analyzing-mft-for-deleted-file-recovery
This Claude Code skill provides structured procedures for analyzing NTFS Master File Table records to recover metadata from deleted files and reconstruct file system timelines. Use it when investigating security incidents requiring deleted file evidence recovery, building forensic detection rules, or conducting incident response analysis on NTFS volumes where the MFT persistence property enables recovery of file attributes, timestamps, and content references even after deletion.
git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills /tmp/analyzing-mft-for-deleted-file-recovery && cp -r /tmp/analyzing-mft-for-deleted-file-recovery/skills/analyzing-mft-for-deleted-file-recovery ~/.claude/skills/analyzing-mft-for-deleted-file-recoverySKILL.md
# Analyzing 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
```powershell
# Extract $MFT from forensic image using KAPE or FTK Imager
# Parse the $MFT with MFTECmd
MFTECmd.exe -f "C:\Evidence\$MFT" --csv C:\Output --csvf mft_full.csv
# Filter for deleted files (InUse = FALSE) in Timeline Explorer
# Look for entries where InUse column is False
```
**Identifying Deleted Files in CSV Output:**
- `InUse` = False indicates a deleted or reallocated record
- `ParentPath` shows original file location before deletion
- `FileSize` shows the original size (may still be recoverable)
- Timestamps in `$STANDARD_INFORMATION` and `$FILE_NAME` attributes persist
### Technique 2: USN Journal ($UsnJrnl:$J) Analysis
The USN Journal records all changes to files on an NTFS volume, including creation, deletion, rename, and data modification events.
```powershell
# 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.
```powershell
# 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.
```python
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
```powershelCreate forensically sound bit-for-bit disk images using dd and dcfldd
Detect dangerous ACL misconfigurations in Active Directory using ldap3
Perform static analysis of Android APK malware samples using apktool
Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect
Analyze advanced persistent threat (APT) group techniques using MITRE
Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query