Artificial Intelligence Cyber Challenge (AIxCC)
Abstract
The Artificial Intelligence Cyber Challenge (AIxCC) program, addressing issues encountered in the Program Analysis for Capability Excellence (PACE) program (budgeted in PE 0602303E, Project IT-03), seeks to develop and demonstrate techniques for automated discovery and remediation of software vulnerabilities at speed and at scale to secure widely used, critical code. Current automated vulnerability discovery and remediation tools are based on techniques such as fuzzing, logical reasoning, and genetic algorithms, but are limited in terms of effectiveness and user support. AIxCC will leverage recent dramatic advances in artificial intelligence (AI) and machine learning, such as large pre-trained models (LPTMs) and neurosymbolic AI, as the basis for new automated cyber security technologies and tools. AIxCC will use a contest model where teams will use their automation and tooling to complete vulnerability discovery and remediation challenges. Performer teams will be selected for the AIxCC competition based on their capability to leverage advances in AI to create usable, automated tools for vulnerability discovery and remediation, with a focus on tools suitable for broad deployment and applicable to critical infrastructure sectors. AIxCC competitors will train and develop their systems to find and fix vulnerabilities in widely-used open source software, focusing on software used in critical infrastructure. Each competitor system will be evaluated on real-world critical infrastructure software suites and will be scored based on their results both in terms of absolute performance and performance relative to other competitor systems. Winning teams will receive cash awards. If successful, AIxCC will create novel AI-enabled cyber vulnerability remediation technology and tools for securing code at the scale and speed needed to defend U.S. critical infrastructure from cyber attacks.
Document Details
- Document Type
- Accomplishment
- Publication Date
- Oct 01, 2025
- Source ID
- d9aa9afea33f780229625f902e71b035