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Inventory Tweet

Attack Surface Management of public bug bounty programs.

The data we collect here includes DNS and Web Server data of public bug bounty programs.

Our aim with this project is to:

  • Monitor over 800 companies for new assets
  • help bug bounty hunters get up and running on new programs as quickly as possible.
  • give security teams better visibility into their assets.
  • reduce the load and noise that some programs face from automated tools (we run them on schedule, and give the results to everyone)

How it works

The setup consists of two workflows

  • Inventory 3.0 - Targets
  • Inventory 3.0

Inventory 3.0 - Targets

This workflow streamlines the consolidation of bug bounty program data from various sources, ensuring a comprehensive and organized view. Let's break it down:

  1. Data collection: The workflow fetches data from two important sources:

  2. Data transformation: The collected data undergoes transformation using Python scripts. The scripts convert the data into a specific format, ensuring consistency and ease of analysis. You can find the detailed data format in the targets.json file.

  3. Program merging: To avoid duplication, the workflow merges programs with the same URL together. This consolidation eliminates redundancies and presents a unified view of bug bounty programs.

  4. Community program inclusion: The workflow incorporates an additional set of programs from the community.json file. These programs are merged with the existing dataset, enhancing its coverage and diversity.

  5. Final output: The workflow generates a final consolidated JSON file, targets.json, which encompasses all the merged bug bounty program data. This file serves as a valuable resource for bug bounty researchers, providing a centralized and comprehensive view of programs.

Trickest Targets

Note: The screenshot above provides a visual representation of the workflow.

Inventory 3.0

  1. Gathering the tagets: Get the list of domains from targets.json, and extract program names.

  2. Making workflow run in parallel: Extracted program names are connected file-splitter node to make the whole workflow distributed per program

Trickest Parsing Targets

  1. Passive Enumeration:

Trickest Passive Enumeration

  1. Active Enumeration
    • Use passive enumeration data and create a new bruteforce wordlist
    • Use dsieve to get environments per subdomain level
    • Generate new potential subdomains with mksub and custom wordlist, with additional level2.txt wordlist
    • Resolve again with puredns

Trickest Active Enumeration

  1. Permutations
    • Merge active and passive results from previous steps
    • Extract environments per subdomain level again
    • Use alterx to generate permutations and resolve with puredns

Trickest Permutations

  1. Collecting previous results
    • Use python script that will get all of the previous hostnames.txt per program
    • Use anew to get the new hostnames found
    • zip active, passive, permutations per program to be pushed to repository

Trickest Previous Results

  1. Reporting
    • Use dnsx to resolve found hostnames and python script for dns-report.csv; mark newly found domains coming from anew with [x]
    • Use httpx to gather web servers and python script for `server-report.csv
    • Push to the repository

Trickest Reporting

Final workflow

Trickest Inventory 3.0 Workflow

Note: As described, almost everything in this repository is generated automatically. We carefully designed the workflows (and continue to develop them) to ensure the results are as accurate as possible.

Contribution

All contributions/ideas/suggestions are welcome! If you want to add/edit a target/workflow, feel free to send us a PR with new targets through community.json, tweet at us @trick3st, or join the conversation on Discord.

Build your own workflows!

We believe in the value of tinkering. Sign up for a demo on trickest.com to customize this workflow to your use case, get access to many more workflows, or build your own from scratch!