At Defcon 22 we presented several improvements in wifi rogue access point attacks. We entitled the talk "Manna from heaven" and released the MANA toolkit. I'll be doing two blog entries. The first will describe the improvements made at a wifi layer, and the second will cover the network credential interception stuff. If you just want the goodies, you can get them at the end of this entry for the price of scrolling down.
This work is about rogue access points, by which we mean a wireless access point that mimics real ones in an attempt to get users to connect to it. The initial work on this was done in 2004 by Dino dai Zovi and Shaun Macaulay. They realised that the way wifi devices probe for wireless networks that they've "remembered" happens without authentication, and that if a malicious access point merely responds to these directed probes, it can trick wireless clients into connecting to it. They called this a KARMA attack.
Additionally, Josh Wright and Brad Antoniewicz in 2008 worked out that if you man in the middle the EAP authentication on secured networks, you could crack that hash and gain access to the network yourself. They implemented this in freeradius-wpe (wireless pwnage edition).
However, KARMA attacks no longer work well, and we wanted to know why. Also, the WPE stuff seemed ripe for use in rogue access points rather than just for gaining access to the original network. This is what we implemented.
Changes in Probing
After a significant amount of time poring over radio captures of the ways in which various devices probed, and informed by our previous work on Snoopy, we realised two things. The first is that modern devices, particularly mobile ones, won't listen to directed probe responses for open, non-hidden networks if that AP didn't also/first respond to a broadcast probe. What this means is that our rogue access point needs to implement the same. However, the challenge is, what do we respond to the broadcast probe *with*?
To overcome that, we took the existing KARMA functionality built by Digininja, ported it to the latest version of hostapd and extended it to store a view of the "remembered networks" (aka the Preferred Network List (PNL)) for each device it sees. Then when hostapd-mana sees a broadcast probe from that device, it will respond with a directed probe response for each network hostapd-mana knows to be in that device's PNL. This is based on our finding, that wifi clients don't have a problem with a single BSSID (i.e. AP MAC address) to have several ESSIDs (aka SSID aka network name).
Practically, suppose there are two devices, one probing for a network foo, and the other probing for two networks bar and baz. When device1 sends a broadcast probe, hostapd-mana will respond with a directed probe response for foo to device1. Likewise when device2 sends a broadcast probe, hostapd-mana will respond with two directed probe responses to device 2, one for bar and one for baz. In addition, the "normal" KARMA functionality of responding to directed probes will also occur. Practically, we found this significantly improved the effectiveness of our rogue AP.
iOS and hidden networks
iOS presented an interesting challenge to us when it came to hidden networks. A hidden network is one configured not to broadcast its ESSID in either its beacons or broadcast probe response. Practically, the only way a client device can know if the hidden network it has remembered is nearby is by constantly sending out directed probes for the network. This is why hidden networks aren't a very good design, as their clients need to spew their names out all over the place. However, when observing iOS devices, while they could join a hidden network just fine, they seemed to not probe for it most of the time. This had us constructing faraday cages, checking other factors like BSSID and geolocation to no avail. Until we realised that iOS will not probe for any hidden networks in its PNL, unless there is at least one hidden network nearby. So, if you'd like to maximise your rogue'ing, make sure you have a hidden network nearby. It doesn't even need to be a real network; use a mifi, use airbase-ng or just create another hostapd network.
Limits in probing
Modern mobile devices probe for networks on their PNL *significantly* less than laptops or older devices do. In an ideal world, manufacturers would change the implementation to never probe for open network, and only wait for a response to a broadcast, effectively limiting these attacks to requiring pre-knowledge, common networks or being performed in the vicinity of the actual network. Actually, a patch was pushed to wpa_supplicant to limit the stupid probing behaviour Android does in low power mode a few months ago, this will make it into Android proper sometime soon. Also, iOS has significantly reduced how often it probes.
There are two ways to work around this. The first is manual; go for a common network. The rise of city-wide wifi projects makes this somewhat easy. Or if you're going for a corporate network, just do some recon and name one of your access points after that. But, we wanted to make things work better than that. The default behaviour of hostapd-mana is to build up a view of each devices PNL and only respond to broadcasts with networks specific to that device. However, we can remove that limitation and build a global PNL, and respond to each broadcast with every network every device has probed for. We call this loud mode, and it's configurable in the hostapd-mana config. This relies on the fact that many devices, particularly laptops and older mobile devices still probe for networks a lot. It also relies on the fact that many devices have networks in common (have they been in the same city, same airport, same conference, same company, same pocket etc.). This works *very* well in less crowded areas, and you'll get a much higher number of devices connecting.
However, in busy areas, or if your antenna is large enough, you'll quickly exceed the capacity for your average wifi device to respond fast enough to all of the devices, and as the number of response probes grows exponentially with each new device, even in quiet areas over time, this problem crops up (but didn't on stage at Defcon miraculously). So, it's *good enough* for now, but needs an in-kernel or in-firmware implementation with some network ageing to scale a bit better (one of the many opportunities for extending this work if you're up for some open source contribution).
Auto Crack 'n Add
freeradius-wpe is great, it provides a nice way to grab EAP hashes for clients that don't validate certificates presented via EAP's that implement SSL (PEAP, EAP-GTC, PEAL-TTLS). However, the patches are for freeradius v1 and, much like the KARMA patches for hostapd, have aged. But, hostapd contains a radius server, and so we could port the freeradius-wpe work to that, something we based off some initial but incomplete patches by Brad Antoniewicz. So hostapd-mana will also let you grab EAP hashes without needing another tool.
However, the KARMA attacks only work against open wifi networks. EAP networks are increasingly common (especially corporate ones) and we wanted to be able to have a go at getting devices probing for those to connect to our rogue AP. To do this, we modified hostapd-mana to always accept any EAP hash, but send it off for cracking. It simply writes these to a file, from which the simple python tool crackapd (included) will grab it and send it off to another process for cracking. Currently, we use asleap (also by Josh) and the rockyou password list, but these can all be easily modified. For example, to use CloudCracker and its incredibly optimised MS-CHAPv2 cracking setup.
The net result is pretty great for simple EAP hashes. The device will try and connect, and fail as we don't know enough to do the challenge response right. But after the hash is cracked, when it retries to connect (something a device will keep doing) it will succeed (and you'll have your first creds). For simple hashes, this is transparent to the user. Of course, very complex hashes will only work if you crack them in time. Worst case scenario, you leave with hashes.
So that's what we built into hostapd-mana. You get improved KARMA attacks, a modern hostapd version, an integrated hash stealer, and the possibility of rogue'ing some EAP networks. You can get the full toolkit at MANA toolkit on GitHub or our hostapd-mana at hostapd-mana on GitHub.
The next blog entry will cover what we did once we got a device to connect.
The Defcon talk:
The supporting slide deck with more information:
The final toolkit: MANA toolkit on GitHub You can also get this on Kali with "apt-get install mana-toolkit"
The modified hostapd (for hackers or people who want to build their own setup): hostapd-mana on GitHub
SensePost today is proud to announce the completion of a contract that will see the company recognized as the world's first “Approved Maltego Solution Provider” (AMSP) and the exclusive provider of this kind in the UK and Southern Africa.
SensePost was founded in 2000 and has developed into one of the worlds leading Information Security Services companies with offices in London, Cape Town and Pretoria. As trusted advisors it has always been our mission to provide our customers with insight, information and systems to enable them to make strong decisions about Information Security that support their business performance. Whilst this mission has traditionally expressed itself in technical security analysis services like Vulnerability Assessment and Penetration Testing we recognise that the threat landscape is constantly changing and that new and more complex realities necessitate the use of sophisticated new skills, tools and techniques with which to support our clients.
“This strategic alliance perfectly fits the ‘Assess-Detect-Protect-Respond' framework that drives the way we design, sell and deliver our service. It's the perfect evolution of our growing services offering.” says Etienne Greef, CEO of the SensePost group holding company SecureData, who's strategy is at the core of this new initiative.
‘Maltego', built by Paterva, is a powerful suite of software tools used for data mining, link analysis and data visualization, giving the user the ability to extract large volumes of data from diverse sources and then analyze it to understand the patterns and relationships it reveals. In the modern digital age these techniques are used to convert data into information and thereby extract concrete value that can be used for effective decision-making.
Maltego is a highly regarded and popular platform used extensively in Open Source Intelligence Gathering, Infrastructure Analysis for Penetration Testing, Cyber Attack Analysis, Fraud Detection and Investigation, Security Intelligence, Information Security Management, Research and more.
This partnership between SensePost and Paterva (who produce the Maltego software) builds on the companies' shared roots and intellectual heritage and will allow both companies to serve their customers and fulfil their respective missions better.
As an AMSP SensePost will be authorised to provide integration, consulting, support and training for the Maltego tools with full endorsement, support and assistance directly from Paterva. This new capability, combined with an existing wealth of information security skills and experience, uniquely positions SensePost to advise and support clients seeking to exploit the unique strategic advantage the Maltego toolset can offer.
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.
Wireless hacking, you say?
You may think wireless hacking is nothing new, and you may think it's just not that relevant or exciting. Come along to our BlackHat Wireless Bootcamp course and we'll show you different! We'll teach you the fundamentals every wireless hacker needs to know, but then move onto the really exciting, cutting edge stuff.
Cutting edge WiFi hacking, you say?
At SensePost we really enjoy wireless hacking - mostly because it gets us good results in terms of compromising our targets! With our years of experience in this area we've written our own tools, as well as refined others. In this course we'll reveal new techniques and tools (can you smell 0day?) that we'll hopefully be presenting at the conference, and give you exclusive hands on training with our very own Snoopy framework (a distributed, tracking, data interception, and profiling framework). Two lucky students who capture our CTFs will also go home with pre-built Snoopy drone. Every student will also get their own Alfa WiFi card to take home, as well as the latest Snoopy pre-release (Snoopy will run fine on your laptop too).
Here's an exact break down of what to expect from this course:
• Wi-Fi theory and background
• Breaking WEP
• Breaking WPA PSK
• Man in the middle attacks for WPA MGT (new attack vectors)
• Breaking WPS
• Wi-Fi Router back doors
• Rogue Access Points attack scenarios (new attack vectors)
• Exclusive Snoopy training
Who should attend?
Anyone interested in WiFi security. The course is relevant for both attackers and defenders (it'll let you put your defense into context). Students should have some technical ability in Linux, and understand networking fundamentals, but this is a bootcamp level course.
Dominic (@singe) and Glenn (@glennzw) will be your instructors. They're both avid wireless hackers, and never leave home without a high gain antenna and an Alfa card! They're looking forward to training you. You can find the sign-up page here.
-Glenn & Dominic
Botconf'13, the "First botnet fighting conference" took place in Nantes, France from 5-6 December 2013. Botconf aimed to bring together the anti-botnet community, including law enforcement, ISPs and researchers. To this end the conference was a huge success, especially since a lot of networking occurred over the lunch and tea breaks as well as the numerous social events organised by Botconf.
I was fortunate enough to attend as a speaker and to present a small part of my Masters research. The talk focused the use of Spatial Statistics to detect Fast-Flux botnet Command and Control (C2) domains based on the geographic location of the C2 servers. This research aimed to find novel techniques that would allow for accurate and lightweight classifiers to detect Fast-Flux domains. Using DNS query responses it was possible to identify Fast-Flux domains based on values such as the TTL, number of A records and different ASNs. In an attempt to increase the accuracy of this classifier, additional analysis was performed and it was observed that Fast-Flux domains tended to have numerous C2 servers widely dispersed geographically. Through the use of the statistical methods employed in plant and animal dispersion statistics, namely Moran's I and Geary's C, new classifiers were created. It was shown that these classifiers could detect Fast-Flux domains with up to a 97% accuracy, maintaining a False Positive rate of only 3.25% and a True Positive rate of 99%. Furthermore, it was shown that the use of these classifiers would not significantly impact current network performance and would not require changes to current network architecture.
The scripts used to conduct the research are available on github and are in the process of being updated (being made human readable): https://github.com/staaldraad/fastfluxanalysis
The following blogs provide a comprehensive round-up of the conference including summaries of the talks: