Code Vulnerability Detection Across Different Programming Languages with AI Models

Security flaws in source codes written in a variety of programming languages are important and difficult to detect.Conventional static analysis systems based on rule-based patterns are ineffective at finding ruleindependent, context-dependent errors and have a high rate of false positives.Recently, new developments in artificial intelligence, notably transformer-based models, such as CodeBERT and CodeLlama, promisingly increase the models' ability to provide contextual relationships within code with a higher degree of depth.This work tests these models on heterogeneous vulnerability datasets, demonstrating that off-the-shelf models can be dynamically adapted to highly performant stability with the right adaptation on safe and vulnerable snippets.The methodology includes the collection of datasets, language normalization, fine-tuning of the model, and the combination of ensemble learning and XAI.According to experimental findings, a highly trained CodeBERT can outperform or equal some of the static analyzers with more than 97 percent accuracy.Whereas large language models may achieve close-to-optimal recall, precision can decline; therefore, hybrid open-ended and closed-ended systems are suggested to minimize false positives.In general, results indicate a high potential of AI-based solutions to generalize across languages and types of vulnerabilities, but current research remains to enhance their robustness, interpretability, and readiness to deploy.