Full Report
Friday the 13th seemed like as good a date as any to release Snoopy 2.0 (aka snoopy-ng). For those in a rush, you can download the source from GitHub, follow the README.md file, and ask for help on this mailing list. For those who want a bit more information, keep reading. What is Snoopy? Snoopy is a distributed, sensor, data collection, interception, analysis, and visualization framework. It is written in a modular format, allowing for the collection of arbitrary signals from various devices via Python plugins.
Analysis Summary
# Tool/Technique: Snoopy 2.0 (snoopy-ng)
## Overview
Snoopy 2.0 (snoopy-ng) is a distributed, sensor, data collection, interception, analysis, and visualization framework. It is designed to collect data from various devices by interfacing with multiple wireless technologies using modular Python plugins. Its primary research goal relates to uniquely identifying devices and discovering information about their owners via emitted wireless signals.
## Technical Details
- Type: Attack Tool / Framework
- Platform: Linux (Tested on Kali 1.x, Ubuntu 12.04 LTS, Maemo (N900). Investigating OpenWRT/ddWRT compatibility.)
- Capabilities: Distributed data collection, signal interception, analysis, visualization, modular Python plugin architecture for supporting various protocols (Wi-Fi, Bluetooth, GSM, NFC, RFID, ZigBee).
- First Seen: June 13, 2014 (This version, 2.0)
## MITRE ATT&CK Mapping
Snoopy is an espionage and collection framework. The closest relevant mappings relate to the collection of data from the environment.
- **TA0043 - C2 Channel**
- T1105 - Ingress Tool Transfer (If downloading plugins or data synchronization is involved)
- **TA0041 - Collection**
- T1120 - Peripheral Device Discovery (Leveraging hardware adapters to interface with local signals)
- **TA0008 - Lateral Movement** (If used in a distributed manner across internal infrastructure)
## Functionality
### Core Capabilities
* **Distributed Operation:** Capable of deploring numerous sensor nodes that synchronize data back to a central server.
* **Modular Design:** Written mostly in Python (99%), allowing for easy creation/integration of plugins to interface with specific technologies.
* **Signal Collection:** Collects arbitrary signals from various devices using plugins supporting protocols like Wi-Fi, Bluetooth, GSM, NFC, RFID, and ZigBee.
* **Data Visualization:** Preferred visualization tool is Maltego.
### Advanced Features
* **Protocol Support Extension:** Extends collection capabilities beyond the initial PoC to include RFID, Wi-Fi, Bluetooth, GSM, NFC, and ZigBee.
* **Airborne Deployment:** Can be integrated onto UAVs (drones) for rapid, high-altitude, stealthy area canvassing and TTL (Tag, Track, Locate) operations.
* **Commercial Enhancements (License Dependent):** Includes features like XBee synchronization, advanced plugins, extra transforms, a web interface, and prebuilt drone integration.
## Indicators of Compromise
The article focuses on the tool's functionality and installation process rather than specific malicious deployment IOCs.
- File Hashes: N/A (Source available via GitHub)
- File Names: `snoopy-ng` (directory name/command)
- Registry Keys: N/A
- Network Indicators: Used for synchronization/C2, example command used `http://<server>:<port>/`. (Defanged: http://<server>:<port>/)
- Behavioral Indicators: Execution of the `snoopy` command; use of specific Python modules for hardware interfacing (e.g., Wi-Fi monitoring interfaces like `mon0`); data synchronization attempts via HTTP.
## Associated Threat Actors
This tool appears to be a publicly released framework developed by SensePost (Authors associated with the release include Glenn Wilkinson). It is described as research material, though its advanced capabilities (especially airborne deployment) suggest potential use by highly sophisticated actors or penetration testing teams. No specific named threat groups are listed as users in the provided text.
## Detection Methods
Detection logic would focus on the installation and runtime environment of the framework.
- Signature-based detection: Signatures for the source code or specific binary components if compiled.
- Behavioral detection: Monitoring for the execution of the `snoopy` command, attempts to load required Python dependencies, modification of network interfaces to monitor mode (e.g., `wlanX` becoming `mon=True` or `mon0`), and outbound traffic attempting to sync data using its defined protocols.
- YARA rules: Could be written against the unique Python source components.
## Mitigation Strategies
Since Snoopy relies on specific hardware adapters and software environments, mitigation focuses on controlling deployment and environment access.
- **Environment Hardening:** Restrict installation of non-approved Python dependencies or software stacks (especially on sensitive endpoints).
- **Physical Security:** Control physical access necessary to attach specialized hardware adapters (Wi-Fi, SDRs, specialized radio hardware) required for many collection plugins.
- **Network Monitoring:** Monitor for unusual sync traffic patterns originating from internal assets using the framework's defined connection methods (e.g., HTTP sync endpoints).
## Related Tools/Techniques
* **Snoopy (Original version):** Predecessor PoC released at 44Con 2012.
* **Maltego:** Used for visualization of collected data.
* **Hardware Probes/Adapters:** AWUS 036H (Wifi), Ubertooth (Bluetooth), RTL2832U SDR (GSM), RFidler (RFID), ACR122U (NFC), Digi Xbee (ZigBee).