Automated Vulnerability Repair Using Prototype-Based Deep Metric Learning with Normative Compliance Constraints

Summer School on Artificial Intelligence in Cybersecurity 2026 Open access

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Authors: Aldo Hernandez-Suarez, Gabriel Sánchez-Pérez, Karina Toscano, Hector Manuel Perez-Meana, Jesús Olivares Mercado, Andrew Wilson, Marco Pérez‐Cisneros
DOI: 10.3390/engproc2026123040
Cited by: 0
References: 8

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Abstract

Automated Program Repair (APR) is increasingly used for vulnerability patching, yet many existing methods focus primarily on syntactic similarity between vulnerable and fixed code, with limited guarantees of semantic correctness and limited alignment with security frameworks.

Fields of Study & Subjects

Software Testing and Debugging Techniques Software Engineering Research Security and Verification in Computing

Publication details

Journal / source
Summer School on Artificial Intelligence in Cybersecurity
Pages
40–40
Publication date
16 March 2026
Language
EN