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knavesec/README.md

whoami

Compilation of projects from my spare time. Built for pentests, red teams, and analysis.

Tools generally released with accompanying blog posts, can be found here: https://whynotsecurity.com/tags/#knavesec

Tool list:

  • CredMaster (Blog1) (Blog2) - Password spraying tool using FireProx AWS APIs to rotate request IP on every attempt. Full opsec considerations applied to not leak information
  • Max (Blog1) (Blog2) (Blog3)- BloodHound utility tool for data import/export, Domain Password Audit Tool (DPAT), analytics
  • External Email Warning Bypass (Blog) - POC to obfuscate "external email warning" banners on phishing emails via CSS injection
  • EyeWitnessTheFitness (Blog) - Generates a single FireProx API to be used for multiple pass through API hosts. Nice to be used for large EyeWitness files without generating a unique FireProx API per host
  • DPS - Distributed port scanning tool, generates high number of AWS Lambdas with unique IPs to scan hosts. Configurable to sleep between scan hosts to bypass scan prevention
  • Reverse-DNS-Info - Reverse search WHOIS records by keywords to enumerate potential alternate client root domains
  • o365fedenum (Blog)- Office365 Federated user enumeration script, based off correlated HTTP response analysis

Conference Talks:

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  1. Max Max Public

    Maximizing BloodHound. Max is a good boy.

    Python 502 60

  2. CredMaster CredMaster Public

    Refactored & improved CredKing password spraying tool, uses FireProx APIs to rotate IP addresses, stay anonymous, and beat throttling

    Python 980 132

  3. Conference-Talks Conference-Talks Public

    Compilation of Conference talks and details

    5

  4. External Email Warning Bypass External Email Warning Bypass
    1
    <!DOCTYPE html>
    2
    <html>
    3
    <head>
    4
    	<title></title>
    5
    	<style type="text/css">
  5. DPS DPS Public

    Distributed Port Scanner (Damage-Per-Second)

    Python 5

  6. o365fedenum o365fedenum Public

    Federated Office365 user enumeration based on correlated response trend analysis

    Python 47 4