hunting-for-data-exfiltration-indicators
Helps your agent find signs of data theft by analyzing network traffic. Useful for detecting unusual data flows and suspicious cloud uploads.
What it does
This tool looks for signs of data theft on your network. It analyzes internet traffic to find suspicious activity, like large data uploads to unusual places or strange DNS usage.
When to use it
- ✓When you suspect data has been stolen.
- ✓If there's an unusual increase in data being sent out of the network.
- ✓To investigate if someone inside the company is stealing information.
Example of what to ask your AI
“Hunt for data theft on our network.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
It compares what's normal on your network with what's happening now. If something is very different, like a huge amount of data being sent to an unknown website, it raises an alert.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill hunting-for-data-exfiltration-indicatorsOr download just the file:
curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/hunting-for-data-exfiltration-indicators/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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
- Define Exfiltration Channels: Identify potential channels (HTTP/S uploads, DNS tunneling, email attachments, cloud storage, removable media, encrypted protocols).
- Baseline Normal Data Flows: Establish baseline outbound data transfer volumes per user, host, and destination over a 30-day window.
- Detect Volume Anomalies: Identify hosts or users transferring significantly more data than baseline to external destinations.
- Analyze Transfer Destinations: Check destination domains/IPs against threat intel, identify newly registered domains, personal cloud storage, and foreign infrastructure.
- Inspect Protocol Abuse: Look for DNS tunneling (large/frequent TXT queries), ICMP tunneling, or data hidden in allowed protocols.
- Correlate with File Access: Link exfiltration indicators to file access events on sensitive file shares, databases, or repositories.
- 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
- Cloud Storage Exfiltration: User uploads sensitive documents to personal Google Drive or Dropbox via browser.
- DNS Tunneling: Malware exfiltrates data encoded in DNS subdomain queries to attacker-controlled nameserver.
- HTTPS Upload: Compromised system POSTs large data blobs to C2 server over encrypted HTTPS.
- Email Attachment Exfiltration: Insider forwards sensitive documents to personal email accounts.
- 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]
FAQ
What are 'data exfiltration indicators'?
These are signals or patterns in network traffic that suggest data is being copied and sent out of the network without authorization.
What kind of suspicious data can it find?
It can find data being sent to personal cloud services, using DNS strangely to hide information, or large data volumes in general.
Do I need to install anything extra?
Yes, this tool works best when combined with other security tools that already monitor network logs, DNS, and cloud uploads.
Details
Original description by the author
Hunt for data exfiltration by analyzing Zeek and Suricata network telemetry for unusual data flows, DNS tunneling via large/frequent TXT queries, uploads to personal cloud storage, and encrypted-channel abuse, correlated against threat intel on destination domains. Use when hunting for data theft in a compromised environment, investigating unusual outbound data volumes, or determining what data wa
Popularity
- GitHub stars
- 35k
- Ranking position
- #5789
- License
- Apache-2.0
- Updated
- 8/31/2026
Comments
- …
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