  Logic locking has been proposed to safeguard intellectual property (IP)
during chip fabrication. Logic locking techniques protect hardware IP by making
a subset of combinational modules in a design dependent on a secret key that is
withheld from untrusted parties. If an incorrect secret key is used, a set of
deterministic errors is produced in locked modules, restricting unauthorized
use. A common target for logic locking is neural accelerators, especially as
machine-learning-as-a-service becomes more prevalent. In this work, we explore
how logic locking can be used to compromise the security of a neural
accelerator it protects. Specifically, we show how the deterministic errors
caused by incorrect keys can be harnessed to produce neural-trojan-style
backdoors. To do so, we first outline a motivational attack scenario where a
carefully chosen incorrect key, which we call a trojan key, produces
misclassifications for an attacker-specified input class in a locked
accelerator. We then develop a theoretically-robust attack methodology to
automatically identify trojan keys. To evaluate this attack, we launch it on
several locked accelerators. In our largest benchmark accelerator, our attack
identified a trojan key that caused a 74\% decrease in classification accuracy
for attacker-specified trigger inputs, while degrading accuracy by only 1.7\%
for other inputs on average.
