# s0md3v/AwesomeXSS

Awesome XSS stuff

Repository: https://github.com/s0md3v/AwesomeXSS
Canonical: https://ross.abutalabs.com/products/awesomexss
Language: JavaScript
License: MIT
License Family: permissive
Topics: xss, payload, xss-payloads, payload-list, xss-detection, xss-cheatsheet
Last push: 2024-10-30T19:01:10+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3097, "days_push": 672, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5138, forks 782 (observed 2026-08-28T04:09:11.101219+00:00)

## What it is
A curated awesome-list of cross-site scripting (XSS) resources including payloads, polyglots, cheatsheets, tools, challenges, and papers. It serves as a reference for security researchers and bug bounty hunters learning and practicing XSS exploitation.

## Use cases
- find xss payloads for bug bounty hunting
- learn cross-site scripting techniques
- study xss filter bypass methods
- find xss practice challenges
- get xss polyglot payloads
- learn dom xss exploitation

## When to choose
- you need a comprehensive reference of XSS payloads and bypass techniques
- you are learning web security and want curated XSS resources
- you are a bug bounty hunter looking for payload lists and cheatsheets

## When to avoid
- you need an automated XSS scanner rather than a reference list
- you are looking for general web security topics beyond XSS

## Facets
- artifact type: learning-resource
- maturity: active
- function: security, penetration-testing, vulnerability-scanning
- domain: security, penetration-testing, web-development, tutorials
- platform: cross-platform, browser
- tags: xss, payload-list, cheatsheet, cross-site-scripting, bug-bounty, awesome-list, web-security

## Member repositories
- s0md3v/AwesomeXSS (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:11.101219+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:02:00.991834+00:00, confidence not recorded.
  - readme: https://github.com/s0md3v/AwesomeXSS (fetched 2026-08-28T04:09:11.101219+00:00, sha e0bfd79a6688)
- Data as of 2026-08-30T08:39:29.467469+00:00.