  High-level synthesis (HLS) is the next emerging trend for designing complex
customized architectures for applications such as Machine Learning, Video
Processing. It provides a higher level of abstraction and freedom to hardware
engineers to perform hardware software co-design. However, it opens up a new
gateway to attackers to insert hardware trojans. Such trojans are semantically
more meaningful and stealthy, compared to gate-level trojans and therefore are
hard-to-detect using state-of-the-art gate-level trojan detection techniques.
Although recent works have proposed detection mechanisms to uncover such
stealthy trojans in high-level synthesis (HLS) designs, these techniques are
either specially curated for existing trojan benchmarks or may run into
scalability issues for large designs. In this work, we leverage the power of
greybox fuzzing combined with concolic execution to explore deeper segments of
design and uncover stealthy trojans. Experimental results show that our
proposed framework is able to automatically detect trojans faster with fewer
test cases, while attaining notable branch coverage, without any manual
pre-processing analysis.
