Category: Mobile

MAMADROID: Detecting Android Malware by Building Markov Chains of Behavioral Models

Now making up 85% of mobile devices, Android smartphones have become profitable targets for cybercriminals, allowing them to bypass two factor authentication or steal sensitive information such as credit cards details or login credentials.

Smartphones have limited battery and memory available, therefore, the defences that can be deployed on them have limitations. For these reasons, malware detection is usually performed in a centralised fashion by Android market operators. As previous work and even recent news have shown, however, even Google Play Store is not able to detect all malicious apps; to make things even worse, there are countries in which Google Play Store is blocked. This forces users to resort to third party markets, which are usually performing less careful malware checks.

Previous malware detection studies focused on models based on permissions or on specific API calls. While the first method is prone to false positives, the latter needs constant retraining, because apps as well as the Android framework itself are constantly changing.

Our intuition is that, while malicious and benign apps may call the same API calls during their execution, the reason why those calls are made may be different, resulting in them being called in a different order. For this reason, we decided to rely on sequences of calls that, as explained later, we abstract to higher level for performance, feasibility, and robustness reasons. To implement this idea we created MaMaDroid, a system for Android malware detection.

MaMaDroid

MaMaDroid is built by combining four different phases:

  • Call graph extraction: starting from the apk file of an app, we extract the call graph of the analysed sample.
  • Sequence extraction: from the call graph, we extract the different potential paths as sequences of API calls and abstract all those calls to higher levels.
  • Markov Chain modelling: all the samples got their sequences of abstracted calls, and these sequences can be modelled as transitions among states of a Markov Chain.
  • Classification: Given the probabilities of transition between states of the chains as features set, we apply machine learning to detect malicious apps.
Four phases of MaMaDroid

Call graph extraction

MaMaDroid is a system based only on static analysis. To analyse the app, we use off-the-shelf tools, such as Soot and FlowDroid for the first step of the system.

Sequence Extraction

Taking the call graph as input, we extract the sequences of functions potentially called by the program and, by identifying the set of entry nodes, enumerate all the possible paths and output them as sequences of API calls.

Example call graph in which we can observe 3 different potential paths, or sequences, starting from the root node

Continue reading MAMADROID: Detecting Android Malware by Building Markov Chains of Behavioral Models

Battery Status Not Included: Assessing Privacy in W3C Web Standards

Designing standards with privacy in mind should be a standard in itself. Historically this was not always the case and the idea of designing systems with privacy is relatively new – it dates from the beginning of this decade. One of the milestones is accepting this view on the IETF level, dating 2013.

This note and the referenced research work focusses on designing standards with privacy in mind. The World Wide Web Consortium (W3C) is one of the most important standardization bodies. W3C is standardizing something everyone – billions of people – use every day for entertainment or work, the web and how web browsers work. It employs a focused, lengthy, but very open and consensus-driven environment in order to make sure that community voice is always heard during the drafting of new specifications. The actual inner workings of W3C are surprisingly complex. One interesting observation is that the W3C Process Document document does not actually mention privacy reviews at all. Although in practice this kind of reviews are always the case.

However, as the web along with its complexity and web browser features constantly grow, and as browsers and the web are expected to be designed in an ever-rapid manner, considering privacy becomes a challenge. But this is what is needed in order to obtain a good specification and well-thought browser features, with good threat models, and well designed privacy strategies.

My recent work, together with the Princeton team, Arvind Narayanan and Steven Englehardt, is providing insight into the standardization process at W3C, specifically the privacy areas of standardization. We point out a number of issues and room for improvements. We also provide recommendations as well as broad evidence of a wide misuse and abuse (in tracking and fingerprinting) of a browser mechanism called Battery Status API.

Battery Status API is a browser feature that was meant to allow websites access the information concerning the battery state of a user device. This seemingly innocuous mechanism initially had no identified privacy concerns. However, following my previous research work in collaboration with Gunes Acar this view has changed (Leaking Battery and a later note).

The work I authored in 2015 identifies a number or privacy risks of the API. Specifically – the issue of potential exploitation of the API to tracking, as well as the potential possibility of recovering the raw value of the battery capacity – something that was not foreseen to be accessed by web sites. Ultimately, this work has led to a fix in Firefox browser and an update to W3C specification. But the matter was not over yet.

Continue reading Battery Status Not Included: Assessing Privacy in W3C Web Standards

On the security and privacy of the ultrasound tracking ecosystem

In April 2016, the US Federal Trade Commission (FTC) sent warning letters to 12 Google Play app developers. The letters were addressed to those who incorporated the SilverPush framework in their apps, and reminded developers who used tracking software to explicitly inform their users (as seen in Section 5 of the Federal Trade Commission Act). The incident was covered by popular press and privacy concerns were raised. Shortly after, SilverPush claimed no active partnerships in the US and the buzz subsided.

Unfortunately, as the incident was resolved relatively fast, very few technical details of the technology were made public. To fill in this gap and understand the potential security implications, we conducted an in-depth study of the SilverPush framework and all the associated technologies.

The development of the framework was motivated by a fast-increasing interest of the marketing industry in products performing high-accuracy user tracking, and their derivative monetization schemes. This resulted in a high demand for cross-device tracking techniques with increased accuracy and reduced prerequisites.

The SilverPush framework fulfilled both of these requirements, as it provided a novel way to track users between their devices (e.g., TV, smartphone), without any user actions (e.g., login to a single platform from all their devices). To achieve that, the framework realized a previously unseen cross-device tracking technique (i.e., ultrasound cross-device tracking, uXDT) that offered high tracking accuracy, and came with various desirable features (e.g., easy to deploy, imperceptible by users). What differentiated that framework from existing ones was the use of high-frequency, inaudible ultrasonic beacons (uBeacons) as a medium/channel for identifier transmission between the user’s devices. This is also offered a major advantage to uXDT, against other competing technologies, as uBeacons can be emitted by most commercial speakers and captured by most commercial microphones. This eliminates the need for specialized emission and/or capturing equipment.

Aspects of a little-known ecosystem

The low deployment cost of the technology fueled the growth of a whole ecosystem of frameworks and applications that use uBeacons for various purposes, such as proximity marketing, audience analytics, and device pairing. The ecosystem is built around the near-ultrasonic transmission channel, and enables marketers to profile users.

Unfortunately, users are often given limited information on the ecosystem’s inner workings. This lack of transparency has been the target of great criticism from the users, the security community and the regulators. Moreover, our security analysis revealed a false assumption in the uBeacon threat model that can be exploited by state-level adversaries to launch complex attacks, including one that de-anonymizes the users of anonymity networks (e.g., Tor).

On top of these, a more fundamental shortcoming of the ecosystem is the violation of the least privilege principle, as a consequence of the access to the device’s microphone. More specifically, any app that wants to employ ultrasound-based mechanisms needs to gain full access to the device’s microphone, as there is currently no way to gain access only to the ultrasound spectrum. This clearly violates the least privilege principle, as the app has now access to all audible frequencies and allows a potentially malicious developer to request access to the microphone for ultrasound-pairing purposes, and then use it to eavesdrop the user’s discussions. This also results in any ultrasound-enabled apps to risk being perceived as “potentially malicious” by the users.

Mitigation

To address these shortcomings, we developed a set of countermeasures aiming to provide protection to the users in the short and medium term. The first one is an extension for the Google Chrome browser, which filters out all ultrasounds from the audio output of the websites loaded by the user. The extension actively prevents web pages from emitting inaudible sounds, and thus completely thwarts any unsolicited ultrasound-tracking attempts. Furthermore, we developed a patch for the Android permission system that allows finer-grained control over the audio channel, and forces applications to declare their intention to capture sound from the inaudible spectrum. This will properly separate the permissions for listening to audible sound and sound in the high-frequency spectrum, and will enable the end users to selectively filter the ultrasound frequencies out of the signal acquired by the smartphone microphone.

More importantly, we argue that the ultrasound ecosystem can be made secure only with the standardization of the ultrasound beacon format. During this process, the threat model will be revised and the necessary security features for uBeacons will be specified. Once this process is completed, APIs for handling uBeacons can be implemented in all major operating systems. Such an API would provide methods for uBeacon discovery, processing, generation and emission, similar to those found in the Bluetooth Low Energy APIs. Thereafter, all ultrasound-enabled apps will need access only to this API, and not to the device’s microphone. Thus, solving the problem of over-privileging that exposed the user’s sensitive data to third-party apps.

Discussion

Our work provides an early warning on the risks looming in the ultrasound ecosystem, and lays the foundations for the secure use of this set of technologies. However, it also raises several questions regarding the security of the audio channel. For instance, in a recent incident a journalist accidentally injected commands to several amazon echo devices, which then allegedly tried to order products online. This underlines the need for security features in the audio channel. Unfortunately, due to the variety of use cases, a universal solution that could be applied to the lower communication layers seems unlikely. Instead, solutions must be sought in the higher communications layers (e.g., application layers), and should be the outcome of careful threat modeling.

Privacy analysis of the W3C Proximity Sensor specification

Mobile developers are familiar with proximity sensors. These provide information about the distance of an object from the device providing access to the sensor (i.e. smartphone, tablet, laptop, or another Web of Things device). This object is usually the user’s head or hand.

Proximity sensors provide binary values such as far (from the object) or near (respectively), or a more verbose readout, in centimeters.

There are many useful applications for proximity. For example, an application can turn off the screen if the user holds the device close to his/her head (face detection). It is also handy for avoiding the execution of undesired actions, possibly arising from scratching the head with the phone’s screen.

The Proximity Sensor API specification is being standardized by W3C. Every web site will be able to access this information. Let’s focus on the privacy engineering point of view.

Proximity is the distance between an object and device. The latest version of W3C Proximity Sensors API takes advantage of a soon-to-be standard Generic Sensors API. The sensor provides the proximity distance in centimeters.

The use of Proximity Sensors is simple; an example displaying the current distance in centimeters using the modern syntax of Generic Sensors API is as follows:

let sensor = new ProximitySensor();

sensor.start();

sensor.onchange = function(){console.log(event.reading.distance)};

This syntax might later allow requesting various proximity sensors (if the device has more than one), including those based on the sensor’s position (such as “rear” or “front”), but the current implementations generally still use the legacy version from the previous edition of the spec:

window.addEventListener('deviceproximity', function(event) {  
console.log(event.value)  
});

From the perspective of privacy considerations, there is currently no significant difference between those two API versions.

Privacy analysis of Proximity Sensor API

Proximity sensor readout is not providing much data, so it may appear there are no privacy implications, but there are good reasons for performing such analysis. Let’s list two:

Firstly, designing new standards and systems with privacy in mind — privacy by design — is a required practice and a good idea. Secondly, in some circumstances even potentially insignificant mechanisms can still bring consequences from privacy point of view. For example, multiple identifiers might be helpful in deanonymizing users. Non-obvious data leaks can surface, as well.

So let’s discuss some of the possible issues.

The average distance from the device to the user’s face (i.e. object) could be used to differentiate and discriminate between users. While the severity is hard to establish, future changes (i.e. influencing with other external data) cannot be ruled out.

If proximity patterns would be individually-attributable, this would offer a possibility to enhance user profiling based on the analysis of device use patterns. For example, the following could be obtained and analyzed:

  • Frequency of the user’s patterns of device use (i.e. waving a hand in front of the proximity sensor)
  • Frequency of the user’s zooming in and out (i.e. device close to the user’s head)
  • Patterns of use. Does the way the user hold the device vary during the day? How?
  • What are the mechanics of holding the device close to the user’s head? Can the distance vary?

Some users may use the mobile operating system’s zoom capability to increase the font or images, but others might casually prefer to hold the device closer to their eyes. Such behavioral differences can also be of note.

Proximity sensors can provide the following data: the current proximity distance and max, the maximum distance readout supported by the device. In case the value of max would differ between various implementations (e.g. among browsers, devices), it could form an identifier. And the two (max, distance) combined could also be used as short-lived identifiers. Moreover, it is not so difficult to imagine a situation where those identifiers are actually not so short-lived.

Recommendations

First of all, is there a need to provide a verbose proximity readout at all? For example, is providing readouts of proximity (distance) value up to 150 cm necessary?

In general, a device (browser, a Web of Things device) should be capable of informing the user when a web site accesses proximity information. The user should also be able to inspect which web sites – and how frequently – accessed the API.

Finally, the sensors should provide an adequately verbose readout of the distance.

Proximity Sensors should also be subject to permissions.

Demonstration

At the moment, the implementations of proximity sensors are limited. But a demonstration is running on my SensorsPrivacy research project (tested against Firefox Mobile on Android). In my case (Nexus 5), Firefox offered data in centimeters, but the verbosity was limited to 5 centimeters on my device. Again, this is a matter of hardware and software. In principle, the accuracy could be much higher and one can imagine an accuracy a sensor providing more accurate readouts (even up to 1 meter).

The screen dump below shows how the example demonstrates the use of proximity data (and whether it works on a browser). The site changes its background color if an object is placed near the proximity sensor of a device.

The readout from the demonstration shows a relative time between events (first column), and the proximity distance (second column).

398  5  
1011 0  
404  5  
607  0

The sensor on my device is reporting two values of distance: 0 (near) and 5 (far), in centimeters. In general, the granularity is subject to the following: standard, implementation and hardware.

And this suggests something, of course. So let’s complement the privacy analysis from the previous section to incorporate a timing analysis.

Even such a limited readout can help performing behavioural analysis, simply by profiling based on time series. As we can see, the sensor readout is quite sensitive in respect to time and can measure with sub-200 ms granularity. The demonstration proof of principle is also showing the minimum relative detected time between events. Feel free to test the performance of your fingers and/or hardware!

Summary

When designing a standard project or an implementation, paying attention to details is imperative. This includes consideration of even potential risks. This is especially the case if the software or systems will be used by millions or hundreds of millions of users, i.e. you are aiming for success.

Finally, standards and specifications are ideal for issuing guidance and good practices

 

This post originally appeared on Security, Privacy & Tech Inquiries, the blog of Lukasz Olejnik. An accompanying demonstration is available on SensorsPrivacy (a project studing privacy of web sensors).

Battery Status readout as a privacy risk

Privacy risks and threats arise even in seemingly innocuous mechanisms. It is a fairly regular issue.

Over a year ago, I was researching the risk of the W3C Battery Status API. The mechanism allows a web site to read the battery level of a device (smartphone, laptop, etc.). One of the positive use cases may be, for example, stopping the execution of intensive operations if the battery is running low.

Our privacy analysis of Battery Status API revealed interesting results.

Privacy analysis of Battery API

The battery status provides the following information:

  • the current level of battery (format: 0.0–1.0, for empty and full battery, respectively)
  • time to a full discharge of battery (in seconds)
  • time to a full charge of battery, if connected to a charger (in seconds)

These items are updated whenever a new value is supplied by the operating system

It turns out that privacy risks may surface even in this kind of – seemingly innocuous – data and access mechanisms.

Frequency of changes

The frequency of changes in the reported readouts from Battery Status API potentially allow the monitoring of users’ computer use habits; for example, potentially enabling analyzing of how frequently the user’s device is under heavy use. This could lead to behavioral analysis.

Additionally, identical installations of computer deployments in standard environments (e.g. at schools, work offices, etc.) are often are behind a NAT. In simple terms, NAT allows a number of users to browse the Internet with an – externally seen – single IP address. The ability of observing any differences between otherwise identical computer installations – potentially allows particular users to be identified (and targeted?).

Battery readouts as identifiers

The information provided by the Battery Status API is not always subject to rapid changes. In other words, this information may be static for a period of time; this in turn may give rise to a short-lived identifier. The situation gets especially interesting when we consider a scenario of users sometimes clearing standard web identifiers (such as cookies). In such a case, a web script could potentially analyse identifiers provided by Battery Status API, and this information then could possibly even lead to re-creation of other identifiers. A simple sketch follows.

Continue reading Battery Status readout as a privacy risk

Do you know what you’re paying for? How contactless cards are still vulnerable to relay attack

Contactless card payments are fast and convenient, but convenience comes at a price: they are vulnerable to fraud. Some of these vulnerabilities are unique to contactless payment cards, and others are shared with the Chip and PIN cards – those that must be plugged into a card reader – upon which they’re based. Both are vulnerable to what’s called a relay attack. The risk for contactless cards, however, is far higher because no PIN number is required to complete the transaction. Consequently, the card payments industry has been working on ways to solve this problem.

The relay attack is also known as the “chess grandmaster attack”, by analogy to the ruse in which someone who doesn’t know how to play chess can beat an expert: the player simultaneously challenges two grandmasters to an online game of chess, and uses the moves chosen by the first grandmaster in the game against the second grandmaster, and vice versa. By relaying the opponents’ moves between the games, the player appears to be a formidable opponent to both grandmasters, and will win (or at least force a draw) in one match.

Similarly, in a relay attack the fraudster’s fake card doesn’t know how to respond properly to the payment terminal because, unlike a genuine card, it doesn’t contain the cryptographic key known only to the card and the bank that verifies the card is genuine. But like the fake chess grandmaster, the fraudster can relay the communication of the genuine card in place of the fake card.

For example, the victim’s card (Alice, in the diagram below) would be in a fake or hacked card payment terminal (Bob) and the criminal would use the fake card (Carol) to attempt a purchase in a genuine terminal (Dave). The bank would challenge the fake card to prove its identity, this challenge is then relayed to the genuine card in the hacked terminal, and the genuine card’s response is relayed back on behalf of the fake card to the bank for verification. The end result is that the terminal used for the real purchase sees the fake card as genuine, and the victim later finds an unexpected and expensive purchase on their statement.

A rigged payment terminal capable of performing the relay attack can be made from off-the-shelf components
The relay attack, where the cards and terminals can be at any distance from each other

Demonstrating the grandmaster attack

I first demonstrated that this vulnerability was real with my colleague Saar Drimer at Cambridge, showing on television how the attack could work in Britain in 2007 and in the Netherlands in 2009.

In our scenario, the victim put their card in a fake terminal thinking they were buying a coffee when in fact their card details were relayed by a radio link to another shop, where the criminal used a fake card to buy something far more expensive. The fake terminal showed the victim only the price of a cup of coffee, but when the bank statement arrives later the victim has an unpleasant surprise.

At the time, the banking industry agreed that the vulnerability was real, but argued that as it was difficult to carry out in practice it was not a serious risk. It’s true that, to avoid suspicion, the fraudulent purchase must take place within a few tens of seconds of the victim putting their card into the fake terminal. But this restriction only applies to the Chip and PIN contact cards available at the time. The same vulnerability applies to today’s contactless cards, only now the fraudster need only be physically near the victim at the time – contactless cards can communicate at a distance, even while the card is in the victim’s pocket or bag.

Continue reading Do you know what you’re paying for? How contactless cards are still vulnerable to relay attack

Analyzing privacy aspects of the W3C Vibration API

When making web standards, multiple scenarios possibly affecting privacy are considered. This includes even extreme ones; and this is a good thing. It’s best to predict the creative use and abuse of web features, before they are exploited.

Vibration API

The mechanism allowing websites to utilize a device’s vibration motor is called the Vibration API. The mechanism allows a device to be vibrated in particular patterns. The argument to the vibration() function is a list called a pattern. The list’s odd indices cause a vibration for a specific length of time, and even values are the still periods. For example, a web designer can make the device to vibrate for a specific duration, say 50 ms and follow that with a still period of 100 ms using the following call:

navigator.vibration([50,100])

In certain circumstances this can create several interesting potential privacy risks. Let’s look at the Vibration API from a privacy point of view. I will consider a number of scenarios on various technical levels.

Toy de-anonymisation scenario

One potential risk is the identification of a particular person in real life. Imagine several people in the same room placing their devices on a table. At some point, one person’s device vibrates in specific patterns. This individual might then become marked to a potential observer.

How could such a script be delivered? One possibility is though web advertising infrastructures. These offer capabilities of targeting individuals with a considerable accuracy (with respect to their location).

Continue reading Analyzing privacy aspects of the W3C Vibration API

Biometrics for payments

HSBC and First Direct recently announced that they are introducing fingerprint and voice recognition authentication for customers of online and telephone banking. In my own research, I first found nearly 20 years ago that people who have a multitude of passwords and PINs cannot manage them as security experts want them to. As the number of digital devices and services we use has increased rapidly, managing dozens of login details has become a headache for most people. We recently reported that most bank customers juggle multiple PINs, and are unable to follow the rules that banks set in their contracts. Our research also found that many people dislike the 2-factor token solutions that are currently used by many UK banks.

Passwords as most people use them today are not particularly secure. Attackers can easily attempt to collect information on individuals, using leaks of password files not properly protected by some websites, “phishing” scams or malware planted on people’s computers. Reusing a banking password on other websites – something that many of us do because we cannot remember dozens of different passwords – is also a significant security risk.

The introduction of fingerprint recognition on smartphones – such as the iPhone – has delighted many users fed up with entering their PINs dozens of times a day. So the announcement that HSBC and other banks will be able to use the fingerprint sensor on their smartphones for banking means that millions of consumers will finally be able to end their battle with passwords and PINs and use biometrics instead. Other services people access from their smartphones are likely to follow suit. And given the negative impact that cumbersome authentication via passwords and PINs has on staff productivity and morale in many organisations, we can expect to see biometrics deployed in work contexts, too.

But while biometrics – unlike passwords – do not require mental gymnastics from users, there are different usability challenges. Leveraging the biometric from the modality of interaction – e.g. voice recognition phone-based interactions – makes authentication an easy task, but it will work considerably better in quiet environments than noisy ones – such as a train stations or with many people talking in the background. As many smartphone users have learnt, fingerprint sensors have a hard time recognising cold and wet fingers. And – as we report in a paper presented at IEEE Identity, Security and Behavior Analysis last week – privacy concerns mean some users ‘don’t like putting their face on the Internet’. Biometrics can’t come soon enough for most users, but there is still a lot of design and testing work to be done to make biometrics work for different interaction, physical and social contexts.

Insecure by design: protocols for encrypted phone calls

The MIKEY-SAKKE protocol is being promoted by the UK government as a better way to secure phone calls. The reality is that MIKEY-SAKKE is designed to offer minimal security while allowing undetectable mass surveillance, through the introduction a backdoor based around mandatory key-escrow. This weakness has implications which go further than just the security of phone calls.

The current state of security for phone calls leaves a lot to be desired. Land-line calls are almost entirely unencrypted, and cellphone calls are also unencrypted except for the radio link between the handset and the phone network. While the latest cryptography standards for cellphones (3G and 4G) are reasonably strong it is possible to force a phone to fall back to older standards with easy-to-break cryptography, if any. The vast majority of phones will not reveal to their user whether such an attack is under way.

The only reason that eavesdropping on land-line calls is not commonplace is that getting access to the closed phone networks is not as easy compared to the more open Internet, and cellphone cryptography designers relied on the equipment necessary to intercept the radio link being only affordable by well-funded government intelligence agencies, and not by criminals or for corporate espionage. That might have been true in the past but it certainly no longer the case with the necessary equipment now available for $1,500. Governments, companies and individuals are increasingly looking for better security.

A second driver for better phone call encryption is the convergence of Internet and phone networks. The LTE (Long-Term Evolution) 4G cellphone standard – under development by the 3rd Generation Partnership Project (3GPP) – carries voice calls over IP packets, and desktop phones in companies are increasingly carrying voice over IP (VoIP) too. Because voice calls may travel over the Internet, whatever security was offered by the closed phone networks is gone and so other security mechanisms are needed.

Like Internet data encryption, voice encryption can broadly be categorised as either link encryption, where each intermediary may encrypt data before passing it onto the next, or end-to-end encryption, where communications are encrypted such that only the legitimate end-points can have access to the unencrypted communication. End-to-end encryption is preferable for security because it avoids intermediaries being able to eavesdrop on communications and gives the end-points assurance that communications will indeed be encrypted all the way to their other communication partner.

Current cellphone encryption standards are link encryption: the phone encrypts calls between it and the phone network using cryptographic keys stored on the Subscriber Identity Module (SIM). Within the phone network, encryption may also be present but the network provider still has access to unencrypted data, so even ignoring the vulnerability to fall-back attacks on the radio link, the network providers and their suppliers are weak points that are tempting for attackers to compromise. Recent examples of such attacks include the compromise of the phone networks of Vodafone in Greece (2004) and Belgacom in Belgium (2012), and the SIM card supplier Gemalto in France (2010). The identity of the Vodafone Greece hacker remains unknown (though the NSA is suspected) but the attacks against Belgacom and Gemalto were carried out by the UK signals intelligence agency – GCHQ – and only publicly revealed from the Snowden leaks, so it is quite possible there are others attacks which remain hidden.

Email is typically only secured by link encryption, if at all, with HTTPS encrypting access to most webmail and Transport Layer Security (TLS) sometimes encrypting other communication protocols that carry email (SMTP, IMAP and POP). Again, the fact that intermediaries have access to plaintext creates a vulnerability, as demonstrated by the 2009 hack of Google’s Gmail likely originating from China. End-to-end email encryption is possible using the OpenPGP or S/MIME protocols but their use is not common, primarily due to their poor usability, which in turn is at least partially a result of having to stay compatible with older insecure email standards.

In contrast, instant messaging applications had more opportunity to start with a clean-slate (because there is no expectation of compatibility among different networks) and so this is where much innovation in terms of end-to-end security has taken place. Secure voice communication however has had less attention than instant messaging so in the remainder of the article we shall examine what should be expected of a secure voice communication system, and in particular see how one of the latest and up-coming protocols, MIKEY-SAKKE, which comes with UK government backing, meets these criteria.

MIKEY-SAKKE and Secure Chorus

MIKEY-SAKKE is the security protocol behind the Secure Chorus voice (and also video) encryption standard, commissioned and designed by GCHQ through their information security arm, CESG. GCHQ have announced that they will only certify voice encryption products through their Commercial Product Assurance (CPA) security evaluation scheme if the product implements MIKEY-SAKKE and Secure Chorus. As a result, MIKEY-SAKKE has a monopoly over the vast majority of classified UK government voice communication and so companies developing secure voice communication systems must implement it in order to gain access to this market. GCHQ can also set requirements of what products are used in the public sector and as well as for companies operating critical national infrastructure.

UK government standards are also influential in guiding purchase decisions outside of government and we are already seeing MIKEY-SAKKE marketed commercially as “government-grade security” and capitalising on their approval for use in the UK government. For this reason, and also because GCHQ have provided implementers a free open source library to make it easier and cheaper to deploy Secure Chorus, we can expect wide use MIKEY-SAKKE in industry and possibly among the public. It is therefore important to consider whether MIKEY-SAKKE is appropriate for wide-scale use. For the reasons outlined in the remainder of this article, the answer is no – MIKEY-SAKKE is designed to offer minimal security while allowing undetectable mass surveillance though key-escrow, not to provide effective security.

Continue reading Insecure by design: protocols for encrypted phone calls

Experimenting with SSL Vulnerabilities in Android Apps

As the number of always-on, always-connected smartphones increase, so does the amount of personal and sensitive information they collect and transmit. Thus, it is crucial to secure traffic exchanged by these devices, especially considering that mobile users might connect to open Wi-Fi networks or even fake cell towers. The go-to protocol to secure network connection is HTTPS i.e., HTTP over SSL/TLS.

In the Android ecosystem, applications (apps for short), support HTTPS on sockets by relying on the android.net, android.webkit, java.net, javax.net, java.security, javax.security.cert, and org.apache.http packages of the Android SDK. These packages are used to create HTTP/HTTPS connections, administer and verify certificates and keys, and instantiate TrustManager and HostnameVerifier interfaces, which are in turn used in the SSL certificate validation logic.

A TrustManager manages the certificates of all Certificate Authorities (CAs) used to assess a certificate’s validity. Only root CAs trusted by Android are contained in the default TrustManager. A HostnameVerifier performs hostname verification whenever a URL’s hostname does not match the hostname in the peer’s identification credentials.

While browsers provide users with visual feedback that their communication is secured (via the lock symbol) as well as certificate validation issues, non-browser apps do so less extensively and effectively. This shortcoming motivates the need to scrutinize the security of network connections used by apps to transmit user sensitive data. We found that some of the most popular Android apps insufficiently secure these connections, putting users’ passwords, credit card details and chat messages at risk.

Continue reading Experimenting with SSL Vulnerabilities in Android Apps