Vulnerability DatabaseGHSA-8w7w-67mw-r5p7

GHSA-8w7w-67mw-r5p7
JavaScript vulnerability analysis and mitigation

Overview

A Regular Expression Denial of Service (ReDoS) vulnerability was identified in generator-jhipster affecting versions >6.8.0 and <6.9.0. The vulnerability was discovered and disclosed on May 17, 2020, affecting applications using JWT or session-based authentication (not OIDC). The vulnerable expression was never officially released and only affected applications generated from the master branch between versions 6.8.0 and 6.9.0 (GitHub Advisory).

Technical details

The vulnerability exists in the login check functionality where a malicious login string input could cause a denial of service due to catastrophic backtracking in the regular expression pattern matching. The issue specifically affects the LOGIN_REGEX pattern in Constants.java when it uses the vulnerable regex pattern ^[a-zA-Z0-9!#$&'+=?^_`{|}~.-]+@?[a-zA-Z0-9-]+(?:.[a-zA-Z0-9-]+)$ (GitHub Advisory).

Impact

When exploited, this vulnerability can cause a denial of service condition in affected applications by making the parsing of login strings computationally expensive, potentially leading to system resource exhaustion (GitHub Advisory).

Exploitability

The vulnerability can be triggered by submitting specially crafted login strings that cause the regular expression engine to enter a catastrophic backtracking state. A proof of concept demonstrating the vulnerability has been published, showing how certain input patterns can cause excessive processing time (GitHub Gist).

Mitigation and workarounds

For applications created from the master branch, the LOGIN_REGEX in Constants.java needs to be modified. The vulnerable regex pattern should be replaced with ^(?>[a-zA-Z0-9!$&+=?^_`{|}~.-]+@[a-zA-Z0-9-]+(?:.[a-zA-Z0-9-]+))|(?>[.@A-Za-z0-9-]+)$. Applications using the pattern ^[.@A-Za-z0-9-]*$ do not need modification unless support for the + sign in logins is required (GitHub Advisory).

Additional resources


SourceThis report was generated using AI

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