the widely-used
academic suites to larger designs such as the open-source MIPS and mor1kx
processors to AES and a GPS module. Additionally, we showcase the impact of
ATTRITION-generated HTs through two case studies (privilege escalation and kill
switch) on the mor1kx processor. We envision that our work, along with our
released HT benchmarks and models, fosters the development of better HT
detection techniques.

本文提出了一种新的基于强化学习的攻击框架，称为 ATTRITION，其可以有效地规避目前流行的硬件木马检测技术，展示了其强大的攻击能力，从而揭示了当前检测技术的局限性，并提出了未来研究的方向。