Improving Developers' Understanding of Regex Denial of Service Tools through Anti-Patterns and Fix Strategies

TL;DR

This paper introduces novel anti-patterns and fix strategies for vulnerable regexes, significantly improving detection, fixing, and developer understanding of ReDoS vulnerabilities through a new theory of regex ambiguity.

Contribution

It presents a new theory of regex infinite ambiguity, along with anti-patterns and fix strategies, enhancing the effectiveness and usability of ReDoS detection and fixing tools.

Findings

Anti-patterns achieved 100% precision and 99% recall on large dataset

Manual application of anti-patterns was twice as effective as previous methods

Developer understanding improved from very weak to strong with anti-patterns and fix strategies

Abstract

Regular expressions are used for diverse purposes, including input validation and firewalls. Unfortunately, they can also lead to a security vulnerability called ReDoS (Regular Expression Denial of Service), caused by a super-linear worst-case execution time during regex matching. Due to the severity and prevalence of ReDoS, past work proposed automatic tools to detect and fix regexes. Although these tools were evaluated in automatic experiments, their usability has not yet been studied; usability has not been a focus of prior work. Our insight is that the usability of existing tools to detect and fix regexes will improve if we complement them with anti-patterns and fix strategies of vulnerable regexes. We developed novel anti-patterns for vulnerable regexes, and a collection of fix strategies to fix them. We derived our anti-patterns and fix strategies from a novel theory of regex infinite ambiguity - a necessary condition for regexes vulnerable to ReDoS. We proved the soundness and completeness of our theory. We evaluated the effectiveness of our anti-patterns, both in an automatic experiment and when applied manually. Then, we evaluated how much our anti-patterns and fix strategies improve developers' understanding of the outcome of detection and fixing tools. Our evaluation found that our anti-patterns were effective over a large dataset of regexes (N=209,188): 100% precision and 99% recall, improving the state of the art 50% precision and 87% recall. Our anti-patterns were also more effective than the state of the art when applied manually (N=20): 100% developers applied them effectively vs. 50% for the state of the art. Finally, our anti-patterns and fix strategies increased developers' understanding using automatic tools (N=9): from median "Very weakly" to median "Strongly" when detecting vulnerabilities, and from median "Very weakly" to median "Very strongly" when fixing them.