ANT-VAT: Knowledge-guided virtual adversarial training for robust vulnerability detection

ANT-VAT is a research method that combines knowledge-guided learning with virtual adversarial training (a technique that tests AI models by feeding them deliberately tricky inputs) to improve how well AI systems can detect software vulnerabilities. The approach aims to make vulnerability detection AI more robust, meaning it works reliably even when given unusual or modified code. This research was published in December 2026 in a peer-reviewed security journal.