This is a tool that I have wanted to build for at least 5 years. Checking my archives, the earliest reference I can find is almost exactly 5 years ago, and I've been thinking about it for longer, I'm sure.
Finally it has made it out of my head, and into the real world!
Be free! Be free!
So, what does it do, and how does it do it?
The core idea for this tool comes from the realisation that, when reviewing how web applications work, it would help immensely to be able to know which user was actually making specific requests, rather than trying to just keep track of that information in your head (or not at all). Once you have an identity associated with a request, that enables more powerful analysis of the requests which have been made.
In particular, it allows the analyst to compare requests made by one user, to requests made by another user, even as those users log in and log out.
There are various ways in which users can be authenticated to web applications, and this extension doesn't try to handle them all, not just yet, anyway. It does handle the most common case, though, which is forms-based auth, with cookie-based session identifiers.
So, as a first step, it allows you to identify the "log in" action, extract the name of the user that is authenticating, and associate that identity with the session ID until it sees a "log out" action. Which is pretty useful in and of itself, I think. Who hasn't asked themselves, while reviewing a proxy history: "Now which user was I logged in as, when I made this request?" Or: "Where is that request that I made while logged in as 'admin'?"
So, how does it do this? Unfortunately, the plugin doesn't have AI, or a vast database of applications all captured for you, with details of how to identify logins and logouts. But it does have the ability to define a set of rules, so you can tell it how your app behaves. These rules can be reviewed and edited in the "Options" tab of the Identity extension.
What sort of rules do we need? Well, to start with, what constitutes a valid logon? Typically, that may include something like "A POST to a specified URL, that gets a 200 response without the text 'login failed' in it". And we need to know which form field contains the username. Oh, and the sessionid in use by the application, so that the next time we see a sessionid with the same value, we can link that same identity to that conversation as well.
The easiest way to create the login rule is probably via the Http Proxy History tab. Just right click on a valid login request, and choose "Identity -> create login rule". It will automatically create a rule that matches the request method, request path, and the response status. Of course, you can customise it as you see fit, adding simple rules (just one condition), or complex rules (this AND that, this OR that), nested to arbitrary levels of complexity. And you can select the session id parameter name, and login parameter name on the Options tab as well.
Awesome! But how do we identify when the user logs out? Well, we need a rule for that as well, obviously. This can often be a lot simpler to identify. An easy technique is just to look for the text of the login form! If it is being displayed, you're very unlikely to be logged in, right? That can also catch the cases where a session gets timed out, but for the moment, we have separate rules and states for "logged out" and "timed out". That may not be strictly necessary, though. Again, these rules can be viewed and edited in the Options tab. Another easy way to create the logout rule is to select the relevant text in the response, right-click, and choose "Identity -> create logout rule".
Sweet! So now we can track a series of conversations from an anonymous user, through the login process, through the actions performed by the person who was logged in, through to the end of that session, whether by active logout, or by inactivity, and session timeout, back to an anonymous user.
Most interestingly, though, by putting the conversations into a "spreadsheet", and allowing you to create a pivot table of selected parameters vs the identity of the person making the request, it becomes possible to almost automate the testing of access control rules.
This tool is not quite at the "automated" stage yet, but it does currently allow you to see which user has performed which actions, on which subject, which makes it almost trivial to see what each user is able to do, and then formulate tests for the other users. You can also see which tests you have executed, as the various cells in the pivot table start filling up.
In this screenshot, we are pivoting on the path of the URL, the method (GET vs POST), and then a bunch of parameters. In this application (WordPress, just for demonstration purposes), we want the "action" parameter, as well as the parameter identifying the blog post being operated on. The "action" parameter can appear in the URL, or in the Body of the request, and the "post" parameter in the URL identifies the blog post, but it is called post_ID in the body. (It might be handy to be able to link different parameters that mean the same thing, for future development!). The resulting table creates rows for each unique parameter combination, exactly as one would expect in an Excel pivot table.
Clicking on each cell allows you to get a list of all the conversations made by that userid, with the specific combination of parameters and values, regardless of the number of times that they had logged in and out, or how many times their session id changed. Clicking on each conversation in the list brings up the conversation details in the request/response windows at the bottom, so you can check the minutiae, and if desired, right-click and send them to the repeater for replay.
So far, the approach has been to manually copy and paste the session cookie for a different user into the repeater window before replaying the request, but this is definitely something that lends itself to automation. A future development will have an option to select "current" session tokens for identified users, and substitute those in the request before replaying it.
So far, so good! But, since the point of this extension is to check access controls, we'd ideally like to be able to say whether the replayed request was successful or not, right? There's a rule for that! Or there could be, if you defined them! By defining rules that identify "successful" requests vs "failed" requests, conversations can be tagged as successful or not, making it easier to see when reviewing lists of several conversations. Future development is intended to bring that data "up" into the pivot table too, possibly by means of colouring the cells based on the status of the conversations that match. That could end up showing a coloured matrix of successful requests for authorised users, and unsuccessful requests for unauthorised users, which, ultimately, is exactly what we want.
We'd love to hear how you get on with using this, or if you have any feature requests for the plugin. For now, the BurpId plugin is available here.
A few days ago, during one of those nights with the baby crying at 2:00 am and the only thing you can do is to read emails, I realised that Gmail shows the content of compressed files when reading them in Google Docs. As often is the case at SensePost, the "think evil (tm)" came to me and I started to ponder the possibilities of injecting HTML inside the file listing. The idea is actually rather simple. Looking at the file format of a .zip file we see the following:
Every file in the compressed file must have two entries; ZipFileRecord and ZipDirEntry. Both of these entries contain the filename, but only the first one contains the length of filename (it must match the actual length). Our first test case is obvious; if we could modify this name once the file was compressed, would Google sanitise it? Thankfully, the answer is, yes! (go Google!)
As you can see, Google shows the file name inside the compressed file but the tag is displayed with HTML entities. If we then try to see the contents of the file, Google responds by telling us it's not possible to read the content of the file (it's empty) and shows you the file "without formatting" after a few seconds:
Finally, the filename is shown but not sanitised:
Why this is possible?
Remember that the zip format has the name of the compressed files twice. Google uses the first one (ZipFileRecord) for displaying the file names, but in the vulnerable page it uses the second one (ZipDirEntry).
Possible attack vectors
Going back to the 'thinking evil (tm)' mindset, it is now possible to leave a "comprehensive" name in the first entry and inject the malicious payload in the second one. When I first discovered the possibility of doing this, I contacted Google, however, the XSS is in the googleusercontent.com domain, which Google's security team described as a "sandbox" domain (i.e. we aren't injecting into the DOM of google.com) and therefore not worthy of a bounty. Which I accept, if I had to prove usefulness this could be used as part of a simple social engineering attack, for example:
Leading the victim to my phishing site:
Which then proceeds to steals their Google session, or allows the attacker to use BeEF:
Granted, there are simpler ways of achieving the same result. I just wanted to demonstrate how you can use file meta-information for such an attack.
Have a keen interest on scanning over 12000 IP's a week for vulnerabilities? Excited about the thought of assessing over 100 web applications for common vulnerabilities? If so, an exciting, as well as demanding, position has become available within the Managed Vulnerability Scanning (MVS) team at SensePost.
Job Title: Vulnerability Management Analyst
Salary Range: Industry standard, commensurate with experience
Location: Johannesburg/Pretoria, South Africa
We are looking for a talented person to join our MVS team to help manage the technology that makes up our Broadview suite and, more importantly, finding vulnerabilities, interpreting the results and manually verifying them. We are after talented people with a broad skill set to join our growing team of consultants. Our BroadView suite of products consists of our extensive vulnerability scanning engine, which looks at both the network-layer and the application layer, as well as our extensive DNS footprinting technologies.
The role of the Vulnerability Management Analyst will possess the following skills:
SensePost is an equal opportunity partner.
Hackathons are used by many tech companies to give their employees breathing space to work on new ideas. Google and Facebook are big fans and Facebook's Like button was conceived as part of a hackathon. Getting everyone together at the same time was no mean feat, the term 'herding cats' springs to mind but on the week of 12th of November, all SensePost'rs were in our new offices and ready to break, build and develop.
Prior to the event, we asked everyone to think about what they wanted to work on. As mentioned above, there was no specific guideline as to what anyone could come up with, as you can't force creativity. After a brainstorming session, the following ideas were given and solutions made during the hackathon period*:
1. SensePost World App
A mobile application (multi-platform) that will streamline the process of receipts, expenses, travel requests, holiday leave etc.
2. SensePost IRC Bot
A IRC bot that will offer:
An application that allows us to utilise SMS from a company-wide perspective, including:
4. Magstripe Hacking
Having moved into our new fancy offices, we decided to look at the current implementation of magstripe used to work out if we could read the data, clone the data and create free parking for us (at the same time, potentially looking for flaws in the magstripe implementation). The magstripes on the parking tickets were very unsual. Between the reader in the office, and Andrew Mohawk's more advanced ones, we could not get a consistent read. It is possible that the cards use an unusual arrangement of tracks. Typically there are 3 horizontal tracks at predefined heights. If the tracks are at unusual heights we may have been getting interference between said tracks. Andrew has tried to dissect one of the cards, but no luck yet.
Watch this space. 5. AV VirusTotal Project
Rather than submitting our payloads to VirusTotal (who then inform the vendors), we will create our own version that uses all vendors, to determine if our custom payloads could be detected.
6. SensePost Green Project
A project to make our business greener in approach and ideas. How responsibly were we using resources? What was our consumption of electricity and water like and could it be made better?
With teams created and everyone clear on what they had to do, 48-hours were given to create the above ideas. Food, drink, hardware and toys were provided. Vlad brought some amazing Russian Vodka and energy drinks were supplied.
The cool thing about the hackathon was that some of the top ideas came from traditionally non-technical people, such as our finance wizard who came up with the idea of the SensePost world app. This was the outcome that we wanted: to prove that you don't need to be a heavy tech-orientated person to come up with meaningful projects or ideas.
Overall the 2012 Hackathon was a brilliant time had. Some amazing ideas have come to light, ones that will see us pushing offensive approaches and also ones that will have an impact on the way we work at SensePost.
For those thinking about running an internal hackathon, I'd say go for it. Giving people the space to work on ideas with likeminded colleagues will only bring benefits.
*There were other projects, but they won't see the light of day as of yet, so will remain confidential until the time is right.
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.
"Snoopy is a distributed tracking and profiling framework."
Below is a diagram of the Snoopy architecture, which I'll elaborate on:
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.
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)
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 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:
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*).
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 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.
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?
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 :-)