Project Ire
Autonomous malware classification
Nouvelles et reportages
Ire identifies another LOTUSLITE specimen
| Brian Caswell, Bob Fleck, Mike Walker, et Sarah Smith
Project Ire examined a timely malware sample and determined its intent through reverse engineering—identifying LOTUSLITE characteristics even as most major EDR tools did not detect it.
Dans l’actualité | Infosecurity Magazine
Microsoft Debuts AI Agent Able to Reverse Engineer Malware
Project Ire autonomously identifies malware at scale
| Brian Caswell, Dustin Fraze, Sarah Smith, Rodrigo Racanicci, Tim Middleton-Sally, Shelby Hayes, Stanley He, Katy Smith, Bhakta Pradhan, et Mike Walker
Designed to classify software without context, Project Ire replicates the gold standard in malware analysis through reverse engineering. It streamlines a complex, expert-driven process, making large-scale malware detection faster & more consistent.