Statically Detecting Vulnerabilities by Processing Programming Languages as Natural Languages
This addresses the challenge of automating vulnerability detection for developers in web applications, offering a novel AI-based method that is incremental in applying NLP to static analysis.
The paper tackles the problem of detecting vulnerabilities in web applications by using a natural language processing approach to automatically learn and identify flaws from source code, resulting in the discovery of several hundred vulnerabilities including 62 zero-day ones across 12 classes.
Web applications continue to be a favorite target for hackers due to a combination of wide adoption and rapid deployment cycles, which often lead to the introduction of high impact vulnerabilities. Static analysis tools are important to search for bugs automatically in the program source code, supporting developers on their removal. However, building these tools requires programming the knowledge on how to discover the vulnerabilities. This paper presents an alternative approach in which tools learn to detect flaws automatically by resorting to artificial intelligence concepts, more concretely to natural language processing. The approach employs a sequence model to learn to characterize vulnerabilities based on an annotated corpus. Afterwards, the model is utilized to discover and identify vulnerabilities in the source code. It was implemented in the DEKANT tool and evaluated experimentally with a large set of PHP applications and WordPress plugins. Overall, we found several hundred vulnerabilities belonging to 12 classes of input validation vulnerabilities, where 62 of them were zero-day.