OMLA: An Oracle-Less Machine Learning-Based Attack on Logic Locking

Lilas Alrahis, Satwik Patnaik, Muhammad Shafique, Ozgur Sinanoglu

Research output: Contribution to journalArticlepeer-review


Hardware-based attacks on the semiconductor supply chain are emerging due to the globalization of the design flow. Logic locking is a design-for-trust scheme that promises protection throughout the supply chain. While attacks have heavily relied on an oracle to break logic locking, machine learning (ML)-based attacks demonstrate the daunting possibility of breaking locking even without an oracle. Although very potent, current ML-based attacks recover only a subset of the transformations introduced by locking. We aim to address this shortcoming by developing an oracle-less graph neural network-based attack called OMLA, questioning once again the security of logic locking. Our experiments on ISCAS-85 and ITC-99 benchmarks demonstrate that OMLA achieves a key-prediction accuracy up to 97.22% and outperforms state-of-the-art SnapShot and SAIL attacks for all evaluated benchmarks.

Original languageEnglish (US)
Pages (from-to)1602-1606
Number of pages5
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Issue number3
StatePublished - Mar 1 2022


  • Logic locking
  • graph neural networks
  • machine learning
  • oracle-less attack

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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