AI detects chip trojans with 97% accuracy in University of Missouri study

University of Missouri researchers have developed an AI-powered method to detect hardware trojans in computer chips with 97% accuracy, using large language models to scan chip designs for malicious modifications. The breakthrough addresses a critical vulnerability in global supply chains, where hidden trojans can steal data, compromise security, or sabotage systems across industries from healthcare to defense.

Why this matters: Unlike software viruses, hardware trojans cannot be removed once a chip is manufactured and remain undetected until activated by attackers, potentially causing devastating damage to devices, data breaches, or disruption of national defense systems.

How it works: The system leverages existing large language models—the same AI technology that powers popular chatbots—to analyze chip designs for suspicious code patterns.

• The method not only identifies malicious code with 97% accuracy but also provides explanations for why specific lines are flagged as threats.
• “That explanation is critical because it saves developers from digging through thousands of lines of code,” said Ripan Kumar Kundu, a doctoral candidate at the University of Missouri leading the project.
• The system can run on local machines or through cloud services, making it accessible for both open-source developers and large corporations.

The big picture: Hardware trojans pose a unique threat because they can be inserted at almost any stage of the global chip production pipeline, from design to manufacturing, making detection extremely difficult using traditional methods.

• These malicious modifications go undetected until triggered by an attacker, potentially causing devices to malfunction, leak sensitive data, or disrupt critical infrastructure.
• Current detection methods are expensive, time-consuming, and complicated, creating gaps in security that this AI approach aims to fill.

Key advantages: The new method offers significant benefits across the chip production pipeline by enabling early detection of threats before they reach consumers.

• Companies can avoid costly recalls and reputational damage by catching trojans during the design phase rather than after production.
• The system’s flexibility allows integration into chip-design processes across multiple industries, including consumer electronics, healthcare, finance, and defense.

What’s next: The Missouri team is expanding their approach beyond detection to include real-time automated fixes and broader security applications.

• Researchers are developing capabilities to automatically repair compromised chips in real time, preventing problems before they reach production.
• The team sees potential for their method to help secure other critical systems, including power grids and infrastructure.

What they’re saying: “These chips are the foundation of our digital world,” said Khaza Anuarul Hoque, associate professor at the University of Missouri and co-author of the study. “By combining the power of artificial intelligence with an understandable explanation, we’re building tools to protect that foundation at every step of the supply chain.”

Research details: The project, titled “PEARL: An adaptive and explainable hardware trojan detection using open source and enterprise large language models,” is published in IEEE Access, with contributions from University of Missouri Curators’ Distinguished Professor Prasad Calyam and students from Columbia College and Loyola University.