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Anthropic-Cybersecurity-Skills/skills/hunting-for-data-exfiltration-indicators/SKILL.md
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mukul975 cb8d79e068 Map all 754 skills to MITRE ATT&CK v19.1
- Add validated mitre_attack frontmatter to all 754 skills (286 distinct
  techniques), verified against MITRE ATT&CK v19.1 via the official
  mitreattack-python library: 0 revoked, deprecated, or invalid IDs
- Curate precise per-skill technique IDs for forensics, malware-analysis,
  threat-intel, and red-team skills (e.g. DCSync -> T1003.006,
  Kerberoasting -> T1558.003, Pass-the-Ticket -> T1550.003)
- Reconcile v19.1 tactic restructuring: Defense Evasion split into
  Stealth (TA0005) and Defense Impairment (TA0112); revoked T1562.*
  family and T1070.001/.002 remapped to active equivalents (T1685.*)
- Normalize word-split tags across 35 skills (remove filename-derived
  stopword tags, add semantic cybersecurity tags)
- Add api-reference.md for 3 skills that were missing it
- Update README ATT&CK section with accurate v19.1 tactic distribution
2026-06-01 12:13:29 +02:00

4.4 KiB

name, description, domain, subdomain, tags, version, author, license, atlas_techniques, nist_ai_rmf, d3fend_techniques, nist_csf, mitre_attack
name description domain subdomain tags version author license atlas_techniques nist_ai_rmf d3fend_techniques nist_csf mitre_attack
hunting-for-data-exfiltration-indicators Hunt for data exfiltration through network traffic analysis, detecting unusual data flows, DNS tunneling, cloud storage uploads, and encrypted channel abuse. cybersecurity threat-hunting
threat-hunting
mitre-attack
data-exfiltration
dlp
network-analysis
proactive-detection
1.0 mahipal Apache-2.0
AML.T0024
AML.T0056
MEASURE-2.7
MAP-5.1
MANAGE-2.4
File Metadata Consistency Validation
Certificate Analysis
Application Protocol Command Analysis
Content Format Conversion
File Content Analysis
DE.CM-01
DE.AE-02
DE.AE-07
ID.RA-05
T1046
T1057
T1082
T1083
T1048

Hunting for Data Exfiltration Indicators

When to Use

  • When hunting for data theft in compromised environments
  • After detecting unusual outbound data volumes or patterns
  • When investigating potential insider threat data theft
  • During incident response to determine what data was stolen
  • When threat intel indicates data exfiltration campaigns targeting your sector

Prerequisites

  • Network proxy/firewall logs with byte-level data transfer metrics
  • DLP solution or CASB with cloud upload visibility
  • DNS query logs for DNS exfiltration detection
  • Email gateway logs for attachment monitoring
  • SIEM with data volume anomaly detection capabilities

Workflow

  1. Define Exfiltration Channels: Identify potential channels (HTTP/S uploads, DNS tunneling, email attachments, cloud storage, removable media, encrypted protocols).
  2. Baseline Normal Data Flows: Establish baseline outbound data transfer volumes per user, host, and destination over a 30-day window.
  3. Detect Volume Anomalies: Identify hosts or users transferring significantly more data than baseline to external destinations.
  4. Analyze Transfer Destinations: Check destination domains/IPs against threat intel, identify newly registered domains, personal cloud storage, and foreign infrastructure.
  5. Inspect Protocol Abuse: Look for DNS tunneling (large/frequent TXT queries), ICMP tunneling, or data hidden in allowed protocols.
  6. Correlate with File Access: Link exfiltration indicators to file access events on sensitive file shares, databases, or repositories.
  7. Report and Contain: Document findings with evidence, estimate data exposure, and recommend containment actions.

Key Concepts

Concept Description
T1041 Exfiltration Over C2 Channel
T1048 Exfiltration Over Alternative Protocol
T1048.001 Exfiltration Over Symmetric Encrypted Non-C2
T1048.002 Exfiltration Over Asymmetric Encrypted Non-C2
T1048.003 Exfiltration Over Unencrypted/Obfuscated Non-C2
T1567 Exfiltration Over Web Service
T1567.002 Exfiltration to Cloud Storage
T1052 Exfiltration Over Physical Medium
T1029 Scheduled Transfer
T1030 Data Transfer Size Limits (staging)
T1537 Transfer Data to Cloud Account
T1020 Automated Exfiltration

Tools & Systems

Tool Purpose
Splunk SIEM for data volume analysis and SPL queries
Zeek Network metadata for data flow analysis
Microsoft Defender for Cloud Apps CASB for cloud exfiltration
Netskope Cloud DLP and exfiltration detection
Suricata Network IDS for protocol anomaly detection
RITA DNS exfiltration and beacon detection
ExtraHop Network traffic analysis for data flow

Common Scenarios

  1. Cloud Storage Exfiltration: User uploads sensitive documents to personal Google Drive or Dropbox via browser.
  2. DNS Tunneling: Malware exfiltrates data encoded in DNS subdomain queries to attacker-controlled nameserver.
  3. HTTPS Upload: Compromised system POSTs large data blobs to C2 server over encrypted HTTPS.
  4. Email Attachment Exfiltration: Insider forwards sensitive documents to personal email accounts.
  5. Staging and Compression: Adversary stages data in compressed archives before slow exfiltration to avoid detection.

Output Format

Hunt ID: TH-EXFIL-[DATE]-[SEQ]
Exfiltration Channel: [HTTP/DNS/Email/Cloud/USB]
Source: [Host/User]
Destination: [Domain/IP/Service]
Data Volume: [Bytes/MB/GB]
Time Period: [Start - End]
Protocol: [HTTPS/DNS/SMTP/SMB]
Files Involved: [Count/Types]
Risk Level: [Critical/High/Medium/Low]
Confidence: [High/Medium/Low]