5 papers
Assessing Large Language Models in Comprehending and Verifying Concurrent Programs across Memory Models
Ridhi Jain, Rahul Purandare
As concurrent programming becomes increasingly prevalent, effectively identifying and addressing concurrency issues such as data races and deadlocks is critical. This study evaluat…
Vulnerability Detection: From Formal Verification to Large Language Models and Hybrid Approaches: A Comprehensive Overview
Norbert Tihanyi, Tamas Bisztray, Mohamed Amine Ferrag +4
Software testing and verification are critical for ensuring the reliability and security of modern software systems. Traditionally, formal verification techniques, such as model ch…
SecureFalcon: Are We There Yet in Automated Software Vulnerability Detection with LLMs?
Mohamed Amine Ferrag, Ammar Battah, Norbert Tihanyi +8
Software vulnerabilities can cause numerous problems, including crashes, data loss, and security breaches. These issues greatly compromise quality and can negatively impact the mar…
How secure is AI-generated Code: A Large-Scale Comparison of Large Language Models
Norbert Tihanyi, Tamas Bisztray, Mohamed Amine Ferrag +2
This study compares state-of-the-art Large Language Models (LLMs) on their tendency to generate vulnerabilities when writing C programs using a neutral zero-shot prompt. Tihanyi et…
Dynamic Intelligence Assessment: Benchmarking LLMs on the Road to AGI with a Focus on Model Confidence
Norbert Tihanyi, Tamas Bisztray, Richard A. Dubniczky +11
As machine intelligence evolves, the need to test and compare the problem-solving abilities of different AI models grows. However, current benchmarks are often simplistic, allowing…