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Logic locking aims to prevent intellectual property (IP) piracy andunauthorized overproduction of integrated circuits (ICs). However, initiallogic locking techniques were vulnerable to the Boolean satisfiability(SAT)-based attacks. In response, researchers proposed various SAT-resistantlocking techniques such as point function-based locking and symmetricinterconnection (SAT-hard) obfuscation. We focus on the latter since pointfunction-based locking suffers from various structural vulnerabilities. TheSAT-hard logic locking technique, InterLock [1], achieves a unified logic androuting obfuscation that thwarts state-of-the-art attacks on logic locking. Inthis work, we propose a novel link prediction-based attack, UNTANGLE, thatsuccessfully breaks InterLock in an oracle-less setting without having accessto an activated IC (oracle). Since InterLock hides selected timing paths inkey-controlled routing blocks, UNTANGLE reveals the gates and interconnectionshidden in the routing blocks upon formulating this task as a link predictionproblem. The intuition behind our approach is that ICs contain a large amountof repetition and reuse cores. Hence, UNTANGLE can infer the hidden timingpaths by learning the composition of gates in the observed locked netlist or acircuit library leveraging graph neural networks. We show that circuitswithstanding SAT-based and other attacks can be unlocked in seconds with 100%precision using UNTANGLE in an oracle-less setting. UNTANGLE is a genericattack platform (which we also open source [2]) that applies to multiplexer(MUX)-based obfuscation, as demonstrated through our experiments on ISCAS-85and ITC-99 benchmarks locked using InterLock and random MUX-based locking.

UNTANGLE: Unlocking Routing and Logic Obfuscation Using Graph Neural Networks-based Link Prediction