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This repo contains a bunch of red team / pentest .ps1 scripts and tools which used after obfuscation as dataset for ML models training.

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#RTOScripts

A curated collection of small tools and helper scripts for red-team engagements. Use them standalone (not recommended, well-detected) or weave them into your own custom tool chain.

I used this repository as a playground for experimenting with obfuscation techniques and for generating labeled datasets for machine-learning tasks (mainly classification).


Script highlights (quick analysis)

All content is PowerShell. A quick map of what’s inside with a few examples:

  • Execution & exploits: Exploit-EternalBlue.ps1, Exploit-JBoss.ps1, Exploit-Jenkins.ps1, Execute-Command-MSSQL.ps1, Download_Execute.ps1, DLLEnc.ps1.
  • Data exfiltration channels: Exfil-Dns.ps1, Exfil-Http.ps1, Exfil-HTTPS.ps1, Exfil-Icmp.ps1, Exfil-Smtp.ps1, Exfil-Ftp.ps1, Do-Exfiltration.ps1, DNS_TXT_Pwnage.ps1, ExfilDataStreamDNS.ps1, ExfilEmail.ps1.
  • Credentials & browser artifacts: Get-ClearTextPassword.ps1, Get-ChromeDump.ps1, Get-BrowserData.ps1, 203_Helpers_Credential.ps1, 308_Credential.ps1.
  • AD / Privilege / Lateral movement: Get-AdminGroup.ps1, Get-DomainController.ps1, Enable-DuplicateToken.ps1, Add-ConstrainedDelegationBackdoor.ps1.
  • Persistence: Add-Persistence.ps1, Add-RegBackdoor.ps1, Add-ScrnSaveBackdoor.ps1, Add-UserLogonScript.ps1, 303_ScheduledTask.ps1.
  • Evasion / Defense bypass: DisableDefender.ps1, Find-AVSignature.ps1, ExcludeProcess.ps1, ExcludeFolder.ps1, ExcludeFileExtension.ps1.
  • Discovery / situational awareness: ComputerInfo.ps1, Get-ComputerDetails.ps1, Find-Fruit.ps1, GPSLocation.ps1.
  • Ecosystem helpers: BloodHound.ps1, AzureHound.ps1, Create-Log4jPayload.ps1, assorted Helpers_* utilities.

Tip: Many scripts are paired with helper modules (000_Global.ps1, 200_Helpers.ps1, etc.) to keep primitives reusable.

ML dataset use:

This repo doubles as a dataset for obfuscation-detection research:

  • Sources were taken as-is (full files) and also split into fixed-size chunks.
  • Each sample was labeled as original or obfuscated (multiple reversible transforms were applied: identifier renaming, spacing/comment noise, string encodings, etc.).
  • Trained and evaluated various ML classifiers (n-gram/TF-IDF + linear models; transformer baselines on chunks) on the task obfuscated vs. not at chunk level, with optional aggregation to file level.

License

MIT — see LICENSE.

About

This repo contains a bunch of red team / pentest .ps1 scripts and tools which used after obfuscation as dataset for ML models training.

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