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Statically Detecting Vulnerabilities by Processing Programming Languages as Natural Languages
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DOI：10.1109/TR.2021.3137314.png)
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Abstract
En 中文
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 vulnerabilities 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 article 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 thousand vulnerabilities belonging to 15 classes of input validation vulnerabilities, where 4143 of them were zero-day.
Keywords:
Hidden Markov models
Codes
Computer bugs
Natural language processing
Databases
Analytical models
Syntactics
Natural language processing
sequence models
software security
static analysis
vulnerabilities
web application
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