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analyzing-mft-for-deleted-file-recovery分析 mft 以进行删除文件恢复

Agent Skill

analyzing-mft-for-deleted-file-recovery 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:analyzing-mft-for-deleted-file-recovery(分析 mft 以进行删除文件恢复)
来源仓库:https://github.com/mukul975/anthropic-cybersecurity-skills
仓库路径:skills/analyzing-mft-for-deleted-file-recovery
安装命令:
npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill analyzing-mft-for-deleted-file-recovery
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill analyzing-mft-for-deleted-file-recovery

简介

解析 NTFS MFT 表恢复已删除文件,利用元数据残留重建路径。

  • 适用于数字取证,识别未释放的 $DATA 和 $FILE_NAME 属性。
  • 需只读访问磁盘镜像,避免写入导致数据覆盖。
  • 结合工具如 The Sleuth Kit 使用效果更佳。
  • analyzing-mft-for-deleted-file-recovery 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.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:

OffsetSizeField
0x004 bytesSignature ("FILE")
0x042 bytesOffset to update sequence
0x062 bytesSize of update sequence
0x088 bytes$LogFile sequence number
0x102 bytesSequence number
0x122 bytesHard link count
0x142 bytesOffset to first attribute
0x162 bytesFlags (0x01 = InUse, 0x02 = Directory)
0x184 bytesUsed size of MFT record
0x1C4 bytesAllocated size of MFT record
0x208 bytesBase file record reference
0x282 bytesNext attribute ID

Key MFT Attributes

Type IDNameDescription
0x10$STANDARD_INFORMATIONTimestamps, flags, owner ID, security ID
0x30$FILE_NAMEFilename, parent MFT reference, timestamps
0x40$OBJECT_IDUnique GUID for the file
0x50$SECURITY_DESCRIPTORACL permissions
0x60$VOLUME_NAMEVolume label (volume metadata files only)
0x80$DATAFile content (resident if <700 bytes) or cluster run list
0x90$INDEX_ROOTB-tree index root for directories
0xA0$INDEX_ALLOCATIONB-tree index entries for large directories
0xB0$BITMAPAllocation 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 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.

# 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

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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

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能力 4

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.3%
按下载量换算117

Claude

31.12%
按下载量换算100

Cursor

18.82%
按下载量换算60

Gemini CLI

11.24%
按下载量换算36

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Snyk

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