33 citations · 58 across the 15 of their papers we have counts for
5 papers · 1 filter
Can Neural Decompilation Assist Vulnerability Prediction on Binary Code?
D. Cotroneo, F. C. Grasso, R. Natella +1
Vulnerability prediction is valuable in identifying security issues efficiently, even though it requires the source code of the target software system, which is a restrictive hypot…
Enhancing AI-based Generation of Software Exploits with Contextual Information
Pietro Liguori, Cristina Improta, Roberto Natella +2
This practical experience report explores Neural Machine Translation (NMT) models' capability to generate offensive security code from natural language (NL) descriptions, highlight…
Neural Fault Injection: Generating Software Faults from Natural Language
Domenico Cotroneo, Pietro Liguori
Traditional software fault injection methods, while foundational, face limitations in adequately representing real-world faults, offering customization, and requiring significant m…
DeVAIC: A Tool for Security Assessment of AI-generated Code
Domenico Cotroneo, Roberta De Luca, Pietro Liguori
Context: AI code generators are revolutionizing code writing and software development, but their training on large datasets, including potentially untrusted source code, raises sec…
AI Code Generators for Security: Friend or Foe?
Roberto Natella, Pietro Liguori, Cristina Improta +2
Recent advances of artificial intelligence (AI) code generators are opening new opportunities in software security research, including misuse by malicious actors. We review use cas…