Symbolic Model Extraction for Web Application Verification

Abstract

Modern web applications use complex data models and access control rules which lead to data integrity and access control errors. One approach to find such errors is to use formal verification techniques. However, as a first step, most formal verification techniques require extraction of a formal model which is a difficult problem in itself due to dynamic features of modern languages, and it is typically done either manually, or using ad hoc techniques. In this paper, we present a technique called symbolic model extraction for extracting formal data models from web applications. The key ideas of symbolic model extraction are 1) to use the source language interpreter for model extraction, which enables us to handle dynamic features of the language, 2) to use code instrumentation so that execution of each instrumented piece of code returns the formal model that corresponds to that piece of code, 3) to instrument the code dynamically so that the models of methods that are created at runtime can also be extracted, and 4) to execute both sides of branches during instrumented execution so that all program behaviors can be covered in a single instrumented execution. We implemented the symbolic model extraction technique for the Rails framework and used it to extract data and access control models from web applications. Our experiments demonstrate that symbolic model extraction is scalable and extracts formal models that are precise enough to find bugs in real-world applications without reporting too many false positives.