AI-Powered IOC Parsing for WRECKSTEEL Detection in CrowdStrike

How It Works

Uncoder AI automates the decomposition of complex IOC-driven detection logic authored in CrowdStrike Endpoint Query Language (EQL). This example centers around the CERT-UA#14283 report, targeting WRECKSTEEL — a PowerShell-based infostealer.

The AI engine interprets an extensive detection rule designed to match various execution chains linked to WRECKSTEEL, enabling analysts to quickly understand how the rule correlates hashes, file paths, URLs, and command-line behaviors across multiple telemetry points.

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Key Detection Components in the EQL Rule

1. Event IDs & Execution Contexts
   The rule tracks both event_id=1 and event_id=4688 — representing process creation and image load events — to ensure deep process visibility.
   
2. Process & Scripting Engine Detection
   It flags instances of:
   
   • powershell.exe with -ExecutionPolicy Bypass
     
   • wscript.exe executing AppFinalDesktop.vbs
     
   • i_view64.exe from IrfanView acting as a likely decoy/delivery mechanism
     
3. Network IOCs
   The rule includes over 30 hardcoded URLs and IPs associated with C2 and payload delivery, covering:
   
   • dropmefiles.com
     
   • fshara.com
     
   • Direct IP-based downloads from 172.86.88.* and 144.172.98.178
     
4. File & Script Artifacts
   Known malicious binaries (lumina.exe, seedcode.exe, visa_letter.exe) and scripts (script.ps1, screenshot.ps1) are matched via SHA256 hashes and filename patterns.
   
5. Command-Line IOC Matching
   A dense array of IOC strings is searched in full command-line logs, helping pinpoint behavioral overlap across infection stages (download → execution → persistence).
   

On the left, Uncoder AI maps these elements to their respective campaign timestamps and SHA256 values, correlating execution artifacts with attacker infrastructure and campaign timelines.

Why It’s Innovative

This level of rule complexity — multi-condition logic, IOC chaining, regex-based matching — is traditionally opaque without deep EQL expertise. Uncoder AI uses LLM-backed parsing to:

• Automatically extract logic branches
  
• Annotate each component with contextual meaning
  
• Visually group indicators by execution phase (initial access, payload, C2)
  

Instead of treating IOCs as flat lists, the AI links them to behavioral signatures inside CrowdStrike’s detection stack. The result: rules become readable and auditable, even under incident pressure.

Operational Value

For detection engineers and threat intel teams working within CrowdStrike:

• Accelerated Rule Auditing
  Cut down review time by 70–90% through structured AI summaries of event chains and logic conditions.
  
• IOC-to-Telemetry Precision
  Clearly understand how each URL, hash, or filename is operationalized in detection — no more guesswork.
  
• Optimized Rule Adaptation
  Extract logic templates from existing IOC reports and adapt them for emerging threats using a click-based interface.
  

By automating the technical breakdown of dense EQL logic, Uncoder AI turns post-compromise intel into proactive detection at scale.

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