[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADQF1YDZYWPBPRF2QF3VZW":3,"skill-same:01M6ADQF1YDZYWPBPRF2QF3VZW":122},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":62,"repositoryHtmlUrl":63,"repositoryHomepageUrl":64,"ownerHtmlUrl":65,"ownerBlogUrl":66,"ownerDescription":67,"updatedAt":68,"install":69,"resources":82},"01M6ADQF1YDZYWPBPRF2QF3VZW","analyzing-mft-for-deleted-file-recovery",null,"Analyze the NTFS Master File Table ($MFT) with MFTECmd, analyzeMFT, and X-Ways Forensics to recover metadata and content of deleted files by examining MFT record entries, $LogFile, $UsnJrnl, and MFT slack space. Use when recovering evidence of deleted files, reconstructing NTFS file-system timelines, or detecting anti-forensic timestomping during a Windows forensic examination.","mukul975\u002FAnthropic-Cybersecurity-Skills","mukul975","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42860185?v=4","01M6ADEQR8T5M4EEG6C67BQCWF","Mahipal","01M6ADJPMYWG1E5GM1FWBE3128",36078,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-mft-for-deleted-file-recovery\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000005","邮件与沟通","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"analyzing mft for deleted file recovery","file","mft","file-system-forensics","mft-slack-space","logfile","usn-journal","mftecmd","deleted-files","ntfs","slack","dfir","file-recovery","forensics","metadata","content","windows","cybersecurity","2026-11-10 10:12:30",false,"---\nname: analyzing-mft-for-deleted-file-recovery\ndescription: Analyze the NTFS Master File Table ($MFT) with MFTECmd, analyzeMFT,\n  and X-Ways Forensics to recover metadata and content of deleted files by examining\n  MFT record entries, $LogFile, $UsnJrnl, and MFT slack space. Use when recovering\n  evidence of deleted files, reconstructing NTFS file-system timelines, or detecting\n  anti-forensic timestomping during a Windows forensic examination.\ndomain: cybersecurity\nsubdomain: digital-forensics\ntags:\n- mft\n- ntfs\n- deleted-files\n- file-recovery\n- mftecmd\n- usn-journal\n- logfile\n- mft-slack-space\n- file-system-forensics\n- dfir\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nnist_csf:\n- RS.AN-03\n- DE.AE-02\n- RS.MA-01\nmitre_attack:\n- T1070.004\n- T1070.006\n- T1005\n---\n\n# Analyzing MFT for Deleted File Recovery\n\n## Overview\n\nThe NTFS Master File Table ($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.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing mft for deleted file recovery\n- When building detection rules or threat hunting queries for this domain\n- When SOC analysts need structured procedures for this analysis type\n- When validating security monitoring coverage for related attack techniques\n\n## Prerequisites\n\n- Forensic disk image (E01, raw\u002Fdd, VMDK, or VHDX format)\n- MFTECmd (Eric Zimmerman) or analyzeMFT (Python-based)\n- FTK Imager, Arsenal Image Mounter, or similar for image mounting\n- Timeline Explorer or Excel for CSV analysis\n- Python 3.8+ for custom analysis scripts\n- Understanding of NTFS file system internals\n\n## MFT Structure and Record Layout\n\n### MFT Record Header\n\nEach MFT record begins with the signature \"FILE\" (0x46494C45) and contains:\n\n| Offset | Size | Field |\n|--------|------|-------|\n| 0x00 | 4 bytes | Signature (\"FILE\") |\n| 0x04 | 2 bytes | Offset to update sequence |\n| 0x06 | 2 bytes | Size of update sequence |\n| 0x08 | 8 bytes | $LogFile sequence number |\n| 0x10 | 2 bytes | Sequence number |\n| 0x12 | 2 bytes | Hard link count |\n| 0x14 | 2 bytes | Offset to first attribute |\n| 0x16 | 2 bytes | Flags (0x01 = InUse, 0x02 = Directory) |\n| 0x18 | 4 bytes | Used size of MFT record |\n| 0x1C | 4 bytes | Allocated size of MFT record |\n| 0x20 | 8 bytes | Base file record reference |\n| 0x28 | 2 bytes | Next attribute ID |\n\n### Key MFT Attributes\n\n| Type ID | Name | Description |\n|---------|------|-------------|\n| 0x10 | $STANDARD_INFORMATION | Timestamps, flags, owner ID, security ID |\n| 0x30 | $FILE_NAME | Filename, parent MFT reference, timestamps |\n| 0x40 | $OBJECT_ID | Unique GUID for the file |\n| 0x50 | $SECURITY_DESCRIPTOR | ACL permissions |\n| 0x60 | $VOLUME_NAME | Volume label (volume metadata files only) |\n| 0x80 | $DATA | File content (resident if \u003C700 bytes) or cluster run list |\n| 0x90 | $INDEX_ROOT | B-tree index root for directories |\n| 0xA0 | $INDEX_ALLOCATION | B-tree index entries for large directories |\n| 0xB0 | $BITMAP | Allocation bitmap for index or MFT |\n\n## Deleted File Recovery Techniques\n\n### Technique 1: MFT Record Analysis with MFTECmd\n\n```powershell\n# Extract $MFT from forensic image using KAPE or FTK Imager\n# Parse the $MFT with MFTECmd\nMFTECmd.exe -f \"C:\\Evidence\\$MFT\" --csv C:\\Output --csvf mft_full.csv\n\n# Filter for deleted files (InUse = FALSE) in Timeline Explorer\n# Look for entries where InUse column is False\n```\n\n**Identifying Deleted Files in CSV Output:**\n- `InUse` = False indicates a deleted or reallocated record\n- `ParentPath` shows original file location before deletion\n- `FileSize` shows the original size (may still be recoverable)\n- Timestamps in `$STANDARD_INFORMATION` and `$FILE_NAME` attributes persist\n\n### Technique 2: USN Journal ($UsnJrnl:$J) Analysis\n\nThe USN Journal records all changes to files on an NTFS volume, including creation, deletion, rename, and data modification events.\n\n```powershell\n# Parse USN Journal with MFTECmd\nMFTECmd.exe -f \"C:\\Evidence\\$J\" --csv C:\\Output --csvf usn_journal.csv\n\n# Key USN reason codes for deletion evidence:\n# USN_REASON_FILE_DELETE     = 0x00000200\n# USN_REASON_CLOSE           = 0x80000000\n# USN_REASON_RENAME_OLD_NAME = 0x00001000\n# USN_REASON_RENAME_NEW_NAME = 0x00002000\n```\n\n### Technique 3: $LogFile Transaction Analysis\n\nThe $LogFile stores NTFS transaction records that can reveal file operations even after the USN Journal has been cycled.\n\n```powershell\n# Parse $LogFile with LogFileParser\nLogFileParser.exe -l \"C:\\Evidence\\$LogFile\" -o C:\\Output\n\n# Look for REDO and UNDO operations indicating file deletion:\n# - DeallocateFileRecordSegment\n# - DeleteAttribute\n# - UpdateResidentValue (clearing InUse flag)\n```\n\n### Technique 4: MFT Slack Space Analysis\n\nMFT 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.\n\n```python\nimport struct\n\ndef parse_mft_slack(mft_path: str, output_path: str):\n    \"\"\"Extract and analyze MFT slack space for deleted file remnants.\"\"\"\n    with open(mft_path, \"rb\") as f:\n        record_size = 1024\n        record_num = 0\n        slack_findings = []\n\n        while True:\n            record = f.read(record_size)\n            if len(record) \u003C record_size:\n                break\n\n            # Verify FILE signature\n            if record[:4] != b\"FILE\":\n                record_num += 1\n                continue\n\n            # Get used size from offset 0x18\n            used_size = struct.unpack(\"\u003CI\", record[0x18:0x1C])[0]\n\n            if used_size \u003C record_size:\n                slack = record[used_size:]\n                # Check if slack contains readable strings or attribute headers\n                if any(c > 0x20 and c \u003C 0x7F for c in slack[:50]):\n                    slack_findings.append({\n                        \"record\": record_num,\n                        \"used_size\": used_size,\n                        \"slack_size\": record_size - used_size,\n                        \"slack_preview\": slack[:100].hex()\n                    })\n\n            record_num += 1\n\n    return slack_findings\n```\n\n## Correlation with Supporting Artifacts\n\n### Cross-Reference MFT with $Recycle.Bin\n\n```powershell\n# Parse Recycle Bin with RBCmd\nRBCmd.exe -d \"C:\\Evidence\\$Recycle.Bin\" --csv C:\\Output --csvf recycle_bin.csv\n\n# Correlate: $I files contain original path and deletion timestamp\n# Match MFT entry numbers from $R files back to original MFT records\n```\n\n### Cross-Reference MFT with Volume Shadow Copies\n\n```powershell\n# List volume shadow copies\nvssadmin list shadows\n\n# Mount shadow copies and extract $MFT from each\n# Compare MFT records across shadow copies to track file changes over time\n```\n\n## Forensic Value\n\n- **Deleted file metadata recovery**: Original filename, path, size, and timestamps\n- **Timeline reconstruction**: File creation, modification, access, and deletion events\n- **Timestomping detection**: Comparing $SI vs $FN timestamps\n- **Data carving guidance**: MFT cluster runs point to file content on disk\n- **Anti-forensic detection**: Identifying wiped or manipulated MFT records\n\n## References\n\n- NTFS MFT Advanced Forensic Analysis: https:\u002F\u002Fwww.deaddisk.com\u002Fposts\u002Fntfs-mft-advanced-forensic-analysis-guide\u002F\n- MFT Slack Space Forensic Value: https:\u002F\u002Fwww.sygnia.co\u002Fblog\u002Fthe-forensic-value-of-mft-slack-space\u002F\n- MFTECmd Documentation: https:\u002F\u002Fericzimmerman.github.io\u002F\n- SANS FOR500: Windows Forensic Analysis\n\n## Example Output\n\n```text\n$ MFTECmd.exe -f \"C:\\Evidence\\$MFT\" --csv \u002Fanalysis\u002Fmft_output\n\nMFTECmd v1.2.2 - MFT Parser\n==============================\nInput: C:\\Evidence\\$MFT (Size: 384 MB)\nTotal MFT Entries: 395,264\n\nParsing MFT entries... Done (12.4 seconds)\n\n--- Deleted File Recovery Summary ---\nTotal Entries:          395,264\nActive Files:           245,832\nDeleted Files:          149,432\n  Recoverable:          87,234 (resident data or clusters not reallocated)\n  Partially Recoverable: 31,456 (some clusters overwritten)\n  Unrecoverable:        30,742 (all clusters reallocated)\n\n--- Recently Deleted Files (Incident Window: 2024-01-15 to 2024-01-18) ---\nMFT Entry | Filename                          | Path                               | Size      | Deleted (UTC)         | Recoverable\n----------|-----------------------------------|------------------------------------|-----------|-----------------------|------------\n148923    | exfil_tool.exe                    | C:\\ProgramData\\Updates\\            | 1,258,496 | 2024-01-17 02:45:12   | YES\n148924    | exfil_tool.log                    | C:\\ProgramData\\Updates\\            | 45,312    | 2024-01-17 02:45:14   | YES\n149001    | passwords.txt                     | C:\\Users\\jsmith\\Desktop\\           | 2,048     | 2024-01-17 02:50:33   | YES\n149150    | scan_results.csv                  | C:\\Users\\jsmith\\AppData\\Local\\Temp | 892,416   | 2024-01-17 03:00:01   | PARTIAL\n149200    | mimikatz.exe                      | C:\\Windows\\Temp\\                   | 1,250,816 | 2024-01-18 01:15:22   | YES\n149201    | sekurlsa.log                      | C:\\Windows\\Temp\\                   | 32,768    | 2024-01-18 01:15:25   | YES\n149302    | .bash_history                     | C:\\Users\\jsmith\\                   | 4,096     | 2024-01-18 03:00:00   | NO\n149400    | ClearEventLogs.ps1                | C:\\Windows\\Temp\\                   | 1,536     | 2024-01-18 03:01:12   | YES\n\n--- $STANDARD_INFORMATION vs $FILE_NAME Timestamp Analysis (Timestomping Detection) ---\nMFT Entry | Filename            | $SI Created          | $FN Created          | Delta     | Verdict\n----------|---------------------|----------------------|----------------------|-----------|----------\n148923    | exfil_tool.exe      | 2023-06-15 10:00:00  | 2024-01-15 14:34:02  | -214 days | TIMESTOMPED\n149200    | mimikatz.exe        | 2022-01-01 00:00:00  | 2024-01-16 02:30:15  | -745 days | TIMESTOMPED\n\nRecovered files exported to: \u002Fanalysis\u002Fmft_output\u002Frecovered\u002F\nFull CSV report: \u002Fanalysis\u002Fmft_output\u002Fmft_analysis.csv (395,264 rows)\nTimeline CSV: \u002Fanalysis\u002Fmft_output\u002Fmft_timeline.csv\n```\n","# Analyzing MFT for Deleted File Recovery\n\n## Overview\n\nThe NTFS Master File Table ($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.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing mft for deleted file recovery\n- When building detection rules or threat hunting queries for this domain\n- When SOC analysts need structured procedures for this analysis type\n- When validating security monitoring coverage for related attack techniques\n\n## Prerequisites\n\n- Forensic disk image (E01, raw\u002Fdd, VMDK, or VHDX format)\n- MFTECmd (Eric Zimmerman) or analyzeMFT (Python-based)\n- FTK Imager, Arsenal Image Mounter, or similar for image mounting\n- Timeline Explorer or Excel for CSV analysis\n- Python 3.8+ for custom analysis scripts\n- Understanding of NTFS file system internals\n\n## MFT Structure and Record Layout\n\n### MFT Record Header\n\nEach MFT record begins with the signature \"FILE\" (0x46494C45) and contains:\n\n| Offset | Size | Field |\n|--------|------|-------|\n| 0x00 | 4 bytes | Signature (\"FILE\") |\n| 0x04 | 2 bytes | Offset to update sequence |\n| 0x06 | 2 bytes | Size of update sequence |\n| 0x08 | 8 bytes | $LogFile sequence number |\n| 0x10 | 2 bytes | Sequence number |\n| 0x12 | 2 bytes | Hard link count |\n| 0x14 | 2 bytes | Offset to first attribute |\n| 0x16 | 2 bytes | Flags (0x01 = InUse, 0x02 = Directory) |\n| 0x18 | 4 bytes | Used size of MFT record |\n| 0x1C | 4 bytes | Allocated size of MFT record |\n| 0x20 | 8 bytes | Base file record reference |\n| 0x28 | 2 bytes | Next attribute ID |\n\n### Key MFT Attributes\n\n| Type ID | Name | Description |\n|---------|------|-------------|\n| 0x10 | $STANDARD_INFORMATION | Timestamps, flags, owner ID, security ID |\n| 0x30 | $FILE_NAME | Filename, parent MFT reference, timestamps |\n| 0x40 | $OBJECT_ID | Unique GUID for the file |\n| 0x50 | $SECURITY_DESCRIPTOR | ACL permissions |\n| 0x60 | $VOLUME_NAME | Volume label (volume metadata files only) |\n| 0x80 | $DATA | File content (resident if \u003C700 bytes) or cluster run list |\n| 0x90 | $INDEX_ROOT | B-tree index root for directories |\n| 0xA0 | $INDEX_ALLOCATION | B-tree index entries for large directories |\n| 0xB0 | $BITMAP | Allocation bitmap for index or MFT |\n\n## Deleted File Recovery Techniques\n\n### Technique 1: MFT Record Analysis with MFTECmd\n\n```powershell\n# Extract $MFT from forensic image using KAPE or FTK Imager\n# Parse the $MFT with MFTECmd\nMFTECmd.exe -f \"C:\\Evidence\\$MFT\" --csv C:\\Output --csvf mft_full.csv\n\n# Filter for deleted files (InUse = FALSE) in Timeline Explorer\n# Look for entries where InUse column is False\n```\n\n**Identifying Deleted Files in CSV Output:**\n- `InUse` = False indicates a deleted or reallocated record\n- `ParentPath` shows original file location before deletion\n- `FileSize` shows the original size (may still be recoverable)\n- Timestamps in `$STANDARD_INFORMATION` and `$FILE_NAME` attributes persist\n\n### Technique 2: USN Journal ($UsnJrnl:$J) Analysis\n\nThe USN Journal records all changes to files on an NTFS volume, including creation, deletion, rename, and data modification events.\n\n```powershell\n# Parse USN Journal with MFTECmd\nMFTECmd.exe -f \"C:\\Evidence\\$J\" --csv C:\\Output --csvf usn_journal.csv\n\n# Key USN reason codes for deletion evidence:\n# USN_REASON_FILE_DELETE     = 0x00000200\n# USN_REASON_CLOSE           = 0x80000000\n# USN_REASON_RENAME_OLD_NAME = 0x00001000\n# USN_REASON_RENAME_NEW_NAME = 0x00002000\n```\n\n### Technique 3: $LogFile Transaction Analysis\n\nThe $LogFile stores NTFS transaction records that can reveal file operations even after the USN Journal has been cycled.\n\n```powershell\n# Parse $LogFile with LogFileParser\nLogFileParser.exe -l \"C:\\Evidence\\$LogFile\" -o C:\\Output\n\n# Look for REDO and UNDO operations indicating file deletion:\n# - DeallocateFileRecordSegment\n# - DeleteAttribute\n# - UpdateResidentValue (clearing InUse flag)\n```\n\n### Technique 4: MFT Slack Space Analysis\n\nMFT 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.\n\n```python\nimport struct\n\ndef parse_mft_slack(mft_path: str, output_path: str):\n    \"\"\"Extract and analyze MFT slack space for deleted file remnants.\"\"\"\n    with open(mft_path, \"rb\") as f:\n        record_size = 1024\n        record_num = 0\n        slack_findings = []\n\n        while True:\n            record = f.read(record_size)\n            if len(record) \u003C record_size:\n                break\n\n            # Verify FILE signature\n            if record[:4] != b\"FILE\":\n                record_num += 1\n                continue\n\n            # Get used size from offset 0x18\n            used_size = struct.unpack(\"\u003CI\", record[0x18:0x1C])[0]\n\n            if used_size \u003C record_size:\n                slack = record[used_size:]\n                # Check if slack contains readable strings or attribute headers\n                if any(c > 0x20 and c \u003C 0x7F for c in slack[:50]):\n                    slack_findings.append({\n                        \"record\": record_num,\n                        \"used_size\": used_size,\n                        \"slack_size\": record_size - used_size,\n                        \"slack_preview\": slack[:100].hex()\n                    })\n\n            record_num += 1\n\n    return slack_findings\n```\n\n## Correlation with Supporting Artifacts\n\n### Cross-Reference MFT with $Recycle.Bin\n\n```powershell\n# Parse Recycle Bin with RBCmd\nRBCmd.exe -d \"C:\\Evidence\\$Recycle.Bin\" --csv C:\\Output --csvf recycle_bin.csv\n\n# Correlate: $I files contain original path and deletion timestamp\n# Match MFT entry numbers from $R files back to original MFT records\n```\n\n### Cross-Reference MFT with Volume Shadow Copies\n\n```powershell\n# List volume shadow copies\nvssadmin list shadows\n\n# Mount shadow copies and extract $MFT from each\n# Compare MFT records across shadow copies to track file changes over time\n```\n\n## Forensic Value\n\n- **Deleted file metadata recovery**: Original filename, path, size, and timestamps\n- **Timeline reconstruction**: File creation, modification, access, and deletion events\n- **Timestomping detection**: Comparing $SI vs $FN timestamps\n- **Data carving guidance**: MFT cluster runs point to file content on disk\n- **Anti-forensic detection**: Identifying wiped or manipulated MFT records\n\n## References\n\n- NTFS MFT Advanced Forensic Analysis: https:\u002F\u002Fwww.deaddisk.com\u002Fposts\u002Fntfs-mft-advanced-forensic-analysis-guide\u002F\n- MFT Slack Space Forensic Value: https:\u002F\u002Fwww.sygnia.co\u002Fblog\u002Fthe-forensic-value-of-mft-slack-space\u002F\n- MFTECmd Documentation: https:\u002F\u002Fericzimmerman.github.io\u002F\n- SANS FOR500: Windows Forensic Analysis\n\n## Example Output\n\n```text\n$ MFTECmd.exe -f \"C:\\Evidence\\$MFT\" --csv \u002Fanalysis\u002Fmft_output\n\nMFTECmd v1.2.2 - MFT Parser\n==============================\nInput: C:\\Evidence\\$MFT (Size: 384 MB)\nTotal MFT Entries: 395,264\n\nParsing MFT entries... Done (12.4 seconds)\n\n--- Deleted File Recovery Summary ---\nTotal Entries:          395,264\nActive Files:           245,832\nDeleted Files:          149,432\n  Recoverable:          87,234 (resident data or clusters not reallocated)\n  Partially Recoverable: 31,456 (some clusters overwritten)\n  Unrecoverable:        30,742 (all clusters reallocated)\n\n--- Recently Deleted Files (Incident Window: 2024-01-15 to 2024-01-18) ---\nMFT Entry | Filename                          | Path                               | Size      | Deleted (UTC)         | Recoverable\n----------|-----------------------------------|------------------------------------|-----------|-----------------------|------------\n148923    | exfil_tool.exe                    | C:\\ProgramData\\Updates\\            | 1,258,496 | 2024-01-17 02:45:12   | YES\n148924    | exfil_tool.log                    | C:\\ProgramData\\Updates\\            | 45,312    | 2024-01-17 02:45:14   | YES\n149001    | passwords.txt                     | C:\\Users\\jsmith\\Desktop\\           | 2,048     | 2024-01-17 02:50:33   | YES\n149150    | scan_results.csv                  | C:\\Users\\jsmith\\AppData\\Local\\Temp | 892,416   | 2024-01-17 03:00:01   | PARTIAL\n149200    | mimikatz.exe                      | C:\\Windows\\Temp\\                   | 1,250,816 | 2024-01-18 01:15:22   | YES\n149201    | sekurlsa.log                      | C:\\Windows\\Temp\\                   | 32,768    | 2024-01-18 01:15:25   | YES\n149302    | .bash_history                     | C:\\Users\\jsmith\\                   | 4,096     | 2024-01-18 03:00:00   | NO\n149400    | ClearEventLogs.ps1                | C:\\Windows\\Temp\\                   | 1,536     | 2024-01-18 03:01:12   | YES\n\n--- $STANDARD_INFORMATION vs $FILE_NAME Timestamp Analysis (Timestomping Detection) ---\nMFT Entry | Filename            | $SI Created          | $FN Created          | Delta     | Verdict\n----------|---------------------|----------------------|----------------------|-----------|----------\n148923    | exfil_tool.exe      | 2023-06-15 10:00:00  | 2024-01-15 14:34:02  | -214 days | TIMESTOMPED\n149200    | mimikatz.exe        | 2022-01-01 00:00:00  | 2024-01-16 02:30:15  | -745 days | TIMESTOMPED\n\nRecovered files exported to: \u002Fanalysis\u002Fmft_output\u002Frecovered\u002F\nFull CSV report: \u002Fanalysis\u002Fmft_output\u002Fmft_analysis.csv (395,264 rows)\nTimeline CSV: \u002Fanalysis\u002Fmft_output\u002Fmft_timeline.csv\n```\n",{"name":5,"tags":47,"author":48,"domain":41,"license":15,"version":49,"nist_csf":50,"subdomain":54,"description":7,"mitre_attack":55},[26,33,32,36,31,30,29,28,27,35],"mahipal","1.0",[51,52,53],"RS.AN-03","DE.AE-02","RS.MA-01","digital-forensics",[56,57,58],"T1070.004","T1070.006","T1005","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fanalyzing-mft-for-deleted-file-recovery",4414,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills","https:\u002F\u002Fmahipal.engineer\u002FAnthropic-Cybersecurity-Skills\u002F","https:\u002F\u002Fgithub.com\u002Fmukul975","Mahipal.engineer","🔐 Cybersecurity | 💻 Dev | 📷 Street Photographer\r\n🎓 MSc | 📚 Research & AI Security","2026-08-31 12:32:44",{"source":8,"skillName":5,"commands":70,"prompt":80,"zipAvailable":81},[71,74,77],{"tool":72,"command":73},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-mft-for-deleted-file-recovery",{"tool":75,"command":76},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-mft-for-deleted-file-recovery",{"tool":78,"command":79},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill analyzing-mft-for-deleted-file-recovery","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-mft-for-deleted-file-recovery\u002FSKILL.md",true,[83,89,94,98,104,108,112,118],{"path":84,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"assets\u002Ftemplate.md","ASSET","资产文件",741,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-mft-for-deleted-file-recovery\u002Fassets\u002Ftemplate.md",{"path":90,"resourceType":90,"resourceTypeDesc":91,"sizeBytes":92,"sourceHtmlUrl":93},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-mft-for-deleted-file-recovery\u002FLICENSE",{"path":95,"resourceType":90,"resourceTypeDesc":91,"sizeBytes":96,"sourceHtmlUrl":97},"LICENSE.txt",1467,"https:\u002F\u002Fgithub.com\u002Fderseitenschneider\u002Fdotfiles\u002Fblob\u002Fmain\u002Fclaude-config\u002F.claude\u002Fskills\u002Fsynced\u002Fc18e4183-2bcf-4f84-9592-16baf9918832_492d02ae-7d32-4227-a9d4-d9f601100de4\u002Fbuilt-in-browser\u002FLICENSE.txt",{"path":99,"resourceType":100,"resourceTypeDesc":101,"sizeBytes":102,"sourceHtmlUrl":103},"references\u002Fapi-reference.md","REFERENCE","参考文档",1672,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-mft-for-deleted-file-recovery\u002Freferences\u002Fapi-reference.md",{"path":105,"resourceType":100,"resourceTypeDesc":101,"sizeBytes":106,"sourceHtmlUrl":107},"references\u002Fstandards.md",1169,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-mft-for-deleted-file-recovery\u002Freferences\u002Fstandards.md",{"path":109,"resourceType":100,"resourceTypeDesc":101,"sizeBytes":110,"sourceHtmlUrl":111},"references\u002Fworkflows.md",1038,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-mft-for-deleted-file-recovery\u002Freferences\u002Fworkflows.md",{"path":113,"resourceType":114,"resourceTypeDesc":115,"sizeBytes":116,"sourceHtmlUrl":117},"scripts\u002Fagent.py","SCRIPT","脚本",6088,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-mft-for-deleted-file-recovery\u002Fscripts\u002Fagent.py",{"path":119,"resourceType":114,"resourceTypeDesc":115,"sizeBytes":120,"sourceHtmlUrl":121},"scripts\u002Fprocess.py",4501,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fanalyzing-mft-for-deleted-file-recovery\u002Fscripts\u002Fprocess.py",[123,152,176,204,225,252],{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":132,"publishedAt":42},"01M6AGDM3HJXTKXX15ZZ0ZVCJ3","verifying-build-provenance-with-slsa-sigstore","Verifies artifact signatures and SLSA provenance using Sigstore's cosign (verify, verify-attestation, verify-blob-attestation) and slsa-verifier (verify-artifact), enforcing keyless OIDC builder identity and source repo against SLSA Build levels. Use in CI\u002FCD before deploying artifacts, when consuming third-party attestations, establishing a SLSA Build L3 pipeline, or confirming provenance during incident response or admission control.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fverifying-build-provenance-with-slsa-sigstore\u002FSKILL.md",[129],{"categoryNo":130,"categoryName":131,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[133,134,135,136,137,41,138,139,140,141,142,143,144,145,146,147,148,149,150,151],"sigstore","provenance","slsa","verifying build provenance with slsa sigstore","security","incident response","identity","pr","code","artifacts","ci","supply-chain","pipeline","supply-chain-security","t1195","attestation","code-signing","cosign","keyless",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":159,"publishedAt":42},"01M6AGDGBPWYVZ2F11GFKJ4HZ9","validating-tpm-measured-boot-attestation","Verifies TPM 2.0 measured-boot integrity and remote attestation with tpm2-tools -- reading PCRs (tpm2_pcrread), replaying the boot event log, generating and checking signed quotes (tpm2_quote\u002Ftpm2_checkquote), and sealing secrets to a PCR policy. Use to confirm a system booted trusted firmware\u002Fkernel for Zero Trust device posture, detect boot-chain tampering via PCR drift, or build a golden-value baseline for fleet attestation.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fvalidating-tpm-measured-boot-attestation\u002FSKILL.md",[158],{"categoryNo":130,"categoryName":131,"taxonomyType":21,"taxonomyTypeDesc":22},[160,148,161,162,137,163,41,140,164,165,166,167,168,169,170,171,172,173,174,175],"boot","TPM","validating tpm measured boot attestation","policy","verification","ZeRO","firmware","Tools","measured-boot","PCR","integrity","secrets","remote-attestation","tpm2-tools","integrity-verification","trusted-computing",{"skillNo":177,"name":178,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":180,"categories":181,"keywords":186,"publishedAt":42},"01M6AGDCW0Y4FXZ3362GT1941M","validating-backup-integrity-for-recovery","Validates backup integrity through cryptographic hash verification, automated restore testing, corruption detection, and recoverability checks to confirm backups are reliable for disaster recovery and ransomware response. Use before relying on backups for recovery, when building post-backup validation pipelines, auditing backup infrastructure for compliance, or checking immutable\u002Fair-gapped backups for silent corruption or tampering.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fvalidating-backup-integrity-for-recovery\u002FSKILL.md",[182,185],{"categoryNo":183,"categoryName":184,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":130,"categoryName":131,"taxonomyType":21,"taxonomyTypeDesc":22},[170,187,188,189,190,41,191,192,164,193,194,195,196,197,198,199,200,201,202,203],"backup","recovery","validating backup integrity for recovery","testing","infrastructure","validation","compliance","hash-verification","incident-response","ransomware","cryptocurrency","detection","t1078","t1490","immutable","disaster-recovery","restore-testing",{"skillNo":205,"name":206,"nameZh":6,"description":207,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":208,"categories":209,"keywords":211,"publishedAt":42},"01M6AGD9GBG2KZ336AE763J3WH","triaging-windows-with-kape","Runs KAPE (Kroll Artifact Parser and Extractor) to collect targeted forensic artifacts (registry hives, $MFT, event logs, prefetch, browser data) via Targets and parse them with Modules wrapping Eric Zimmerman's EZ Tools (PECmd, MFTECmd, RECmd). Use during early incident containment\u002Ftriage when full disk imaging is impractical but a defensible, parseable Windows artifact set is needed quickly, including at-scale remote collection.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-windows-with-kape\u002FSKILL.md",[210],{"categoryNo":130,"categoryName":131,"taxonomyType":21,"taxonomyTypeDesc":22},[40,212,213,41,214,215,216,217,35,26,31,218,219,220,54,221,167,222,223,224],"kape","triaging windows with kape","registry","browser","logs","windows-forensics","data","prefetch","eric-zimmerman","SET","collection","pecmd","artifact-collection",{"skillNo":226,"name":227,"nameZh":6,"description":228,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":229,"categories":230,"keywords":232,"publishedAt":42},"01M6AGD5PW5J4EVZM958WD74ZQ","triaging-vulnerabilities-with-ssvc-framework","Triages and prioritizes vulnerabilities with CISA's Stakeholder-Specific Vulnerability Categorization (SSVC) decision tree, weighing exploitation status (via the CISA KEV catalog and FIRST EPSS API), technical impact, automatability, and mission prevalence to output Track\u002FTrack*\u002FAttend\u002FAct decisions. Use when prioritizing vulnerability scan results (OpenVAS, Nessus, Qualys) for remediation planning beyond raw CVSS scores.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-vulnerabilities-with-ssvc-framework\u002FSKILL.md",[231],{"categoryNo":130,"categoryName":131,"taxonomyType":21,"taxonomyTypeDesc":22},[233,234,235,41,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251],"vulnerabilities","framework","triaging vulnerabilities with ssvc framework","status","planning","CISA","vulnerability-management","risk-management","nessus","qualys","openvas","cvss","remediation","risk","vulnerability","api","prioritization","ssvc","decision-tree",{"skillNo":253,"name":254,"nameZh":6,"description":255,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":256,"categories":257,"keywords":259,"publishedAt":42},"01M6AGD24TTQQM4CXG69E3B9TJ","triaging-security-incident","Performs initial triage of security incidents using the NIST SP 800-61r3 and SANS PICERL frameworks, classifying incident type, assigning priority by business impact, and routing to the appropriate response team. Use when a SIEM\u002FEDR alert needs human classification, concurrent alerts must be prioritized, or a user report or threat-intel IOC match requires initial incident categorization.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-security-incident\u002FSKILL.md",[258],{"categoryNo":130,"categoryName":131,"taxonomyType":21,"taxonomyTypeDesc":22},[137,260,41,261,25,262,263,264,265,266,267,199,268,200,269,270,271,272,273,274],"triaging security incident","siem","ioc","soc","NIST-800-61","NIST","severity","edr","execution","analysis","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]