elearning/Frontend-Learner/node_modules/scslre/README.md
2026-01-13 10:48:02 +07:00

54 lines
3.8 KiB
Markdown

# Single-character super-linear RegExps
<sup><sub>what a name...</sub></sup>
[![Actions Status](https://github.com/RunDevelopment/scslre/workflows/CI/badge.svg)](https://github.com/RunDevelopment/scslre/actions)
[![npm](https://img.shields.io/npm/v/scslre)](https://www.npmjs.com/package/scslre)
A library to find JS RegExp with super-linear worst-case time complexity for attack strings that repeat a single character.
The static analysis method implemented by this library focuses on finding attack string tuples where a single character is repeated. This major limitation allows the library to be fast while also offering decent support for backreferences and [assertions](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions/Assertions).
This library is not intended as a full static analysis to guard against super-linear worst-case time complexity. It is meant to be as a supplementary analysis on top of existing general analysis methods that don't (or don't fully) support advanced regex features, or as a lightweight analysis on top of existing full (but heavyweight) analysis methods. Libraries that provide such general or near-full analysis are known as [recheck](https://github.com/MakeNowJust-Labo/recheck) and [vuln-regex-detector](https://github.com/davisjam/vuln-regex-detector). You may consider using these libraries as well.
## Usage
This library exports only a single function, `analyse`, which takes a RegExp literal and returns a list of reports that show the quantifiers causing super-linear worst-case time complexity.
### Documentation
For more information on the exact inputs and outputs of each function, see the full API documentation.
- [Latest release](https://rundevelopment.github.io/scslre/docs/latest/)
- [Development](https://rundevelopment.github.io/scslre/docs/dev/)
## Limitations
### Analysis
This library is implemented using a very limited static analysis method that can only find attack strings where a single character is repeated. Attack strings are generated from a tuple _(x,y,z)_ such that every string _s = xy<sup>n</sup>z_ (or `x + y.repeat(n) + z` for JS folks) takes _O(n<sup>p</sup>)_ or _O(2<sup>n</sup>)_ many steps to reject, p>1. This analysis method can only find tuples where _y_ is a single character. E.g. the polynomial backtracking in `/^(ab)*(ab)*$/` for _(x,y,z) = ("", "ab", "c")_ cannot be detected by this library because _y_ is not a single character.
However, this limitation allows the static analysis method to be quick and to provide good (but not perfect) support for backreferences and assertions (e.g. `\b`, `(?<!ba+)`).
### False negatives
The analysis method primarily searches for polynomial backtracking. Finds of exponential backtracking are only a byproduct. Because of this, not all causes of super-linear worst-case time complexity are found.
### False positives
This library doesn't actually search for the whole tuple _(x,y,z)_; it only searches for _y_ and assumes that adequate values for _x_ and _z_ can be found. A single-character approximation of the suffix _z_ will be computed and accounted for but false positives are still possible.
## Reports
There are 3 different types of reports that each indicate a different type of cause for the super-linear worst-case time complexity. All are explained in the documentation of their types.
### Exponential backtracking
While most reports show polynomial backtracking, some report exponential backtracking. Exponential backtracking is a lot more dangerous and can easily be exploited for [ReDoS attacks](https://owasp.org/www-community/attacks/Regular_expression_Denial_of_Service_-_ReDoS).
While other reports may be dismissed, __all reports of exponential backtracking must be fixed__.
All reports with `exponential: true` report exponential backtracking.