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分析 Docker 容器鑑識

調查受入侵的 Docker 容器，透過分析映像檔、層與主機端產物來找出安全問題。

分析 Docker 容器鑑識 是什麼？

這個技能專為鑑識工程師與資安應變者設計，提供一套標準化流程來取證、分析與回報受入侵的 Docker 容器。技能涵蓋從容器狀態保全、映像檔層級分析、主機端（overlay2、daemon.json）取證、檔案系統變更比對，到弱點掃描與惡意檔案偵測。支援 CLI 工具如 docker inspect、dive、container-diff 與 Trivy，並內建 Python 腳本自動標記可疑掛載、敏感能力與權限。也涵蓋常見情境如網頁應用程式入侵、供應鏈攻擊、容器逃逸與挖礦劫持。輸出格式為結構化摘要，適合嵌入資安報告。平台：Docker CLI、Linux 主機。

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說明文件

分析 Docker 容器鑑識 是做什麼的？

使用時機

  • 調查受入侵的 Docker 容器或容器主機時
  • 分析從 registry 拉取的惡意 Docker 映像檔時
  • 應對容器化應用程式入侵事件時
  • 檢查容器逃脫嘗試或權限提升時
  • 稽核容器設定並找出錯誤配置時

先決條件

  • 在鑑識工作站上具備 Docker CLI 存取權限
  • 可存取 Docker 主機檔案系統（鑑識映像檔或即時系統）
  • 了解 Docker 分層檔案系統（overlay2、aufs）
  • 安裝 dive、docker-explorer 或 container-diff 進行映像檔分析
  • 具備 Docker daemon 設定與 socket 安全知識
  • 安裝 Trivy 或 Grype 進行容器映像檔弱點掃描

工作流程

步驟 1：保全容器狀態與證據

# 列出所有容器（包括已停止的）
docker ps -a --no-trunc > /cases/case-2024-001/docker/container_list.txt

# 檢查受入侵容器
CONTAINER_ID="abc123def456"
docker inspect $CONTAINER_ID > /cases/case-2024-001/docker/container_inspect.json

# 匯出容器檔案系統為 tar 檔（保全當前狀態）
docker export $CONTAINER_ID > /cases/case-2024-001/docker/container_export.tar

# 從容器當前狀態建立映像檔
docker commit $CONTAINER_ID forensic-evidence:case-2024-001
docker save forensic-evidence:case-2024-001 > /cases/case-2024-001/docker/container_image.tar

# 擷取容器日誌
docker logs $CONTAINER_ID --timestamps > /cases/case-2024-001/docker/container_logs.txt 2>&1

# 擷取執行中的程序（如果容器仍在執行）
docker top $CONTAINER_ID > /cases/case-2024-001/docker/container_processes.txt

# 擷取網路連線
docker exec $CONTAINER_ID netstat -tlnp 2>/dev/null > /cases/case-2024-001/docker/container_network.txt

# 從容器複製特定檔案
docker cp $CONTAINER_ID:/var/log/ /cases/case-2024-001/docker/container_var_log/
docker cp $CONTAINER_ID:/tmp/ /cases/case-2024-001/docker/container_tmp/
docker cp $CONTAINER_ID:/etc/passwd /cases/case-2024-001/docker/container_passwd

# 雜湊所有匯出的證據
sha256sum /cases/case-2024-001/docker/*.tar > /cases/case-2024-001/docker/evidence_hashes.txt

步驟 2：分析容器映像檔層

# 安裝 dive 進行映像檔層分析
wget https://github.com/wagoodman/dive/releases/latest/download/dive_linux_amd64.deb
sudo dpkg -i dive_linux_amd64.deb

# 互動式分析映像檔層
dive forensic-evidence:case-2024-001

# 非互動式層分析
dive forensic-evidence:case-2024-001 --ci --json /cases/case-2024-001/docker/dive_analysis.json

# 解壓縮並檢查個別層
mkdir -p /cases/case-2024-001/docker/layers/
tar -xf /cases/case-2024-001/docker/container_image.tar -C /cases/case-2024-001/docker/layers/

# 列出映像檔 manifest 與層順序
cat /cases/case-2024-001/docker/layers/manifest.json | python3 -m json.tool

# 檢查每一層的變更
for layer in /cases/case-2024-001/docker/layers/*/layer.tar; do
    echo "=== 層：$(dirname $layer | xargs basename) ==="
    tar -tf "$layer" | head -20
    echo "..."
done

# 使用 container-diff 與原始基礎映像檔比較
# 安裝 container-diff
curl -LO https://storage.googleapis.com/container-diff/latest/container-diff-linux-amd64
chmod +x container-diff-linux-amd64

# 比較提交的映像檔與原始版本
./container-diff-linux-amd64 diff daemon://nginx:latest daemon://forensic-evidence:case-2024-001 \
   --type=file --type=apt --type=history --json \
   > /cases/case-2024-001/docker/container_diff.json

步驟 3：檢查 Docker 主機端產物

# Docker 資料目錄（預設：/var/lib/docker/）
DOCKER_ROOT="/mnt/evidence/var/lib/docker"

# 檢查 overlay2 檔案系統層
ls -la $DOCKER_ROOT/overlay2/

# 尋找容器的合併檔案系統
CONTAINER_HASH=$(docker inspect $CONTAINER_ID --format '{{.GraphDriver.Data.MergedDir}}' 2>/dev/null)
# 或從鑑識映像檔手動尋找：
# 查看 /var/lib/docker/containers/<container_id>/config.v2.json

# 分析容器設定檔
cat $DOCKER_ROOT/containers/$CONTAINER_ID/config.v2.json | python3 -m json.tool \
   > /cases/case-2024-001/docker/container_config.json

# 檢查 Docker daemon 設定
cat /mnt/evidence/etc/docker/daemon.json 2>/dev/null > /cases/case-2024-001/docker/daemon_config.json

# 檢查 Docker 事件日誌
cat $DOCKER_ROOT/containers/$CONTAINER_ID/*.log > /cases/case-2024-001/docker/container_json_logs.txt

# 檢查磁碟區掛載（可能的主機檔案系統存取）
python3 << 'PYEOF'
import json

with open('/cases/case-2024-001/docker/container_inspect.json') as f:
    data = json.load(f)

inspect = data[0] if isinstance(data, list) else data

print("=== 容器安全性分析 ===\n")

# 檢查掛載
print("磁碟區掛載：")
for mount in inspect.get('Mounts', []):
    rw = "讀寫" if mount.get('RW') else "唯讀"
    print(f"  {mount.get('Source', 'N/A')} -> {mount.get('Destination', 'N/A')} ({rw})")
    if mount.get('Source') in ('/', '/etc', '/var', '/root') and mount.get('RW'):
        print(f"    警告：敏感的主機路徑以讀寫方式掛載！")

# 檢查特權模式
host_config = inspect.get('HostConfig', {})
if host_config.get('Privileged'):
    print("\n警告：容器以「特權」模式執行！")

# 檢查能力
cap_add = host_config.get('CapAdd', [])
if cap_add:
    print(f"\n新增的能力：{cap_add}")
    dangerous_caps = ['SYS_ADMIN', 'SYS_PTRACE', 'NET_ADMIN', 'SYS_MODULE']
    for cap in cap_add:
        if cap in dangerous_caps:
            print(f"  警告：危險能力：{cap}")

# 檢查 PID 命名空間
if host_config.get('PidMode') == 'host':
    print("\n警告：容器共用主機 PID 命名空間！")

# 檢查網路模式
if host_config.get('NetworkMode') == 'host':
    print("\n警告：容器共用主機網路命名空間！")

# 檢查使用者
user = inspect.get('Config', {}).get('User', 'root (預設)')
print(f"\n執行使用者：{user}")

# 檢查環境變數中的秘密
env_vars = inspect.get('Config', {}).get('Env', [])
print(f"\n環境變數：{len(env_vars)} 個")
for env in env_vars:
    key = env.split('=')[0]
    if any(s in key.upper() for s in ['PASSWORD', 'SECRET', 'KEY', 'TOKEN', 'CREDENTIAL']):
        print(f"  敏感性資訊：{key}=***已遮罩***")
PYEOF

步驟 4：分析容器檔案系統變更

# 比較容器檔案系統與原始映像檔
docker diff $CONTAINER_ID > /cases/case-2024-001/docker/filesystem_changes.txt

# A = 新增，C = 變更，D = 刪除
# 分析變更
python3 << 'PYEOF'
added = []
changed = []
deleted = []

with open('/cases/case-2024-001/docker/filesystem_changes.txt') as f:
    for line in f:
        line = line.strip()
        if line.startswith('A '):
            added.append(line[2:])
        elif line.startswith('C '):
            changed.append(line[2:])
        elif line.startswith('D '):
            deleted.append(line[2:])

print(f"新增檔案：{len(added)} 個")
print(f"變更檔案：{len(changed)} 個")
print(f"刪除檔案：{len(deleted)} 個")

# 標記可疑新增
suspicious = [f for f in added if any(s in f for s in
    ['/tmp/', '/dev/shm/', '/root/', '.sh', '.py', '.elf', 'reverse', 'shell', 'backdoor'])]
if suspicious:
    print(f"\n可疑新增檔案：")
    for f in suspicious:
        print(f"  {f}")

# 標記可疑變更
sus_changed = [f for f in changed if any(s in f for s in
    ['/etc/passwd', '/etc/shadow', '/etc/crontab', '/etc/ssh', '.bashrc'])]
if sus_changed:
    print(f"\n可疑變更檔案：")
    for f in sus_changed:
        print(f"  {f}")
PYEOF

# 解壓縮並檢查容器匯出內容
mkdir -p /cases/case-2024-001/docker/container_fs/
tar -xf /cases/case-2024-001/docker/container_export.tar -C /cases/case-2024-001/docker/container_fs/

# 掃描網頁後門與惡意檔案
find /cases/case-2024-001/docker/container_fs/tmp/ -type f -exec file {} \;
find /cases/case-2024-001/docker/container_fs/ -name "*.php" -newer /cases/case-2024-001/docker/container_fs/etc/hostname

步驟 5：掃描弱點並產生報告

# 掃描映像檔的已知弱點
trivy image forensic-evidence:case-2024-001 \
   --format json \
   --output /cases/case-2024-001/docker/vulnerability_scan.json

# 掃描匯出的檔案系統
trivy fs /cases/case-2024-001/docker/container_fs/ \
   --format table \
   --output /cases/case-2024-001/docker/fs_vulnerabilities.txt

# 檢查映像檔中的秘密
trivy image forensic-evidence:case-2024-001 \
   --scanners secret \
   --format json \
   --output /cases/case-2024-001/docker/secrets_scan.json

關鍵概念

概念說明
映像檔層唯讀檔案系統層堆疊形成容器映像檔
overlay2預設 Docker 儲存驅動程式，使用聯合檔案系統處理層
容器差異比較執行時期檔案系統變更與原始映像檔
特權模式容器具備完整主機能力（繞過大部分隔離）
Docker socket控制 Docker daemon 的 Unix socket（/var/run/docker.sock）
容器逃脫突破容器隔離到主機的技術
磁碟區掛載可在容器內存取的主機檔案系統路徑
映像檔歷史用於建立每一層的 Dockerfile 指令記錄

工具與系統

工具目的
docker inspect詳細容器設定與狀態資訊
docker diff顯示執行中/已停止容器所做的檔案系統變更
dive互動式 Docker 映像檔層分析工具
container-diffGoogle 工具，用於比較容器映像檔內容
Trivy容器映像檔與檔案系統的弱點掃描器
docker-explorer離線 Docker 產物分析的鑑識工具
Sysdig容器執行時期安全性監控與鑑識
Falco容器與 Kubernetes 的執行時期威脅偵測

常見情境

情境 1：網頁應用程式容器入侵 匯出容器檔案系統，找出網頁根目錄中的網頁後門，分析存取日誌以探查利用嘗試，檢查新增的檔案與修改的設定，檢查網路連線以找出 C2 通訊，檢視容器能力以尋找權限提升路徑。

情境 2：透過惡意映像檔的供應鏈攻擊 使用 dive 分析映像檔層以找出哪一層新增惡意內容，使用 container-diff 與官方基礎映像檔比較，檢查映像檔歷史尋找可疑的 RUN 指令，掃描內建的後門與加密貨幣挖礦程式，從 registry 日誌追蹤映像檔拉取記錄。

情境 3：容器逃脫調查 檢查容器是否以特權或危險能力執行，檢查主機檔案系統掛載點以探查未授權存取，檢視 Docker socket 掛載（可能啟用 Docker-in-Docker 濫用），分析主機系統日誌以找出容器逃脫指標，檢查核心漏洞利用產物。

情境 4：容器環境中的挖礦劫持 找出高 CPU 使用率的容器，匯出並分析容器映像檔中的挖礦二進位檔，檢查 registry 中是否有未授權映像檔，檢視容器建立事件以找出惡意部署，檢查網路連線以找出礦池通訊。

輸出格式

Docker 容器鑑識摘要：
  Container：abc123def456 (nginx-app)
  Image：company/web-app:v2.1
  Status：執行中 (啟動於 2024-01-10 09:00 UTC)
  Host：docker-host-01.corp.local

  安全性設定：
    特權模式：否
    新增的能力：NET_ADMIN (警告)
    磁碟區掛載：/var/log -> /host-logs (讀寫)
    網路模式：bridge
    使用者：root (警告)

  檔案系統變更：
    新增：23 個檔案 (5 個可疑)
    變更：12 個檔案 (2 個可疑)
    刪除：0 個檔案

  可疑發現：
    /tmp/reverse.sh - 反向 shell 腳本 (新增)
    /var/www/html/.hidden/shell.php - PHP 網頁後門 (新增)
    /etc/crontab - 已修改 (已新增持續性 cron 項目)
    /root/.ssh/authorized_keys - 已修改 (已新增未授權金鑰)

  弱點掃描：
    重大：3 (CVE-2024-xxxx 於基礎映像檔中)
    高：12
    中：34

  證據位置：/cases/case-2024-001/docker/

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

abusing-dpapi-for-credential-access

Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using SharpDPAPI, SharpChrome, Mimikatz, or Impacket

abusing-shadow-credentials-for-privesc

Take over Active Directory accounts by writing attacker-controlled public keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, or Certipy, then authenticate via PKINIT to recover the target

achieving-cmmc-level-2-compliance

>-

acquiring-disk-image-with-dd-and-dcfldd

Create forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.

analyzing-active-directory-acl-abuse

Detect dangerous ACL misconfigurations in Active Directory using ldap3

analyzing-android-malware-with-apktool

Perform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and reflection-based API calls. Use to statically triage a suspicious APK without executing it or to build mobile malware detection rules.

analyzing-api-gateway-access-logs

Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect

analyzing-apt-group-with-mitre-navigator

Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups

analyzing-azure-activity-logs-for-threats

Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query

analyzing-bootkit-and-rootkit-samples

Analyzes bootkit and advanced rootkit malware infecting the Master

analyzing-browser-forensics-with-hindsight

Parse Chromium-based browser databases with Hindsight to extract and correlate browsing history, downloads, cookies, cached content, autofill data, saved passwords, and extensions from Chrome, Edge, Brave, Opera, and Vivaldi into a unified timeline (XLSX, JSON, or SQLite output). Use during incident response, insider-threat investigations, or criminal cases when you need to reconstruct a user

analyzing-campaign-attribution-evidence

Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.

analyzing-certificate-transparency-for-phishing

Monitor Certificate Transparency logs using crt.sh and Certstream to

analyzing-cloud-storage-access-patterns

Detect abnormal access in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics for after-hours bulk downloads, new-IP access, and API-call spikes (e.g. GetObject) via statistical baselines and time-series anomaly detection. Use when investigating suspected cloud data exfiltration or building related detection rules.

analyzing-cobalt-strike-beacon-configuration

Extract and analyze Cobalt Strike beacon configuration from PE files

analyzing-cobaltstrike-malleable-c2-profiles

Parse and analyze Cobalt Strike Malleable C2 profiles with dissect.cobaltstrike (profiles and beacon-payload configs) and pyMalleableC2 (AST parsing) to extract HTTP/DNS transforms, URIs, headers, sleep/jitter, and injection behavior, then generate network detection signatures. Use when reverse-engineering a captured malleable profile or building detections against Cobalt Strike Beacon traffic.

analyzing-command-and-control-communication

Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom

analyzing-cyber-kill-chain

Analyzes intrusion activity against the Lockheed Martin Cyber Kill Chain

analyzing-disk-image-with-autopsy

Perform comprehensive forensic analysis of raw (dd), E01, or AFF disk images with Autopsy and The Sleuth Kit, recovering deleted files, examining metadata and embedded artifacts, keyword searching, and building investigation timelines with visual reports. Use for structured analysis of a forensic disk image or when stakeholders need visual reports from evidence.

analyzing-dns-logs-for-exfiltration

Analyzes DNS query logs to detect data exfiltration via DNS tunneling,

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