As cyberattacks become increasingly automated and amplified by emerging technologies, particularly Artificial Intelligence (AI), reliably assessing the security resilience of software systems becomes crucial. However, traditional methods centered on known vulnerabilities provide limited insight into attack impact. Intr...
Charles F. Gonçalves, Marco Vieira· Proceedings of the ACM on So...· 0 citations
Large language models (LLMs) demonstrate strong capabilities in code-related tasks, however their effectiveness in software vulnerability detection (SVD) remains poorly understood due to inadequate evaluation frameworks. Existing benchmarks suffer from training data contamination, isolated function evaluation without c...
Arastoo Zibaeirad, Rodrigo Pato Nogueira, Marco Vieira· IEEE Transactions on Reliabi...· 0 citations
It is observed that generated code often omits basic input validation or memory-safety checks, which can lead to overflows, resource exhaustion, or other reliability/security issues, and even the largest models frequently make simple mistakes.
Rodrigo Pato Nogueira, Marco Vieira, João R. Campos· 0 citations
The findings show that, while LLMs achieve promising results, they struggle with harder problems and with programming languages that have fewer available resources for training, and they often fail due to fundamental and easily avoidable errors that underscore the unreliability of automatically generated code.
Rodrigo Pato Nogueira, Marco Vieira, João R. Campos· Empirical Software Engineeri...· 1 citation
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