iSniff GPS passively sniffs for SSID probes, ARPs and MDNS (Bonjour) packets broadcast by nearby iPhones, iPads and other wireless devices. The aim is to collect data which can be used to identify each device and determine previous geographical locations, based solely on information each device discloses about previously joined WiFi networks.
iOS devices transmit ARPs which sometimes contain MAC addresses (BSSIDs) of previously joined WiFi networks, as described in [1]. iSniff GPS captures these ARPs and submits MAC addresses to Apple's WiFi location service (masquerading as an iOS device) to obtain GPS coordinates for a given BSSID. If only SSID probes have been captured for a particular device, iSniff GPS can query network names on wigle.net and visualise possible locations.
By geo-locating multiple SSIDs and WiFi router MAC addresses, it is possible to determine where a device (and by implication its owner) is likely to have been.
Below: Visualisation of WiFi router locations in Melbourne, AU returned by Apple Location Services
iSniff GPS contains 2 major components and further python modules:
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iSniff_import.py uses Scapy to extract data from a live capture or pcap file and inserts it into a database (iSniff_GPS.sqlite3 by default).
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A Django web application provides a browser-based interface to view and analyse the data collected. This includes views of all detected devices and the SSIDs / BSSIDs each has probed for, a view by network, Google Maps views for visualising possible locations of a given BSSID or SSID, and a pie chart view showing a breakdown of the most popular device manufacturers based on client MAC address Ethernet OUIs.
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wloc.py provides a QueryBSSID() function which looks up a given BSSID (AP MAC address) on Apple's WiFi location service. It will return the coordinates of the MAC queried for and usually an additional 400 nearby BSSIDs and their coordinates.
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wigle_api.py provides a getLocation() function for querying a given SSID on the wigle.net database and returns GPS coordinates. It must be configured with a valid username and password set in the settings.py file. Please respect the wigle.net ToS in using this module.
To use the web interface:
- Install or update required Python modules by running
pip install -U -r requirements.txt. - Initialise an empty database by running
./manage.py syncdb. - Start the web interface by running
./manage.py runserver 127.0.0.1:8000.
To sniff wifi traffic:
- Install Scapy
- Import data from a wifi pcap capture by running
./run.sh -r <chan11.pcap> - For live capture, bring up a wifi interface in monitor mode (usually mon0) so that airodump-ng shows traffic.
- Start live sniffing with
./run.sh -i mon0.
To solicit ARPs from iOS devices, set up an access point with DHCP disabled (e.g. using airbase-ng) and configure your sniffing interface to the same channel.
Once associated, iOS devices will send up to three ARPs destined for the MAC address of the DHCP server on previously joined networks. On typical home WiFi routers, the DHCP server MAC address is the same as the WiFi interface MAC address, which can be used for accurate geolocation. On larger corporate WiFi networks, the MAC of the DHCP server may be different and thus cannot be used for geolocation.
Note that as of iOS 6, DNAv4 ARPs containing the information disclosure should only be sent on encrypted networks (to be verified). See http://lists.apple.com/archives/security-announce/2012/Sep/msg00003.html (CVE-2012-3725)
See requirements.txt for python modules and versions required.
iSniff GPS was developed on a Ubuntu 12.04 (32-bit) VM with Python 2.7.3, Django 1.5.4 and Scapy 2.2.0-dev. The web interface code has been updated and tested with Django 1.7.1 running on Mac OS X Yosemite with Python 2.7.8. Network sniffing has not been tested on Mac OS X.
Written by @hubert3 / hubert(at)pentest.com. Presented at Blackhat USA July 2012, code published on Github 2012-08-31.
The implementation of wloc.py is based on work by François-Xavier Aguessy and Côme Demoustier [2].
Mark Wuergler of Immunity, Inc. provided helpful information through mailing list posts and Twitter replies.
Includes Bluff JS chart library by James Coglan.
