When performing spear phishing attacks, the more information you have at your disposal, the better. One tactic we thought useful was this Skype security flaw disclosed in the early days of 2012 (discovered by one of the Skype engineers much earlier).

For those who haven't heard of it - this vulnerability allows an attacker to passively disclose victims external, as well as internal, IP addresses in a matter of seconds, by viewing the victims VCard through an 'Add Contact' form.

Why is this useful?

1. Verifying the identity and the location of the target contact. Great when performing geo-targeted phishing attacks.

2. Checking whether your Skype account has not been used elsewhere :)

3. Spear phishing enumeration while Pen Testing.

4. Just out of plain curiosity.

To get this working, following these basic steps:

1. Download and install the patched version of Skype 5.5 from here (the patch enables the Skype client to save the logs in non obfuscated form)

2. Save the lines below as a Skype_log_patch.reg reg file:

Windows Registry Editor Version 5.00

[HKEY_CURRENT_USER\Software\Skype\Phone\UI\General]

"LastLanguage"="en"

"Logging"="SkypeDebug2003"

"Logging2"="on"

Once saved, run it to enable the Skype Debug Log File.

4. Start Skype.

5. Search for any Skype contact and click on the 'Add a Skype Contact' button, but do not send the request, rather click on the user to view their VCard.

4. Open the log file (it should appear in the same folder as Skype executable e.g. debug-20121003-0150)

5. Look for the PresenceManager line - you should see something similar to this - >

In the above image you can spot my Skype name, external as well as internal IP addresses.

The log will include similar credentilas for everyone listed as a "contact" under your Skype account, as well as many other fresh, genuine and useful information received directly from your local Skype tracker.

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 :-)

As 44Con 2012 starts to gain momentum (we'll be there again this time around) I was perusing some of the talks from last year's event...

It was a great event with some great presentations, including (if I may say) our own Ian deVilliers' *Security Application Proxy Pwnage*. Another presentation that caught my attention was Haroon Meer's *Penetration Testing considered harmful today*. In this presentation Haroon outlines concerns he has with Penetration Testing and suggests some changes that could be made to the way we test in order to improve the results we get. As you may know a core part of SensePost's business, and my career for almost 13 years, has been security testing, and so I followed this talk quite closely. The raises some interesting ideas and I felt I'd like to comment on some of the points he was making.

As I understood it, the talk's hypothesis could be (over) simplified as follows:

1. Despite all efforts the security problem is growing and we're heading towards a 'security apocalypse';
2. Penetration Testing has been presented as a solution to this problem;
3. Penetration Testing doesn't seem to be working - we're still just one 0-day away from being owned - even for our most valuable assets;
4. One of the reasons for this is that we don't cater for the 0-day, which is a game-changer. 0-day is sometimes overemphasized, but mostly it's underemphasized, making the value of the test spurious at best;
5. There are some ways in which this can be improved, including the use '0-day cards', which allow the tester to emulate the use of a 0-day on a specific system without needing to actually have one. Think of this like a joker in a game of cards.

To begin with, let's consider the term "Penetration Testing", which sits at the core of the hypotheses. This term is widely used to express a number of security testing methodologies and could also be referred to as "attack & penetration", "ethical hacking", "vulnerability testing" or "vulnerability assessment". At SensePost we use the latter term, and the methodology it expresses includes a number of phases of which 'penetration testing' - the attempt to actually leverage the vulnerabilities discovered and practically demonstrate their potential impact to the business - is only one. The talk did not specify which specific definition of Penetration Test he was using. However, given the emphasis later in the talk about the significance of the 0-day and 'owning' things, I'm assuming he was using the most narrow, technical form of the term. It would seem to me that this already impacts much of his assertion: There are cases of course where a customer wants us simply to 'own' something, or somethings, but most often Penetration Testing is performed within the context of some broader assessment within which many of Haroon's concerns may already be being addressed. As the talk pointed out, there are instances where the question is asked "can we breached?", or "can we be breached without detecting it?". In such cases a raw "attack and penetration" test can be exactly what's needed; indeed it's a model that's been used by the military for decades. However for the most part penetration testing should only be used as a specific phase in an assessment and to achieve a specific purpose. I believe many services companies, including our own, have already evolved to the point where this is the case.

Next, I'd like to consider the assertion that penetration testing or even security assessment is presented as the "solution" to the security problem. While it's true that many companies do employ regular testing, amongst our customers it's most often used as a part of a broader strategy, to achieve a specific purpose. Security Assessment is about learning. Through regular testing, the tester, the assessment team and the customer incrementally understand threats and defenses better. Assumptions and assertions are tested and impacts are demonstrated. To me the talk's point is like saying that cholesterol testing is being presented as a solution to heart attacks. This seems untrue. Medical testing for a specific condition helps us gauge the likelihood of someone falling victim to a disease. Having understood this, we can apply treatments, change behavior or accept the odds and carry on. Where we have made changes, further testing helps us gauge whether those changes were successful or not. In the same way, security testing delivers a data point that can be used as part of a general security management process. I don't believe many people are presenting testing as the 'solution' to the security problem.

It is fair to say that the entire process within which security testing functions is not having the desired effect; Hence the talk's reference to a "security apocalypse". The failure of security testers to communicate the severity of the situation in language that business can understand surely plays a role here. However, it's not clear to me that the core of this problem lies with the testing component.

A significant, and interesting component of the talk's thesis has to do with the role of "0-day" in security and testing. He rightly points out that even a single 0-day in the hands of an attacker can completely change the result of the test and therefore the situation for the attacker. He suggests in his talk that the testing teams who do have 0-day are inclined to over-emphasise those that they have, whilst those who don't have tend to underemphasize or ignore their impact completely. Reading a bit into what he was saying, you can see the 0-day as a joker in a game of cards. You can play a great game with a great hand but if your opponent has a joker he's going to smoke you every time. In this the assertion is completely true. The talk goes on to suggest that testers should be granted "0-day cards", which they can "play" from time to time to be granted access to a particular system and thereby to illustrate more realistically the impact a 0-day can have. I like this idea very much and I'd like to investigate incorporating it into the penetration testing phase for some of our own assessments.

What I struggle to understand however, is why the talk emphasizes the particular 'joker' over a number of others that seems apparent to me. For example, why not have a "malicious system administrator card", a "spear phishing card", a "backdoor in OTS software" card or a "compromise of upstream provider" card? As the 'compromise' of major UK sites like the Register and the Daily Telegraph illustrate there are many factors that could significantly alter the result of an attack but that would typically fall outside the scope of a traditional penetration test. These are attack vectors that fall within the victim's threat model but are often outside of their reasonable control. Their existence is typically not dealt with during penetration testing, or even assessment, but also cannot be ignored. This doesn't doesn't invalidate penetration testing itself, it simply illustrates that testing is not equal to risk management and that risk management also needs to consider factors beyond the client's direct control.

The solution to this conundrum was touched on in the presentation, albeit very briefly, and it's "Threat Modeling". For the last five years I've been arguing that system- or enterprise-wide Threat Modeling presents us with the ability to deal with all these unknown factors (and more) and perform technical testing in a manner that's both broader and more efficient.

The core of the approach I'm proposing is roughly based on the Microsoft methodology and looks as follows:

1. Develop a model of your target environment, incorporating all players, locations, and interfaces. This is done in close collaboration between the client and the tester, thus incorporating both the 'insider' and the 'outsider' perspective;
2. Enumerate all potential risks, and map them to the model. This results in a very long and comprehensive list of hypothetical risks, which would naturally include the 0-day, but also all the other 'jokers' that we discussed above;
3. Sort the list into some order of priority and group similar hypothetical risks together;
4. Perform tests in order of priority where appropriate to prove or disprove the hypothetical risks;
5. Remediate, mitigate, insure or inform as appropriate;
6. Rinse and repeat.

This approach provides a reasonable balance between solid theoretical risk management and aggressive technical testing that addresses all the concerns raised in the talk about the way penetration testing is done today. It also provides the customer with a concrete register of tested risks that can easily be updated from time-to-time and makes sense to both technical and business leaders.

Threat Modeling makes our testing smarter, broader, more efficient and more relevant and as such is a vital improvement to our risk assessment methodology.

Solving the security problem in total is sadly still going to take a whole lot more work...

By the year 2015 sub-Saharan Africa will have more people with mobile network access than with access to electricity at home.

This remarkable fact from a 2011 MobileMonday report [1] came to mind again as I read an article just yesterday about the introduction of Mobile Money in the UK: By the start of next year, every bank customer in the country may have the ability to transfer cash between bank accounts, using an app on their mobile phone. [2]

I originally came across the MobileMonday report while researching the question of mobility and security in Africa for a conference I was asked to present at [3]. In this presentation I examine the global growth and impact of the so-called mobile revolution and then its relevance to Africa, before looking at some of the potential security implications this revolution will have.

The bit about the mobile revolution is easy: According to the Economist there will be 10 billion mobile devices connected to the Internet by 2020, and the number of mobile devices will surpass the number of PCs and laptops by this year already. The mobile-only Internet population will grow 56-fold from 14 million at the end of 2010 to 788 million by the end of 2015. Consumerization - the trend for new information technology to emerge first in the consumer market and then spread into business organizations, resulting in the convergence of the IT and consumer electronics industries - implies that the end-user is defining the roadmap for these technologies as manufacturers, networks and businesses scramble desperately to absorb their impact.

Africa, languishing behind in so many other respects, is right there on the rushing face of this new wave, as my initial quote illustrates. In fact the kind of mobile payment technology referred to in the BBC article is already quite prevalent in our home markets in Africa and we're frequently engaged to test mobile application security in various forms. In my presentation for example, I make reference to m-Pesa - the mobile payments system launched in Kenya and now mimicked in South Africa also. Six million people in Kenya use m-Pesa, and more than 5% of that country's annual GDP is moved to and fro directly from mobile to mobile. There are nearly five times the number of m-Pesa outlets than the total number of PostBank branches, post offices, bank branches, and automated teller machines (ATMs) in the country combined.

Closer to home in South Africa, it is estimated that the number of people with mobile phones outstrips the number of people with fixed-line Internet connections by a factor of ten! And this impacts our clients and their businesses directly: Approximately 44% of urban cellphone users in South Africa now make use of mobile banking services. The reasoning is clear: Where fixed infrastructure is poor mobile will dominate, and where the mobile dominates mobile services will soon follow. Mobile banking, mobile wallets, mobile TV and mobile social networking and mobile strong-authentication systems are all already prevalent here in South Africa and are already bringing with them the expected new array of security challenges. Understanding this is one of the reasons our customers come to us.

In my presentation I describe the Mobile Threat Model as having three key facets:

• Security: The challenge of ensuring Confidentiality, Integrity and Authenticity for the data and transactions on the device;
• Privacy: The implications of mobility (and especially convergence) for citizens and their rights to talk, move, think and act unobserved; and
• Control: The challenge presented by the mobile revolution to governments fighting crime, gangsterism and terrorism.

All of these issues are real and complex, but I'm restricting myself to the security question here. I encourage readers to peruse the presentation itself for a full breakdown of the Threat Model because for this article I think it suffices to consider just the conclusion of my presentation, and it's this:

The technical security issues we discover on mobile devices and mobile applications today are really no different from what we've been finding in other environments for years. There are some interesting new variations and interesting new attack vectors, but it's really just a new flavor of the same thing. But there are four attributes of the modern mobile landscape that combine to present us with an entirely new challenge:

Firstly, mobiles are highly connected. The mobile phone is permanently on some IP network and by extension permanently on the Internet. However, it's also connected via GSM and CDMA; it's connected with your PC via USB, your Bluetooth headset and your GPS, and soon it will be connected with other devices in your vicinity via NFC. Never before in our history have communications been so converged, and all via the wallet-sized device in your pocket right now!

Secondly, the mobile device is deeply integrated. On or through this platform is everything anyone would ever want to know about you: Your location, your phone calls, your messages, your personal data, your photos, your location, your location history and your entire social network. Indeed, in an increasing number of technical paradigms, your mobile device is you! Moreover, the device has the ability to collect, store and transmit everything you say, see and hear, and everywhere you go!

Thirdly, as I've pointed out, mobile devices are incredibly widely distributed. Basically, everyone has one or soon will. And, we're rapidly steering towards a homogenous environment defined by IOS and Google's Android. Imagine the effect this has on the value of an exploit or attack vector. Finally, the mobile landscape is still being very, very poorly managed. Except for the Apple AppStore, and recent advances by Google to manage the Android market, there is extremely little by way of standardization, automated patching or central management to be seen. Most devices, once deployed, will stay in commission for years to come and so security mistakes being made now are likely to become a nightmare for us in the future.

Thus, the technical issues well known from years of security testing in traditional environments are destined to prevail in mobile, and we're already seeing this in the environments we've tested. This reality, combined with how connected, integrated, distributed and poorly managed these platforms are, suggests that careless decisions today could cost us very dearly in the future...

[1] Mobile Africa Report 2011, Regional Hubs of Excellence and Innovation by Dr Madanmohan Rao, Research Project Director, MobileMonday March 2011

[2] http://www.bbc.co.uk/news/business-17115946

[3] http://prezi.com/as-szhrug5zr/examining-the-impact-of-the-adoption-of-mobile-devices-throughout-africa-and-the-subsequent-rise-of-security-related-risks-sensepost-information-security/

Last week we presented an invited talk at the ISSA conference on the topic of online privacy (embedded below, click through to SlideShare for the original PDF.)

The talk is an introductory overview of Privacy from a Security perspective and was prompted by discussions between security & privacy people along the line of "Isn't Privacy just directed Security? Privacy is to private info what PCI is to card info?" It was further prompted by discussion with Joe the Plumber along the lines of "Privacy is dead!"

The talk, is unfortunately best delivered as a talk, and not as standalone slides, so here's some commentary:

We start off the problem statement describing why privacy has grown in importance. The initial reactions were based on new technology allowing new types of information to be captured and disseminated. While the example given is from the 1980s, the reaction is a recurring one, as we've seen with each release of new tech (some examples: Cameras, Newspapers, Credit Cards, The Internet, Facebook). Reactions are worsened by the existence of actors with the funding & gall to collect and collate much information to further potentially disagreeable goals (usually Governments). However, the new threat is that there has been a fundamental shift in the way in which we live our lives, where information about us is no longer merely *recorded* online, but rather, our lives are *lived* on line. It is quite possible that for an average day, from waking up to going to sleep, a significant number of the actions you perform will not only be conducted (in part) online, but that it is possible for them to be conducted using the services of one service provider. My intention is not to beat up on Google, but rather use them as an example. They are a pertinent example, as every business book seems to use them as one. The, arguably, most successful corporation of our current age's primary business model is the collection & monetisation of private data. Thus, while Google is the example, there are and will be many followers.

The next section moves into providing a definition of privacy, and attempts to fly through some fairly dry aspects of philosophy, law & psychology. We've done some entry-level work on collating the conception of privacy across history and these fields, however, brighter minds, such as Daniel Solove and Kamil Reddy have done better jobs of this. In particular, Solove's paper "I've got nothing to hide", and other misconception of privacy is a good introductory read. The key derived point however, is that private data is data with an implied access control & authorised use. Which of the implied access controls & authorised uses are reasonable to enforce or can be legally enforced is a developing field.

As the talk is about "Online Privacy" the talk moves into a description of the various levels at which private data is collected, what mechanisms are used to attempt to collect that data, and what sort of data can be gleaned. It was an academic conference, so I threw in the word "taxonomy." Soon, it will be more frequently quoted than Maslow's Hierarchy, any day now.

At each level, a brief demonstration of non-obvious leaks and their implications was demonstrated. From simple techniques such as cross-site tracking using tracking pixels or cookies, to exploit of rich browser environments such as the simple CSS history hack, to less structured and less obvious leaks such as search data (as demonstrated by the AOL leak), moving to deanonymisation of an individual by correlating public data sets (using the awesome Maltego) and finally to unintended leaks provided by meta-data (through analysis of twitter & facebook friends groups).

Finally, a mere two slides are used to explain some of the implications and defenses. These are incomplete and are the current area of research I'm engaged in.