Individual skill
performing-mobile-device-forensics-with-cellebrite
Acquire and analyze mobile device data using Cellebrite UFED and open-source tools to extract communications, location data, and application artifacts.
internal/tools/skills/data/digital-forensics/performing-mobile-device-forensics-with-cellebrite/SKILL.md
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- Last checked
- Nov 9, 2026
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- License
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- cybersecurity Repository topic
- security Repository topic
- bug-bounty Repository topic
- automation Repository topic
- autonomous-pentesting Repository topic
- ai-agent Repository topic
- ai-security Repository topic
- ethical-hacking Repository topic
- golang Repository topic
- penetration-testing Repository topic
- forensics Declared skill tag
- mobile-forensics Declared skill tag
- cellebrite Declared skill tag
- smartphone-analysis Declared skill tag
- ios-forensics Declared skill tag
- android-forensics Declared skill tag
- data Derived category
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Read excerpt
# Performing Mobile Device Forensics with Cellebrite
## When to Use
- When extracting evidence from smartphones or tablets during an investigation
- For recovering deleted messages, call logs, and location data from mobile devices
- During investigations involving communications via messaging apps
- When analyzing mobile application data for evidence of criminal activity
- For corporate investigations involving employee mobile device misuse
## Detection Gaps & Validation
The extraction type and device lock state silently bound what you can recover. Don't mistake a shallow acquisition for a complete one:
- **Logical extraction misses the deleted data.** UFED/iLEAPP logical pulls return only live app data; deleted messages, freelist/WAL rows, and unallocated content require File System or Physical (checkm8/GrayKey on supported iOS, rooted/EDL on Android). Report the extraction level explicitly — "no deleted messages found" on a logical pull is a coverage gap, not a finding.