Grey bar Blue bar
Share this:

Thu, 12 Dec 2013

Never mind the spies: the security gaps inside your phone

For the last year, Glenn and I have been obsessed with our phones; especially with regard to the data being leaked by a device that is always with you, powered on and often provided with a fast Internet connection. From this obsession, the Snoopy framework was born and released.


After 44con this year, Channel 4 contacted us to be part of a new experimental show named 'Data Baby', whose main goal is to grab ideas from the security community, and transform them into an easy-to-understand concept screened to the public during the 7 o'clock news.


Their request was simple: Show us the real threat!


To fulfil their request, we setup Snoopy to intercept, profile and access data from a group of "victim" students at a location in Central London. While this is something we've done extensively over the past twelve months, we've never had to do it with a television crew and cameras watching your every move!


The venue, Evans and Peel Detective Agency, added to the sinister vibe with their offices literally located underground. We were set up in a secret room behind a book case like friggin spies and got the drones ready for action. As the students arrived, we had a single hour to harvest as much information as we could. Using Snoopy, Maltego and a whole lot of frantic clicks and typing (hacking under stress is not easy), we were filmed gaining access to their inbox's and other personal information.


In the end, Snoopy and Maltego delivered the goods and Glenn added a little charm for the ladies.



After the segment was aired, we participated in a live Twitter Q&A session with viewers (so, so many viewers, we had to tag in others to help reply to all the tweets) and gave advice on how they could prevent themselves from being the next victim. Our advice to them, and indeed anyone else concerned is:


How to avoid falling foul of mobile phone snooping
- Be discerning about when you switch Wi-Fi on
- Check which Wi-Fi network you're connecting to; if you're connecting to Starbucks when you're nowhere near a branch, something's wrong
- Download the latest updates for your phone's operating system, and keep the apps updated too
- Check your application providers (like e-mail) security settings to make sure all your email traffic is "encrypted", not just the login process
- Tell your phone to forget networks once you're done with them, and be careful about joining "open" aka "unencrypted" networks

Fri, 1 Nov 2013

A new owner for a new chapter

We're pleased to announce our acquisition today by SecureData Europe.


SecureData (www.secdata.com) is a complete independent security services provider based in the UK and was also previously part of the SecureData Holdings group before being acquired by management in November 2012. The strategic acquisition complements SecureData's vision for enabling an end-to-end, proactive approach to security for global customers by assessing risk, detecting threats in real-time, protecting valuable assets and responding to security issues when they occur.


This deal signals the culmination of a long period of negotiation between SecureData Holdings, SecureData Europe and SensePost management and represents a cordial and amicable arrangement that is considered to be to the benefit of all three businesses. As the management of SensePost we are fully committed to this change, which we believe is in the best interests of SensePost, our staff and our customers. We believe this move will herald for us a new era of growth and development that will see us better equipped and prepared to meet the requirements of the market and fulfil our mission of providing insight, information and systems that enable our customers to make informed decisions about information security.


We look forward to a to an exciting period of innovation, growth and development that we believe this transaction will ultimately enable!

Fri, 12 Apr 2013

Analysis of Security in a P2P storage cloud

A cloud storage service such as Microsoft SkyDrive requires building data centers as well as operational and maintenance costs. An alternative approach is based on distributed computing model which utilizes portion of the storage and processing resources of consumer level computers and SME NAS devices to form a peer to peer storage system. The members contribute some of their local storage space to the system and in return receive "online backup and data sharing" service. Providing data confidentiality, integrity and availability in such de-centerlized storage system is a big challenge to be addressed. As the cost of data storage devices declines, there is a debate that whether the P2P storage could really be cost saving or not. I leave this debate to the critics and instead I will look into a peer to peer storage system and study its security measures and possible issues. An overview of this system's architecture is shown in the following picture:


Each node in the storage cloud receives an amount of free online storage space which can be increased by the control server if the node agrees to "contribute" some of its local hard drive space to the system. File synchronisation and contribution agents that are running on every node interact with the cloud control server and other nodes as shown in the above picture. Folder/File synchronisation is performed in the following steps:



1) The node authenticates itself to the control server and sends file upload request with file meta data including SHA1 hash value, size, number of fragments and file name over HTTPS connection.


2) The control server replies with the AES encryption key for the relevant file/folder, a [IP Address]:[Port number] list of contributing nodes called "endpoints list" and a file ID.


3) The file is split into blocks each of which is encrypted with the above AES encryption key. The blocks are further split into 64 fragments and redundancy information also gets added to them.


4) The node then connects to the contribution agent on each endpoint address that was received in step 2 and uploads one fragment to each of them


Since the system nodes are not under full control of the control server, they fall offline any time or the stored file fragments may become damaged/modified intentionally. As such, the control server needs to monitor node and fragment health regularly so that it may move lost/damaged fragments to alternate nodes if need be. For this purpose, the contribution agent on each node maintains an HTTPS connection to the control server on which it receives the following "tasks":


a) Adjust settings : instructs the node to modify its upload/download limits , contribution size and etc


b) Block check : asks the node to connect to another contribution node and verify a fragment existence and hash value


c) Block Recovery : Assist the control server to recover a number of fragments


By delegating the above task, the control system has placed some degree of "trust" or at least "assumptions" about the availability and integrity of the agent software running on the storage cloud nodes. However, those agents can be manipulated by malicious nodes in order to disrupt cloud operations, attack other nodes or even gain unauthorised access to the distributed data. I limited the scope of my research to the synchronisation and contribution agent software of two storage nodes under my control - one of which was acting as a contribution node. I didn't include the analysis of the encryption or redundancy of the system in my preliminary research because it could affect the live system and should only be performed on a test environment which was not possible to set up, as the target system's control server was not publicly available. Within the contribution agent alone, I identified that not only did I have unauthorised access file storage (and download) on the cloud's nodes, but I had unauthorised access to the folder encryption keys as well.


a) Unauthorised file storage and download


The contribution agent created a TCP network listener that processed commands from the control server as well as requests from other nodes. The agent communicated over HTTP(s) with the control server and other nodes in the cloud. An example file fragment upload request from a remote node is shown below:



Uploading fragments with similar format to the above path name resulted in the "bad request" error from the agent. This indicated that the fragment name should be related to its content and this condition is checked by the contribution agent before accepting the PUT request. By decompiling the agent software code, it was found that the fragment name must have the following format to pass this validation:


<SHA1(uploaded content)>.<Fragment number>.<Global Folder Id>


I used the above file fragment format to upload notepad.exe to the remote node successfully as you can see in the following figure:



The download request (GET request) was also successful regardless of the validity of "Global Folder Id" and "Fragment Number". The uploaded file was accessible for about 24 hours, until it was purged automatically by the contribution agent, probably because it won't receive any "Block Check" requests for the control server for this fragment. Twenty four hours still is enough time for malicious users to abuse storage cloud nodes bandwidth and storage to serve their contents over the internet without victim's knowledge.


b) Unauthorised access to folder encryption keys


The network listener responded to GET requests from any remote node as mentioned above. This was intended to serve "Block Check" commands from the control server which instructs a node to fetch a number of fragments from other nodes (referred to as "endpoints") and verify their integrity but re-calculating the SHA1 hash and reporting back to the control server. This could be part of the cloud "health check" process to ensure that the distributed file fragments are accessible and not tampered with. The agent could also process "File Recovery" tasks from the control server but I didn't observe any such command from the control server during the dynamic analysis of the contribution agent, so I searched the decompiled code for clues on the file recovery process and found the following code snippet which could suggest that the agent is cable of retrieving encryption keys from the control server. This was something odd, considering that each node should only have access to its own folders encryption keys and it stores encrypted file fragments of other nodes.


One possible explanation for the above file recovery code, could be that the node first downloads its own file fragments from remote endpoints (using an endpoint list received from the control server) and then retrieves the required folder encryption key from the control server in order to decrypt and re-assemble its own files. In order to test if it's possible to abuse the file recovery operation to gain access to encryption key of the folders belonging to other nodes. I extracted the folderInfo request format from the agent code and set up another storage node as a target to test this idea. The result of the test was successful as shown in the following figure and it was possible to retrieve the AES-256 encryption key for the Folder Id "1099869693336". This could enable an attacker who has set up an contributing storage node to decrypt the fragments that belong to other cloud users.



Conclusion:


While peer to peer storage systems have lower setup/maintenance costs, they face security threats from the storage nodes that are not under direct physical/remote control of the cloud controller system. Examples of such threats relate to the cloud's client agent software and the cloud server's authorisation control, as demonstrated in this post. While analysis of the data encryption and redundancy in the peer to peer storage system would be an interesting future research topic, we hope that the findings from this research can be used to improve the security of various distributed storage systems.

Tue, 11 Dec 2012

CSIR Cyber Games

The Council for Scientific and Industrial Research (CSIR) recently hosted the nation Cyber Games Challenge as part of Cyber Security Awareness month. The challenge pit teams of 4-5 members from different institutes against each other in a Capture the Flag style contest. In total there were seven teams, with two teams from Rhodes university, two from the University of Pretoria and three teams from the CSIR.


The games were designed around an attack/defence scenario, where teams would be given identical infrastructure which they could then patch against vulnerabilities and at the same time identify possible attack vectors to use against rival teams. After the initial reconnaissance phase teams were expected to conduct a basic forensic investigation to find 'flags' hidden throughout their systems. These 'flags' were hidden in images, pcap files, alternative data streams and in plain sight.


It was planned that teams would then be given access to a few web servers to attack and deface, gain root, patch and do other fun things to. Once this phase was complete the system would be opened up and the 'free-for-all' phase would see teams attacking each others systems. Teams would lose points for each service that was rendered inaccessible. Unfortunately due to technical difficulties the competition did not go as smoothly as initially planned. Once the games started the main website was rendered unusable almost immediately due to teams DirBuster to enumerate the competition scoring system. The offending teams were asked to cease their actions and the games proceeding from there. Two teams were disqualified after not ceasing their attacks on official infrastructure. Once teams tried to access their virtual infrastructure new problems arose, with only the two teams from Rhodes being able to access the ESX server while the rest of the teams based at the CSIR had no connectivity. This was rectified, at a cost, resulting in all teams except for the two Rhodes teams having access to their infrastructure. After a few hours of struggle it was decided to scrap the attack/defence part of the challenge. Teams were awarded points for finding hidden flags, with the most basic flag involving 'decoding' a morse-code pattern or a phrase 'encrypted' using a quadratic equation. It was unfortunate that the virtual infrastructure did not work as planned as this was to be the main focus of the games and sadly without it many teams were left with very little to do in the time between new 'flag' challenges being released.


In the days prior to the challenge our team, team Blitzkrieg, decided to conduct a social engineering exercise. We expected this to add to the spirit of the games and to introduce a little friendly rivalry between the teams prior to the games commencing. A quick google search for "CSIR Cyber Games" revealed a misconfigured cyber games server that had been left exposed on a public interface. Scrapping this page for information allowed us to create a fake Cyber Games site. A fake Twitter account was created on behalf of the CSIR Cyber Games organisers and used to tweet little titbits of disinformation. Once we had set-up our fake site and twitter account, a spoofed email in the name of the games organiser was sent out to all the team captains. Teams were invited to follow our fake user on twitter and to register on our cyber games page. Unfortunately this exercise did not go down too well with the games organisers and our team was threatened with disqualification or starting the games on negative points. In hindsight we should have run this by the organisers first to insure that it was within scope. After the incident we engaged with the organisers to explain our position and intentions, they were very understanding and decided to not disqualify us and waver any point based penalty. As part of our apology, we agreed to submit a few challenges for next years Cyber Games.


Overall we believe concept of using structured Cyber Games to promote security awareness is both fun and useful. While the games were hampered by network issues there was enough content available to make for an entertaining and exciting afternoon. The rush of solving challenges as fast as possible and everyone communicating ideas made for an epic day. In closing, the CSIR Cyber Games was a success, as with all things we believe it will improve over time and provide a good platform to promote security awareness.


For the defacement phase of the games we made a old school defacement page.

Tue, 25 Sep 2012

Snoopy: A distributed tracking and profiling framework

At this year's 44Con conference (held in London) Daniel and I introduced a project we had been working on for the past few months. Snoopy, a distributed tracking and profiling framework, allowed us to perform some pretty interesting tracking and profiling of mobile users through the use of WiFi. The talk was well received (going on what people said afterwards) by those attending the conference and it was great to see so many others as excited about this as we have been.

In addition to the research, we both took a different approach to the presentation itself. A 'no bullet points' approach was decided upon, so the slides themselves won't be that revealing. Using Steve Jobs as our inspiration, we wanted to bring back the fun to technical conferences, and our presentation hopefully represented that. As I type this, I have been reliably informed that the DVD, and subsequent videos of the talk, is being mastered and will be ready shortly. Once we have it, we will update this blog post. In the meantime, below is a description of the project.

Background

There have been recent initiatives from numerous governments to legalise the monitoring of citizens' Internet based communications (web sites visited, emails, social media) under the guise of anti-terrorism. Several private organisations have developed technologies claiming to facilitate the analysis of collected data with the goal of identifying undesirable activities. Whether such technologies are used to identify such activities, or rather to profile all citizens, is open to debate. Budgets, technical resources, and PhD level staff are plentiful in this sphere.

Snoopy

The above inspired the goal of the Snoopy project: with the limited time and resources of a few technical minds could we create our own distributed tracking and data interception framework with functionality for simple analysis of collected data? Rather than terrorist-hunting, we would perform simple tracking and real-time + historical profiling of devices and the people who own them. It is perhaps worth mentioning at this point that Snoopy is compromised of various existing technologies combined into one distributed framework.

"Snoopy is a distributed tracking and profiling framework."

Below is a diagram of the Snoopy architecture, which I'll elaborate on:

1. Distributed?

Snoopy runs client side code on any Linux device that has support for wireless monitor mode / packet injection. We call these "drones" due to their optimal nature of being small, inconspicuous, and disposable. Examples of drones we used include the Nokia N900, Alfa R36 router, Sheeva plug, and the RaspberryPi. Numerous drones can be deployed over an area (say 50 all over London) and each device will upload its data to a central server.

2. WiFi?

A large number of people leave their WiFi on. Even security savvy folk; for example at BlackHat I observed >5,000 devices with their WiFi on. As per the RFC documentation (i.e. not down to individual vendors) client devices send out 'probe requests' looking for networks that the devices have previously connected to (and the user chose to save). The reason for this appears to be two fold; (i) to find hidden APs (not broadcasting beacons) and (ii) to aid quick transition when moving between APs with the same name (e.g. if you have 50 APs in your organisation with the same name). Fire up a terminal and bang out this command to see these probe requests:

tshark -n -i mon0 subtype probereq

(where mon0 is your wireless device, in monitor mode)

2. Tracking?

Each Snoopy drone collects every observed probe-request, and uploads it to a central server (timestamp, client MAC, SSID, GPS coordinates, and signal strength). On the server side client observations are grouped into 'proximity sessions' - i.e device 00:11:22:33:44:55 was sending probes from 11:15 until 11:45, and therefore we can infer was within proximity to that particular drone during that time.

We now know that this device (and therefore its human) were at a certain location at a certain time. Given enough monitoring stations running over enough time, we can track devices/humans based on this information.

3. Passive Profiling?

We can profile device owners via the network SSIDs in the captured probe requests. This can be done in two ways; simple analysis, and geo-locating.

Simple analysis could be along the lines of "Hmm, you've previously connected to hooters, mcdonalds_wifi, and elCheapoAirlines_wifi - you must be an average Joe" vs "Hmm, you've previously connected to "BA_firstclass, ExpensiveResataurant_wifi, etc - you must be a high roller".

Of more interest, we can potentially geo-locate network SSIDs to GPS coordinates via services like Wigle (whose database is populated via wardriving), and then from GPS coordinates to street address and street view photographs via Google. What's interesting here is that as security folk we've been telling users for years that picking unique SSIDs when using WPA[2] is a "good thing" because the SSID is used as a salt. A side-effect of this is that geo-locating your unique networks becomes much easier. Also, we can typically instantly tell where you work and where you live based on the network name (e.g BTBusinessHub-AB12 vs BTHomeHub-FG12).

The result - you walk past a drone, and I get a street view photograph of where you live, work and play.

4. Rogue Access Points, Data Interception, MITM attacks?

Snoopy drones have the ability to bring up rogue access points. That is to say, if your device is probing for "Starbucks", we'll pretend to be Starbucks, and your device will connect. This is not new, and dates back to Karma in 2005. The attack may have been ahead of its time, due to the far fewer number of wireless devices. Given that every man and his dog now has a WiFi enabled smartphone the attack is much more relevant.

Snoopy differentiates itself with its rogue access points in the way data is routed. Your typical Pineapple, Silica, or various other products store all intercepted data locally, and mangles data locally too. Snoopy drones route all traffic via an OpenVPN connection to a central server. This has several implications:

(i) We can observe traffic from *all* drones in the field at one point on the server. (ii) Any traffic manipulation needs only be done on the server, and not once per drone. (iii) Since each Drone hands out its own DHCP range, when observing network traffic on the server we see the source IP address of the connected clients (resulting in a unique mapping of MAC <-> IP <-> network traffic). (iv) Due to the nature of the connection, the server can directly access the client devices. We could therefore run nmap, Metasploit, etc directly from the server, targeting the client devices. This is a much more desirable approach as compared to running such 'heavy' software on the Drone (like the Pineapple, pr Pwnphone/plug would). (v) Due to the Drone not storing data or malicious tools locally, there is little harm if the device is stolen, or captured by an adversary.

On the Snoopy server, the following is deployed with respect to web traffic:

(i) Transparent Squid server - logs IP, websites, domains, and cookies to a database (ii) sslstrip - transparently hijacks HTTP traffic and prevent HTTPS upgrade by watching for HTTPS links and redirecting. It then maps those links into either look-alike HTTP links or homograph-similar HTTPS links. All credentials are logged to the database (thanks Ian & Junaid). (iii) mitmproxy.py - allows for arbitary code injection, as well as the use of self-signed SSL certificates. By default we inject some JavaScipt which profiles the browser to discern the browser version, what plugins are installed, etc (thanks Willem).

Additionally, a traffic analysis component extracts and reassembles files. e.g. PDFs, VOiP calls, etc. (thanks Ian).

5. Higher Level Profiling? Given that we can intercept network traffic (and have clients' cookies/credentials/browsing habbits/etc) we can extract useful information via social media APIs. For example, we could retrieve all Facebook friends, or Twitter followers.

6. Data Visualization and Exploration? Snoopy has two interfaces on the server; a web interface (thanks Walter), and Maltego transforms.

-The Web Interface The web interface allows basic data exploration, as well as mapping. The mapping part is the most interesting - it displays the position of Snoopy Drones (and client devices within proximity) over time. This is depicted below:

-Maltego Maltego Radium has recently been released; and it is one awesome piece of kit for data exploration and visualisation.What's great about the Radium release is that you can combine multiple transforms together into 'machines'. A few example transformations were created, to demonstrate:

1. Devices Observed at both 44Con and BlackHat Vegas Here we depict devices that were observed at both 44Con and BlackHat Las Vegas, as well as the SSIDs they probed for.

2. Devices at 44Con, pruned Here we look at all devices and the SSIDs they probed for at 44Con. The pruning consisted of removing all SSIDs that only one client was looking for, or those for which more than 20 were probing for. This could reveal 'relationship' SSIDs. For example, if several people from the same company were attending- they could all be looking for their work SSID. In this case, we noticed the '44Con crew' network being quite popular. To further illustrate Snoopy we 'targeted' these poor chaps- figuring out where they live, as well as their Facebook friends (pulled from intercepted network traffic*).

Snoopy Field Experiment

We collected broadcast probe requests to create two main datasets. I collected data at BlackHat Vegas, and four of us sat in various London underground stations with Snoopy drones running for 2 hours. Furthermore, I sat at King's Cross station for 13 hours (!?) collecting data. Of course it may have made more sense to just deploy an unattended Sheeva plug, or hide a device with a large battery pack - but that could've resulted in trouble with the law (if spotted on CCTV). I present several graphs depicting the outcome from these trials:

The pi chart below depicts the proportion of observed devices per vendor, from the total sample of 77,498 devices. It is interesting to see Apple's dominance. pi_chart

The barchart below depicts the average number of broadcast SSIDs from a random sample of 100 devices per vendor (standard deviation bards need to be added - it was quite a spread).

The barchart below depicts my day sitting at King's Cross station. The horizontal axis depicts chunks of time per hour, and the vertical access number of unique device observations. We clearly see the rush hours.

Potential Use

What could be done with Snoopy? There are likely legal, borderline, and illegal activities. Such is the case with any technology.

Legal -Collecting anonymized statistics on thoroughfare. For example, Transport for London could deploy these devices at every London underground to get statistics on peak human traffic. This would allow them to deploy more staff, or open more pathways, etc. Such data over the period of months and years would likely be of use for future planning. -Penetration testers targeting clients to demonstrate the WiFi threat.

Borderline -This type of technology could likely appeal to advertisers. For example, a reseller of a certain brand of jeans may note that persons who prefer certain technologies (e.g. Apple) frequent certain locations. -Companies could deploy Drones in one of each of their establishments (supermarkets, nightclubs, etc) to monitor user preference. E.g. a observing a migration of customers from one establishment to another after the deployment of certain incentives (e.g. promotions, new layout). -Imagine the Government deploying hundreds of Drones all over a city, and then having field agents with mobile Drones in their pockets. This could be a novel way to track down or follow criminals. The other side of the coin of course being that they track all of us...

Illegal -Let's pretend we want to target David Beckham. We could attend several public events at which David is attending (Drone in pocket), ensuring we are within reasonable proximity to him. We would then look for overlap of commonly observed devices over time at all of these functions. Once we get down to one device observed via this intersection, we could assume the device belongs to David. Perhaps at this point we could bring up a rogue access point that only targets his device, and proceed maliciously from there. Or just satisfy ourselves by geolocating places he frequents. -Botnet infections, malware distribution. That doesn't sound very nice. Snoopy drones could be used to infect users' devices, either by injection malicious web traffic, or firing exploits from the Snoopy server at devices. -Unsolicited advertising. Imagine browsing the web, and an unscrupulous 3rd party injects viagra adverts at the top of every visited page?


Similar tools

Immunity's Stalker and Silica Hubert's iSniff GPS

Snoopy in the Press

Risky Biz Podcast Naked Scientist Podcast(transcript) The Register Fierce Broadband Wireless

***FAQ***

Q. But I use WPA2 at home, you can't hack me! A. True - if I pretend to be a WPA[2] network association it will fail. However, I bet your device is probing for at least one open network, and when I pretend to be that one I'll get you.

Q. I use Apple/Android/Foobar - I'm safe! A. This attack is not dependent on device/manufacture. It's a function of the WiFi specification. The vast majority of observed devices were in fact Apple (>75%).

Q. How can I protect myself? A. Turn off your WiFi when you l leave home/work. Be cautions about using it in public places too - especially on open networks (like Starbucks). A. On Android and on your desktop/laptop you can selectively remove SSIDs from your saved list. As for iPhones there doesn't seem to be option - please correct me if I'm wrong? A. It'd be great to write an application for iPhone/Android that turns off probe-requests, and will only send them if a beacon from a known network name is received.

Q. Your research is dated and has been done before! A. Some of the individual components, perhaps. Having them strung together in our distributed configuration is new (AFAIK). Also, some original ideas where unfortunately published first; as often happens with these things.

Q. But I turn off WiFi, you'll never get me! A. It was interesting to note how many people actually leave WiFi on. e.g. 30,000 people at a single London station during one day. WiFi is only one avenue of attack, look out for the next release using Bluetooth, GSM, NFC, etc :P

Q. You're doing illegal things and you're going to jail! A. As mentioned earlier, the broadcast nature of probe-requests means no laws (in the UK) are being broken. Furthermore, I spoke to a BT Engineer at 44Con, and he told me that there's no copyright on SSID names - i.e. there's nothing illegal about pretending to be "BTOpenzone" or "SkyHome-AFA1". However, I suspect at the point where you start monitoring/modifying network traffic you may get in trouble. Interesting to note that in the USA a judge ruled that data interception on an open network is not illegal.

Q. But I run iOS 5/6 and they say this is fixed!! A. Mark Wuergler of Immunity, Inc did find a flaw whereby iOS devices leaked info about the last 3 networks they had connected to. The BSSID was included in ARP requests, which meant anyone sniffing the traffic originating from that device would be privy to the addresses. Snoopy only looks at broadcast SSIDs at this stage - and so this fix is unrelated. We haven't done any tests with the latest iOS, but will update the blog when we have done so.

Q. I want Snoopy! A. I'm working on it. Currently tidying up code, writing documentation, etc. Soon :-)