
Analyzing MFT for Deleted File Recovery
FreeRecover deleted files by analyzing NTFS Master File Table.
Free · Opens the source repo
What Analyzing MFT for Deleted File Recovery does
The Analyzing MFT for Deleted File Recovery skill provides tools to analyze the NTFS Master File Table ($MFT) and recover metadata and content from deleted files. By leveraging utilities such as MFTECmd, analyzeMFT, and X-Ways Forensics, users can extract valuable information from MFT records, $LogFile, $UsnJrnl, and MFT slack space. This skill is particularly useful for digital forensics professionals who need to recover evidence of deleted files, reconstruct timelines of file system activity, or detect anti-forensic techniques like timestomping.
When a file is deleted on an NTFS volume, its MFT record is marked as inactive but remains intact until it is overwritten by new data. The skill allows users to analyze these records to find details such as timestamps, permissions, and even the original content of the file. This is crucial in forensic investigations where understanding the history of file changes is necessary. The skill also includes techniques for analyzing the USN Journal and $LogFile, which track changes to the file system, providing further context and evidence for investigations.
The prerequisites for using this skill include a forensic disk image and familiarity with NTFS file system internals. Users should have tools like MFTECmd or analyzeMFT installed, and Python 3.8 or higher for running custom scripts. This skill is designed for security analysts, incident responders, and anyone involved in digital forensics who requires structured methodologies for analyzing deleted files and validating security monitoring coverage.
In summary, this skill equips users with the necessary techniques and tools to effectively analyze the NTFS file system for deleted file recovery, making it an essential addition to any forensic toolkit.
When to use it
Use this skill when investigating security incidents that involve deleted files, or when building detection rules for threat hunting related to file system changes.
When not to use it
This skill may not be suitable for environments using non-NTFS file systems or for basic file recovery tasks that do not require forensic analysis.
What you can build with it
Investigating Security Incidents
Use this skill to recover deleted files during a security incident investigation, providing crucial evidence.
Building Detection Rules
Leverage the skill to create detection rules for identifying suspicious file deletions in a monitored environment.
Forensic Analysis Training
Incorporate this skill into training sessions for new forensic analysts to teach them about NTFS file recovery techniques.
How to install Analyzing MFT for Deleted File Recovery
View source1. Install with the skills CLI
npx skills add mukul975/anthropic-cybersecurity-skills/analyzing-mft-for-deleted-file-recovery --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by mukul975Analyzing MFT for Deleted File Recovery
Overview
The NTFS Master File Table ($MFT) is the central metadata repository for every file and directory on an NTFS volume. Each file is represented by at least one 1024-byte MFT record containing attributes such as $STANDARD_INFORMATION (timestamps, permissions), $FILE_NAME (name, parent directory, timestamps), and $DATA (file content or cluster run pointers). When a file is deleted, its MFT record is marked as inactive (InUse flag cleared) but the metadata remains until the entry is reallocated by a new file. This persistence makes MFT analysis a primary technique for recovering deleted file evidence, reconstructing file system timelines, and detecting anti-forensic activity such as timestomping.
When to Use
- When investigating security incidents that require analyzing mft for deleted file recovery
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Forensic disk image (E01, raw/dd, VMDK, or VHDX format)
- MFTECmd (Eric Zimmerman) or analyzeMFT (Python-based)
- FTK Imager, Arsenal Image Mounter, or similar for image mounting
- Timeline Explorer or Excel for CSV analysis
- Python 3.8+ for custom analysis scripts
- Understanding of NTFS file system internals
MFT Structure and Record Layout
MFT Record Header
Each MFT record begins with the signature "FILE" (0x46494C45) and contains:
| Offset | Size | Field |
|---|---|---|
| 0x00 | 4 bytes | Signature ("FILE") |
| 0x04 | 2 bytes | Offset to update sequence |
| 0x06 | 2 bytes | Size of update sequence |
| 0x08 | 8 bytes | $LogFile sequence number |
| 0x10 | 2 bytes | Sequence number |
| 0x12 | 2 bytes | Hard link count |
| 0x14 | 2 bytes | Offset to first attribute |
| 0x16 | 2 bytes | Flags (0x01 = InUse, 0x02 = Directory) |
| 0x18 | 4 bytes | Used size of MFT record |
| 0x1C | 4 bytes | Allocated size of MFT record |
| 0x20 | 8 bytes | Base file record reference |
| 0x28 | 2 bytes | Next attribute ID |
Key MFT Attributes
| Type ID | Name | Description |
|---|---|---|
| 0x10 | $STANDARD_INFORMATION | Timestamps, flags, owner ID, security ID |
| 0x30 | $FILE_NAME | Filename, parent MFT reference, timestamps |
| 0x40 | $OBJECT_ID | Unique GUID for the file |
| 0x50 | $SECURITY_DESCRIPTOR | ACL permissions |
| 0x60 | $VOLUME_NAME | Volume label (volume metadata files only) |
| 0x80 | $DATA | File content (resident if <700 bytes) or cluster run list |
| 0x90 | $INDEX_ROOT | B-tree index root for directories |
| 0xA0 | $INDEX_ALLOCATION | B-tree index entries for large directories |
| 0xB0 | $BITMAP | Allocation bitmap for index or MFT |
Deleted File Recovery Techniques
Technique 1: MFT Record Analysis with MFTECmd
# Extract $MFT from forensic image using KAPE or FTK Imager
# Parse the $MFT with MFTECmd
MFTECmd.exe -f "C:\Evidence\$MFT" --csv C:\Output --csvf mft_full.csv
# Filter for deleted files (InUse = FALSE) in Timeline Explorer
# Look for entries where InUse column is False
Identifying Deleted Files in CSV Output:
InUse= False indicates a deleted or reallocated recordParentPathshows original file location before deletionFileSizeshows the original size (may still be recoverable)- Timestamps in
$STANDARD_INFORMATIONand$FILE_NAMEattributes persist
Technique 2: USN Journal ($UsnJrnl:$J) Analysis
The USN Journal records all changes to files on an NTFS volume, including creation, deletion, rename, and data modification events.
# Parse USN Journal with MFTECmd
MFTECmd.exe -f "C:\Evidence\$J" --csv C:\Output --csvf usn_journal.csv
# Key USN reason codes for deletion evidence:
# USN_REASON_FILE_DELETE = 0x00000200
# USN_REASON_CLOSE = 0x80000000
# USN_REASON_RENAME_OLD_NAME = 0x00001000
# USN_REASON_RENAME_NEW_NAME = 0x00002000
Technique 3: $LogFile Transaction Analysis
The $LogFile stores NTFS transaction records that can reveal file operations even after the USN Journal has been cycled.
# Parse $LogFile with LogFileParser
LogFileParser.exe -l "C:\Evidence\$LogFile" -o C:\Output
# Look for REDO and UNDO operations indicating file deletion:
# - DeallocateFileRecordSegment
# - DeleteAttribute
# - UpdateResidentValue (clearing InUse flag)
Technique 4: MFT Slack Space Analysis
MFT slack space exists between the end of the used portion of an MFT record and the end of the allocated 1024 bytes. This area may contain remnants of previous file records.
import struct
def parse_mft_slack(mft_path: str, output_path: str):
"""Extract and analyze MFT slack space for deleted file remnants."""
with open(mft_path, "rb") as f:
record_size = 1024
record_num = 0
slack_findings = []
while True:
record = f.read(record_size)
if len(record) < record_size:
break
# Verify FILE signature
if record[:4] != b"FILE":
record_num += 1
continue
# Get used size from offset 0x18
used_size = struct.unpack("<I", record[0x18:0x1C])[0]
if used_size < record_size:
slack = record[used_size:]
# Check if slack contains readable strings or attribute headers
if any(c > 0x20 and c < 0x7F for c in slack[:50]):
slack_findings.append({
"record": record_num,
"used_size": used_size,
"slack_size": record_size - used_size,
"slack_preview": slack[:100].hex()
})
record_num += 1
return slack_findings
Correlation with Supporting Artifacts
Cross-Reference MFT with $Recycle.Bin
# Parse Recycle Bin with RBCmd
RBCmd.exe -d "C:\Evidence\$Recycle.Bin" --csv C:\Output --csvf recycle_bin.csv
# Correlate: $I files contain original path and deletion timestamp
# Match MFT entry numbers from $R files back to original MFT records
Cross-Reference MFT with Volume Shadow Copies
# List volume shadow copies
vssadmin list shadows
# Mount shadow copies and extract $MFT from each
# Compare MFT records across shadow copies to track file changes over time
Forensic Value
- Deleted file metadata recovery: Original filename, path, size, and timestamps
- Timeline reconstruction: File creation, modification, access, and deletion events
- Timestomping detection: Comparing $SI vs $FN timestamps
- Data carving guidance: MFT cluster runs point to file content on disk
- Anti-forensic detection: Identifying wiped or manipulated MFT records
References
- NTFS MFT Advanced Forensic Analysis: https://www.deaddisk.com/posts/ntfs-mft-advanced-forensic-analysis-guide/
- MFT Slack Space Forensic Value: https://www.sygnia.co/blog/the-forensic-value-of-mft-slack-space/
- MFTECmd Documentation: https://ericzimmerman.github.io/
- SANS FOR500: Windows Forensic Analysis
Example Output
$ MFTECmd.exe -f "C:\Evidence\$MFT" --csv /analysis/mft_output
MFTECmd v1.2.2 - MFT Parser
==============================
Input: C:\Evidence\$MFT (Size: 384 MB)
Total MFT Entries: 395,264
Parsing MFT entries... Done (12.4 seconds)
--- Deleted File Recovery Summary ---
Total Entries: 395,264
Active Files: 245,832
Deleted Files: 149,432
Recoverable: 87,234 (resident data or clusters not reallocated)
Partially Recoverable: 31,456 (some clusters overwritten)
Unrecoverable: 30,742 (all clusters reallocated)
--- Recently Deleted Files (Incident Window: 2024-01-15 to 2024-01-18) ---
MFT Entry | Filename | Path | Size | Deleted (UTC) | Recoverable
----------|-----------------------------------|------------------------------------|-----------|-----------------------|------------
148923 | exfil_tool.exe | C:\ProgramData\Updates\ | 1,258,496 | 2024-01-17 02:45:12 | YES
148924 | exfil_tool.log | C:\ProgramData\Updates\ | 45,312 | 2024-01-17 02:45:14 | YES
149001 | passwords.txt | C:\Users\jsmith\Desktop\ | 2,048 | 2024-01-17 02:50:33 | YES
149150 | scan_results.csv | C:\Users\jsmith\AppData\Local\Temp | 892,416 | 2024-01-17 03:00:01 | PARTIAL
149200 | mimikatz.exe | C:\Windows\Temp\ | 1,250,816 | 2024-01-18 01:15:22 | YES
149201 | sekurlsa.log | C:\Windows\Temp\ | 32,768 | 2024-01-18 01:15:25 | YES
149302 | .bash_history | C:\Users\jsmith\ | 4,096 | 2024-01-18 03:00:00 | NO
149400 | ClearEventLogs.ps1 | C:\Windows\Temp\ | 1,536 | 2024-01-18 03:01:12 | YES
--- $STANDARD_INFORMATION vs $FILE_NAME Timestamp Analysis (Timestomping Detection) ---
MFT Entry | Filename | $SI Created | $FN Created | Delta | Verdict
----------|---------------------|----------------------|----------------------|-----------|----------
148923 | exfil_tool.exe | 2023-06-15 10:00:00 | 2024-01-15 14:34:02 | -214 days | TIMESTOMPED
149200 | mimikatz.exe | 2022-01-01 00:00:00 | 2024-01-16 02:30:15 | -745 days | TIMESTOMPED
Recovered files exported to: /analysis/mft_output/recovered/
Full CSV report: /analysis/mft_output/mft_analysis.csv (395,264 rows)
Timeline CSV: /analysis/mft_output/mft_timeline.csv
Frequently asked questions about Analyzing MFT for Deleted File Recovery
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