Outlook PST电子邮件取证分析

分析Outlook PST进行电子邮件取证

概述

Microsoft Outlook PST（个人存储表）和OST（脱机存储表）文件是数字取证调查中的关键证据来源。PST文件以基于MAPI（消息处理API）属性系统的专有二进制格式存储电子邮件、日历事件、联系人、任务和便笺。对这些文件进行取证分析可以恢复已删除的电子邮件（来自“可恢复项”文件夹）、提取邮件标头以追踪消息路由、分析附件中的恶意软件或外泄数据，并重建通信模式。现代PST文件采用Unicode格式，页面大小为4KB，最大可达50GB，而传统的ANSI格式限制为2GB。

何时使用

• 当调查需要分析Outlook PST进行电子邮件取证的安全事件时
• 当为此领域构建检测规则或威胁狩猎查询时
• 当SOC分析师需要此类分析的结构化流程时
• 当验证相关攻击技术的安全监控覆盖范围时

前提条件

• libpff/pffexport（开源PST解析器）
• Python 3.8+，带有pypff或libratom库
• MailXaminer、Forensic Email Collector或SysTools PST Forensics（商业工具）
• Microsoft Outlook（可选，用于原生PST访问）
• 足够的磁盘空间用于提取内容

PST文件位置

来源	路径
Outlook 2016+ 默认	%USERPROFILE%\Documents\Outlook Files*.pst
Outlook 旧版	%LOCALAPPDATA%\Microsoft\Outlook*.pst
OST缓存	%LOCALAPPDATA%\Microsoft\Outlook*.ost
存档	%USERPROFILE%\Documents\Outlook Files\archive.pst

使用开源工具分析

libpff / pffexport

# 从PST文件导出所有项目
pffexport -m all evidence.pst -t exported_pst

# 仅导出电子邮件
pffexport -m items evidence.pst -t exported_emails

# 导出恢复/已删除的项目
pffexport -m recovered evidence.pst -t recovered_items

# 获取PST文件信息
pffinfo evidence.pst

Python PST分析

import pypff
import os
import json
import hashlib
import email
import sys
from datetime import datetime
from collections import defaultdict


class PSTForensicAnalyzer:
    """Outlook PST/OST文件的取证分析。"""

    def __init__(self, pst_path: str, output_dir: str):
        self.pst_path = pst_path
        self.output_dir = output_dir
        os.makedirs(output_dir, exist_ok=True)
        self.pst = pypff.file()
        self.pst.open(pst_path)
        self.messages = []
        self.attachments = []
        self.stats = defaultdict(int)

    def process_folder(self, folder, folder_path: str = ""):
        """递归处理PST文件夹并提取消息。"""
        folder_name = folder.name or "Root"
        current_path = f"{folder_path}/{folder_name}" if folder_path else folder_name

        for i in range(folder.number_of_sub_messages):
            try:
                message = folder.get_sub_message(i)
                msg_data = self.extract_message(message, current_path)
                if msg_data:
                    self.messages.append(msg_data)
                    self.stats["total_messages"] += 1
            except Exception as e:
                self.stats["parse_errors"] += 1

        for i in range(folder.number_of_sub_folders):
            try:
                subfolder = folder.get_sub_folder(i)
                self.process_folder(subfolder, current_path)
            except Exception:
                continue

    def extract_message(self, message, folder_path: str) -> dict:
        """从单个电子邮件中提取取证元数据。"""
        msg_data = {
            "folder": folder_path,
            "subject": message.subject or "",
            "sender": message.sender_name or "",
            "sender_email": "",
            "creation_time": str(message.creation_time) if message.creation_time else None,
            "delivery_time": str(message.delivery_time) if message.delivery_time else None,
            "modification_time": str(message.modification_time) if message.modification_time else None,
            "has_attachments": message.number_of_attachments > 0,
            "attachment_count": message.number_of_attachments,
            "body_size": len(message.plain_text_body or b""),
            "html_size": len(message.html_body or b""),
        }

        # 提取传输标头用于路由分析
        headers = message.transport_headers
        if headers:
            msg_data["headers_present"] = True
            msg_data["headers_size"] = len(headers)
            # 解析关键标头
            parsed = email.message_from_string(headers)
            msg_data["from_header"] = parsed.get("From", "")
            msg_data["to_header"] = parsed.get("To", "")
            msg_data["date_header"] = parsed.get("Date", "")
            msg_data["message_id"] = parsed.get("Message-ID", "")
            msg_data["x_originating_ip"] = parsed.get("X-Originating-IP", "")
            msg_data["received_headers"] = parsed.get_all("Received", [])

        # 处理附件
        for j in range(message.number_of_attachments):
            try:
                attachment = message.get_attachment(j)
                att_data = {
                    "message_subject": msg_data["subject"],
                    "name": attachment.name or f"attachment_{j}",
                    "size": attachment.size,
                    "content_type": "",
                }
                self.attachments.append(att_data)
                self.stats["total_attachments"] += 1
            except Exception:
                continue

        return msg_data

    def save_attachments(self, max_size_mb: int = 100):
        """将附件导出到磁盘进行分析。"""
        att_dir = os.path.join(self.output_dir, "attachments")
        os.makedirs(att_dir, exist_ok=True)

        root = self.pst.get_root_folder()
        self._save_attachments_recursive(root, att_dir, max_size_mb)

    def _save_attachments_recursive(self, folder, att_dir, max_size_mb):
        for i in range(folder.number_of_sub_messages):
            try:
                message = folder.get_sub_message(i)
                for j in range(message.number_of_attachments):
                    att = message.get_attachment(j)
                    if att.size and att.size < max_size_mb * 1024 * 1024:
                        name = att.name or f"unknown_{i}_{j}"
                        safe_name = "".join(c if c.isalnum() or c in ".-_" else "_" for c in name)
                        path = os.path.join(att_dir, safe_name)
                        try:
                            data = att.read_buffer(att.size)
                            with open(path, "wb") as f:
                                f.write(data)
                        except Exception:
                            continue
            except Exception:
                continue

        for i in range(folder.number_of_sub_folders):
            try:
                self._save_attachments_recursive(folder.get_sub_folder(i), att_dir, max_size_mb)
            except Exception:
                continue

    def generate_report(self) -> str:
        """生成全面的PST取证分析报告。"""
        root = self.pst.get_root_folder()
        self.process_folder(root)

        report = {
            "analysis_timestamp": datetime.now().isoformat(),
            "pst_file": self.pst_path,
            "pst_size_bytes": os.path.getsize(self.pst_path),
            "statistics": dict(self.stats),
            "messages": self.messages[:500],
            "attachments": self.attachments[:200],
        }

        report_path = os.path.join(self.output_dir, "pst_forensic_report.json")
        with open(report_path, "w") as f:
            json.dump(report, f, indent=2, default=str)

        print(f"[*] 总消息数: {self.stats['total_messages']}")
        print(f"[*] 总附件数: {self.stats['total_attachments']}")
        print(f"[*] 解析错误数: {self.stats['parse_errors']}")
        return report_path

    def close(self):
        self.pst.close()


def main():
    if len(sys.argv) < 3:
        print("用法: python process.py <pst_file> <output_dir>")
        sys.exit(1)
    analyzer = PSTForensicAnalyzer(sys.argv[1], sys.argv[2])
    analyzer.generate_report()
    analyzer.close()


if __name__ == "__main__":
    main()

电子邮件标头分析

用于取证调查的关键标头：

标头	取证价值
Received	消息路由链（从下往上阅读）
X-Originating-IP	发件人的实际IP地址
Message-ID	用于关联的唯一标识符
Date	发送时间戳
Return-Path	退信地址（可能不同于From）
DKIM-Signature	域名身份验证签名
Authentication-Results	SPF、DKIM、DMARC验证结果
X-Mailer	使用的电子邮件客户端

参考资料

• MailXaminer PST Forensics: https://www.mailxaminer.com/blog/outlook-pst-file-forensics/
• libpff Documentation: https://github.com/libyal/libpff
• PST File Format Specification: https://docs.microsoft.com/en-us/openspecs/office_file_formats/ms-pst/
• SANS Email Forensics: https://www.sans.org/blog/email-forensics/

示例输出

$ pffexport /evidence/jsmith_archive.pst -t /analysis/pst_output

pffexport 20231205 - libpff PST/OST Export Tool
=================================================
输入: /evidence/jsmith_archive.pst (2.3 GB)

导出PST内容...
  文件夹:       45
  消息:         12,456
  附件:         3,234
  联系人:       567
  日历:         234
  任务:         89

导出完成，耗时3分42秒。

$ python3 pst_analyzer.py /analysis/pst_output /analysis/email_report

PST取证分析报告
==============================
来源: jsmith_archive.pst ([email protected])
日期范围: 2023-06-01 至 2024-01-18

--- 邮箱统计 ---
  总邮件数:       12,456
  已发送:         4,567
  已接收:         7,889
  带附件:         3,234
  已删除（恢复）: 234

--- 钓鱼/可疑邮件 ---
邮件 #8923
  日期:        2024-01-15 14:30:22 UTC
  发件人:      "IT Support" <[email protected]>
  收件人:      [email protected]
  主题:        紧急：需要重置密码
  标头:
    Return-Path:    [email protected]
    X-Originating-IP: 203.0.113.55
    Received:       from mail-relay.c0rporate-help.com (203.0.113.55)
    SPF:            FAIL (domain c0rporate-help.com)
    DKIM:           NONE
    DMARC:          FAIL
  附件:
    - Password_Reset_Form.xlsm (245 KB) SHA-256: 7a3b8c9d...e1f2a3b4
  正文预览:  "尊敬的员工，您的密码将在24小时后过期。
              请打开附件表格以重置您的凭证..."

--- 数据外泄指标 ---
邮件 #9102
  日期:        2024-01-16 03:15:45 UTC
  发件人:      [email protected]
  收件人:      [email protected]
  主题:        (无主题)
  附件:
    - archive_part1.7z (24.5 MB) - 加密
    - archive_part2.7z (24.5 MB) - 加密

邮件 #9103
  日期:        2024-01-16 03:18:22 UTC
  发件人:      [email protected]
  收件人:      [email protected]
  主题:        Re:
  附件:
    - archive_part3.7z (18.2 MB) - 加密

--- 关键字命中 ---
  "confidential":     45 封邮件
  "password":         23 封邮件
  "transfer":         12 封邮件
  "resign":           3 封邮件
  "delete evidence":  1 封邮件 (邮件 #9200, 2024-01-17 22:30:00 UTC)

摘要:
  检测到钓鱼邮件:    1 (初始入侵向量)
  可疑已发送邮件:    5 (发送至个人账户并带附件)
  加密附件:          3 (总计67.2 MB - 可能的数据外泄)
  报告: /analysis/email_report/pst_forensic_report.json