How Tor’s privacy was (momentarily) broken, and the questions it raises

Just how secure is Tor, one of the most widely used internet privacy tools? Court documents released from the Silk Road 2.0 trial suggest that a “university-based research institute” provided information that broke Tor’s privacy protections, helping identify the operator of the illicit online marketplace.

Silk Road and its successor Silk Road 2.0 were run as a Tor hidden service, an anonymised website accessible only over the Tor network which protects the identity of those running the site and those using it. The same technology is used to protect the privacy of visitors to other websites including journalists reporting on mafia activity, search engines and social networks, so the security of Tor is of critical importance to many.

How Tor’s privacy shield works

Almost 97% of Tor traffic is from those using Tor to anonymise their use of standard websites outside the network. To do so a path is created through the Tor network via three computers (nodes) selected at random: a first node entering the network, a middle node (or nodes), and a final node from which the communication exits the Tor network and passes to the destination website. The first node knows the user’s address, the last node knows the site being accessed, but no node knows both.

The remaining 3% of Tor traffic is to hidden services. These websites use “.onion” addresses stored in a hidden service directory. The user first requests information on how to contact the hidden service website, then both the user and the website make the three-hop path through the Tor network to a rendezvous point which joins the two connections and allows both parties to communicate.

In both cases, if a malicious operator simultaneously controls both the first and last nodes to the Tor network then it is possible to link the incoming and outgoing traffic and potentially identify the user. To prevent this, the Tor network is designed from the outset to have sufficient diversity in terms of who runs nodes and where they are located – and the way that nodes are selected will avoid choosing closely related nodes, so as to reduce the likelihood of a user’s privacy being compromised.

How Tor works
How Tor works (source: EFF)

This type of design is known as distributed trust: compromising any single computer should not be enough to break the security the system offers (although compromising a large proportion of the network is still a problem). Distributed trust systems protect not only the users, but also the operators; because the operators cannot break the users’ anonymity – they do not have the “keys” themselves – they are less likely to be targeted by attackers.

Unpeeling the onion skin

With about 2m daily users Tor is by far the most widely used privacy system and is considered one of the most secure, so research that demonstrates the existence of a vulnerability is important. Most research examines how to increase the likelihood of an attacker controlling both the first and last node in a connection, or how to link incoming traffic to outgoing.

When the 2014 programme for the annual BlackHat conference was announced, it included a talk by a team of researchers from CERT, a Carnegie Mellon University research institute, claiming to have found a means to compromise Tor. But the talk was cancelled and, unusually, the researchers did not give advance notice of the vulnerability to the Tor Project in order for them to examine and fix it where necessary.

This decision was particularly strange given that CERT is worldwide coordinator for ensuring software vendors are notified of vulnerabilities in their products so they can fix them before criminals can exploit them. However, the CERT researchers gave enough hints that Tor developers were able to investigate what had happened. When they examined the network they found someone was indeed attacking Tor users using a technique that matched CERT’s description.

The multiple node attack

The attack turned on a means to tamper with a user’s traffic as they looked up the .onion address in the hidden service directory, or in the hidden service’s traffic as it uploaded the information to the directory.

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New EU Innovative Training Network project “Privacy & Us”

Last week, “Privacy & Us” — an Innovative Training Network (ITN) project funded by the EU’s Marie Skłodowska-Curie actions — held its kick-off meeting in Munich. Hosted in the nice and modern Wisschenschafts Zentrum campus by Uniscon, one of the project partners, principal investigators from seven different countries set out the plan for the next 48 months.

Privacy & Us really stands for “Privacy and Usability” and aims to conduct privacy research and, over the next 3 years, train thirteen Early Stage Researchers (ESRs) — i.e., PhD students — to be able to reason, design, and develop innovative solutions to privacy research challenges, not only from a technical point of view but also from the “human side”.

The project involves nine “beneficiaries”: Karlstads Universitet (Sweden), Goethe Universitaet Frankfurt (Germany), Tel Aviv University (Israel), Unabhängiges Landeszentrum für Datenschutz (Germany), Uniscon (Germany), University College London (UK), USECON (Austria), VASCO Innovation Center (UK), and Wirtschaft Universitat Wien (Austria), as well as seven partner organizations: the Austrian Data Protection Authority (Austria), Preslmayr Rechtsanwälte OG (Austria), Friedrich-Alexander University Erlangen (Germany), University of Bonn (Germany), the Bavarian Data Protection Authority (Germany), EveryWare Technologies (Italy), and Sentor MSS AB (Sweden).

The people behind Privacy & Us project at the kick-off meeting in Munich, December 2015
The people behind Privacy & Us project at the kick-off meeting in Munich, December 2015

The Innovative Training Networks are interdisciplinary and multidisciplinary in nature and promote, by design, a collaborative approach to research training. Funding is extremely competitive, with acceptance rate as low as 6%, and quite generous for the ESRs who often enjoy higher than usual salaries (exact numbers depend on the hosting country), plus 600 EUR/month mobility allowance and 500 EUR/month family allowance.

The students will start in August 2016 and will be trained to face both current and future challenges in the area of privacy and usability, spending a minimum of six months in secondment to another partner organization, and participating in several training and development activities.

Three studentships will be hosted at UCL,  under the supervision of Dr Emiliano De Cristofaro, Prof. Angela Sasse, Prof. Ann Blandford, and Dr Steven Murdoch. Specifically, one project will investigate how to securely and efficiently store genomic data, design and implementing privacy-preserving genomic testing, as well as support user-centered design of secure personal genomic applications. The second project will aim to better understand and support individuals’ decision-making around healthcare data disclosure, weighing up personal and societal costs and benefits of disclosure, and the third (with the VASCO Innovation Centre) will explore techniques for privacy-preserving authentication, namely, extending these to develop and evaluate innovative solutions for secure and usable authentication that respects user privacy.

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Forced authorisation chip and PIN scam hitting high-end retailers

Chip and PIN was designed to prevent fraud, but it also created a new opportunity for criminals that is taking retailers by surprise. Known as “forced authorisation”, committing the fraud requires no special equipment and when it works, it works big: in one transaction a jewellers store lost £20,500. This type of fraud is already a problem in the UK, and now that US retailers have made it through the first Black Friday since the Chip and PIN deadline, criminals there will be looking into what new fraud techniques are available.

The fraud works when the retailer has a one-piece Chip and PIN terminal that’s passed between the customer and retailer during the course of the transaction. This type of terminal is common, particularly in smaller shops and restaurants. They’re a cheaper option compared to terminals with a separate PIN pad (at least until a fraud happens).

The way forced authorisation fraud works is that the retailer sets up the terminal for a transaction by inserting the customer’s card and entering the amount, then hands the terminal over to the customer so they can type in the PIN. But the criminal has used a stolen or counterfeit card, and due to the high value of the transaction the terminal performs a “referral” — asking the retailer to call the bank to perform additional checks such as the customer answering a security question. If the security checks pass, the bank will give the retailer an authorisation code to enter into the terminal.

The problem is that when the terminal asks for these security checks, it’s still in the hands of the criminal, and it’s the criminal that follows the steps that the retailer should have. Since there’s no phone conversation with the bank, the criminal doesn’t know the correct authorisation code. But what surprises retailers is that the criminal can type in anything at this stage and the transaction will go through. The criminal might also be able to bypass other security features, for example they could override the checking of the PIN by following the steps the retailer would if the customer has forgotten the PIN.

By the time the terminal is passed back to the retailer, it looks like the transaction was completed successfully. The receipt will differ only very subtly from that of a normal transaction, if at all. The criminal walks off with the goods and it’s only at the end of the day that the authorisation code is checked by the bank. By that time, the criminal is long gone. Because some of the security checks the bank asked for weren’t completed, the retailer doesn’t get the money.

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Program obfuscation

I had the pleasure of visiting the Simons Institute for the Theory of Computing at UC Berkeley over the Summer. One of the main themes of the programme was obfuscation.

Recently there has been a lot of exciting research on developing cryptographic techniques for program obfuscation. Obfuscation is of course not a new thing, you may already be familiar with the obfuscated C contest. But the hope underlying this research effort is to replace ad hoc obfuscation, which may or may not be possible to reverse engineer, with general techniques that can be applied to obfuscate any program and that satisfy a rigorous definition of what it means for a program to be obfuscated.

Even defining what it means for a program to be obfuscated is not trivial. Ideally, we would like an obfuscator to be a compiler that turns any computer program into a virtual black-box. By a virtual black-box we mean that the obfuscated program should preserve functionality while not leaking any other information about the original code, i.e., it should have the same input-output behaviour as the original program and you should not be able to learn anything beyond what you could learn by executing the original program. Unfortunately it turns out that this is too ambitious a goal: there are special programs, which are impossible to virtual black-box obfuscate.

Instead cryptographers have been working towards developing something called indistinguishability obfuscation. Here the goal is that given two functionally equivalent programs P1 and P2 of the same size and an obfuscation of one of them O(Pi), it should not be possible to tell which one has been obfuscated. Interestingly, even though this is a weaker notion than virtual black-box obfuscation it has already found many applications in the construction of cryptographic schemes. Furthermore, it has been proved that indistinguishability obfuscation is the best possible obfuscation in the sense that any information leaked by an obfuscated program is also leaked by any other program computing the same functionality.

So, when will you see obfuscation algorithms that you can use to obfuscate your code? Well, current obfuscation algorithms have horrible efficiency and are far from practical applicability so there is still a lot of research to be done on improving them. Moreover, current obfuscation proposals are based on something called graded encoding schemes. At the moment, there is a tug of war going on between cryptographers proposing graded encoding schemes and cryptanalysts breaking them. Breaking graded encoding schemes does not necessarily break the obfuscation algorithms building on them but it is fair to say that right now the situation is a mess. Which from a researcher’s perspective makes the field very exciting since there is still a lot to discover!

If you want to learn more about obfuscation I recommend the watching some of the excellent talks from the programme.

Scaling Tor hidden services

Tor hidden services offer several security advantages over normal websites:

  • both the client requesting the webpage and the server returning it can be anonymous;
  • websites’ domain names (.onion addresses) are linked to their public key so are hard to impersonate; and
  • there is mandatory encryption from the client to the server.

However, Tor hidden services as originally implemented did not take full advantage of parallel processing, whether from a single multi-core computer or from load-balancing over multiple computers. Therefore once a single hidden service has hit the limit of vertical scaling (getting faster CPUs) there is not the option of horizontal scaling (adding more CPUs and more computers). There are also bottle-necks in the Tor networks, such as the 3–10 introduction points that help to negotiate the connection between the hidden service and the rendezvous point that actually carries the traffic.

For my MSc Information Security project at UCL, supervised by Steven Murdoch with the assistance of Alec Muffett and other Security Infrastructure engineers at Facebook in London, I explored possible techniques for improving the horizontal scalability of Tor hidden services. More precisely, I was looking at possible load balancing techniques to offer better performance and resiliency against hardware/network failures. The focus of the research was aimed at popular non-anonymous hidden services, where the anonymity of the service provider was not required; an example of this could be Facebook’s .onion address.

One approach I explored was to simply run multiple hidden service instances using the same private key (and hence the same .onion address). Each hidden service periodically uploads its own descriptor, which describes the available introduction points, to six hidden service directories on a distributed hash table. The hidden service instance chosen by the client depends on which hidden service instance most recently uploaded its descriptor. In theory this approach allows an arbitrary number of hidden service instances, where each periodically uploads its own descriptors, overwriting those of others.

This approach can work for popular hidden services because, with the large number of clients, some will be using the descriptor most recently uploaded, while others will have cached older versions and continue to use them. However my experiments showed that the distribution of the clients over the hidden service instances set up in this way is highly non-uniform.

I therefore ran experiments on a private Tor network using the Shadow network simulator running multiple hidden service instances, and measuring the load distribution over time. The experiments were devised such that the instances uploaded their descriptors simultaneously, which resulted in different hidden service directories receiving different descriptors. As a result, clients connecting to a hidden service would be balanced more uniformly over the available instances.

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Sarah Meiklejohn – Security and Cryptography

Sarah Meiklejohn As a child, Sarah Meiklejohn thought she might become a linguist, largely because she was so strongly interested in the work being done to decode the ancient Greek writing systems Linear A and Linear B.

“I loved all that stuff,” she says. “And then I started doing mathematics.” At that point, with the help of Simon Singh’s The Code Book, she realised the attraction was codebreaking rather than human languages themselves. Simultaneously, security and privacy were increasingly in the spotlight.

“I’m a very private person, and so privacy is near and dear to my heart,” she says. “It’s an important right that a lot of people don’t seem interested in exercising, but it’s still a right. Even if no one voted we would still agree that it was important for people to be able to vote.”

It was during her undergraduate years at Brown, which included a fifth-year Masters degree, that she made the transition from mathematics to cryptography and began studying computer science. She went on to do her PhD at the University of California at San Diego. Her appointment at UCL, which is shared between the Department of Computer Science and the Department of Crime Science, is her first job.

Probably her best-known work is A Fistful of Bitcoins: Characterizing Payments Among Men with No Names (PDF), written with Marjori Pomarole, Grant Jordan, Kirill Levchenko, Damon McCoy, Geoffrey M. Voelker, and Stefan Savage and presented at USENIX 2013, which studied the question of how much anonymity bitcoin really provides.

“The main thing I was trying to focus on in that paper is what bitcoin is used for,” she says. The work began with buying some bitcoin (in 2012, at about £3 each), and performing some transactions with them over a period of months. Using the data collected this way allowed her to uncover some “ground truth” data.

“We developed these clustering techniques to get down to single users and owners.” The result was that they could identify which addresses belonged to which exchanges and enabled them to get a view of what was going on in the network. “So we could say this many bitcoins passed through this exchange per month, or how many were going to underground services like Silk Road.”

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Category errors in (information) security: how logic can help

(Information) security can, pretty strongly arguably, be defined as being the process by which it is ensured that just the right agents have just the right access to just the right (information) resources at just the right time. Of course, one can refine this rather pithy definition somewhat, and apply tailored versions of it to one’s favourite applications and scenarios.

A convenient taxonomy for information security is determined by the concepts of confidentiality, integrity, and availability, or CIA; informally:

Confidentiality
the property that just the right agents have access to specified information or systems;
Integrity
the property that specified information or systems are as they should be;
Availability
the property that specified information or systems can be accessed or used when required.

Alternatives to confidentiality, integrity, and availability are sensitivity and criticality, in which sensitivity amounts to confidentiality together with some aspects of integrity and criticality amounts to availability together with some aspects of integrity.

But the key point about these categories of phenomena is that they are declarative; that is, they provide a statement of what is required. For example, that all documents marked ‘company private’ be accessible only to the company’s employees (confidentiality), or that all passengers on the aircraft be free of weapons (integrity), or that the company’s servers be up and running 99.99% of the time (availability).

It’s all very well stating, declaratively, one’s security objectives, but how are they to be achieved? Declarative concepts should not be confused with operational concepts; that is, ones that describe how something is done. For example, passwords and encryption are used to ensure that documents remain confidential, or security searches ensure that passengers do not carry weapons onto an aircraft, or RAID servers are employed to ensure adequate system availability. So, along with each declarative aim there is a collection of operational tools that can be used to achieve it.

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An Analysis of Reshipping Mule Scams

Credit cards are a popular target for cybercriminals. Miscreants infect victim computers with malware that reports back to their command and control servers any credit card information that the user inserts in her computer, or compromise large retail stores stealing their customers’ credit card information. After obtaining credit card details from their victims, cybercriminals face the problem of monetising such information. As we recently covered on this blog, cybercriminals monetise stolen credit cards by cloning them and using very clever tricks to bypass the Chip and PIN verification mechanisms. This way they are able to use the counterfeit credit card in a physical store, purchase expensive items such as cigarettes, and re-sell them for a profit.

Another possible way for cybercriminals to monetise stolen credit cards is by purchasing goods on online stores. To this end, they need more information than the one contained on the credit card alone: for those of you who are familiar with online shopping, some merchants require a billing address as well to allow the purchase (which is called “card not present transaction”). This additional information is often available to the criminal – it might, for example, have been retrieved together with the credit card credentials as part of a data breach against an online retailer. When purchasing goods online, cybercriminals face the issue of shipping: if they shipped the stolen goods to their home address, this would make it easy for law enforcement to find and arrest them. For this reason, miscreants need intermediaries in the shipping process.

In our recent paper, which was presented at the ACM Conference on Computer and Communications Security (CCS), we analyse a criminal scheme designed to help miscreants who wish to monetise stolen credit cards as we described: A cybercriminal (called operator) recruits unsuspecting citizens with the promise of a rewarding work-from-home job. This job involves receiving packages at home and having to re-ship them to a different address, provided by the operator. By accepting the job, people unknowingly become part of a criminal operation: the packages that they receive at their home contain stolen goods, and the shipping destinations are often overseas, typically in Russia. These shipping agents are commonly known as reshipping mules (or drops for stuff in the underground community). The operator then rents shipping mules as a service to cybercriminals wanting to ship stolen goods abroad. The cybercriminals taking advantage of such services are known as stuffers in the underground community. As a price for the service, the stuffer will pay a commission to the operator for each package reshipped through the service.

reshippinggraphic-580x328

In collaboration with the FBI and the United States Postal Inspection Service (USPIS) we conducted a study on such reshipping scam sites. This study involved data coming from seven different reshipping sites, and provides the research community with invaluable insights on how these operations are run. We observed that the vast majority of the re-shipped packages end up in the Moscow, Russia area, and that the goods purchased with stolen credit cards span multiple categories, from expensive electronics such as Apple products, to designer clothes, to DSLR cameras and even weapon accessories. Given the amount of goods shipped by the reshipping mule sites that we analysed, the annual revenue generated from such operations can span between 1.8 and 7.3 million US dollars. The overall losses are much higher though: the online merchant loses an expensive item from its inventory and typically has to refund the owner of the stolen credit card. In addition, the rogue goods typically travel labeled as “second hand goods” and therefore custom taxes are also evaded. Once the items purchased with stolen credit cards reach their destination they will be sold on the black market by cybercriminals.

Studying the management of the mules lead us to some surprising findings. When applying for the job, people are usually required to send the operator copies of their ID cards and passport. After they are hired, mules are promised to be paid at the end of their first month of employment. However, from our data it is clear that mules are usually never paid. After their first month expires, they are never contacted back by the operator, who just moves on and hires new mules. In other words, the mules become victims of this scam themselves, by never seeing a penny. Moreover, because they sent copies of their documents to the criminals, mules can potentially become victims of identity theft.

Our study is the first one shedding some light on these monetisation schemes linked to credit card fraud. We believe the insights in this paper can provide law enforcement and researchers with a better understanding of the cybercriminal ecosystem and allow them to develop more effective mitigation techniques against these problems.

George Danezis – Smart grid privacy, peer-to-peer and social network security

“I work on technical aspects of privacy,” says George Danezis, a reader in security and privacy engineering at UCL and part of the Academic Centre of Excellence in Cyber Security Research (ACE-CSR). There are, of course, many other limitations: regulatory, policy, economic. But, he says, “Technology is the enabler for everything else – though you need everything else for it to be useful.” Danezis believes providing privacy at the technology level is particularly important as it seems clear that both regulation and the “moralising” approach (telling people the things they shouldn’t do) have failed.

There are many reasons why someone gets interested in researching technical solutions to intractable problems. Sometimes the motivation is to eliminate a personal frustration; other times it’s simply a fascination with the technology itself. For Danezis, it began with other people.

“I discovered that a lot of the people around me could not use technology out of the box to do things personally or collectively.” For example, he saw NGOs defending human rights worry about sending an email or chatting online, particularly in countries hostile to their work. A second motivation had to do with timing: when he began work it wasn’t yet clear that the Internet would develop into a medium anyone could use freely to publish stories. That particular fear has abated, but other issues such as the need for anonymous communications and private data sharing are still with us.

“Without anonymity we can’t offer strong privacy,” he says.

Unlike many researchers, Danezis did not really grow up with computers. He spent his childhood in Greece and Belgium, and until he got Internet access at 16, “I had access only to the programming books I could find in an average Belgian bookshop. There wasn’t a BBC Micro in every school and it was difficult to find information. I had one teacher who taught me how to program in Logo, and no way of finding more information easily.” Then he arrived at Cambridge in 1997, and “discovered thousands of people who knew how to do crazy stuff with computers.”

Danezis’ key research question is, “What functionality can we achieve while still attaining a degree of hard privacy?” And the corollary: at what cost in complexity of engineering? “We can’t just say, let’s recreate the whole computer environment,” he said. “We need to evolve efficiently out of today’s situation.”

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Just how sophisticated will card fraud techniques become?

In late 2009, my colleagues and I discovered a serious vulnerability in EMV, the most widely used standard for smart card payments, known as “Chip and PIN” in the UK. We showed that it was possible for criminals to use a stolen credit or debit card without knowing the PIN, by tricking the terminal into thinking that any PIN is correct. We gave the banking industry advance notice of our discovery in early December 2009, to give them time to fix the problem before we published our research. After this period expired (two months, in this case) we published our paper as well explaining our results to the public on BBC Newsnight. We demonstrated that this vulnerability was real using a proof-of-concept system built from equipment we had available (off-the shelf laptop and card reader, FPGA development board, and hand-made card emulator).

No-PIN vulnerability demonstration

After the programme aired, the response from the banking industry dismissed the possibility that the vulnerability would be successfully exploited by criminals. The banking trade body, the UK Cards Association, said:

“We believe that this complicated method will never present a real threat to our customers’ cards. … Neither the banking industry nor the police have any evidence of criminals having the capability to deploy such sophisticated attacks.”

Similarly, EMVCo, who develop the EMV standards said:

“It is EMVCo’s view that when the full payment process is taken into account, suitable countermeasures to the attack described in the recent Cambridge Report are already available.”

It was therefore interesting to see that in May 2011, criminals were caught having stolen cards in France then exploiting a variant of this vulnerability to buy over €500,000 worth of goods in Belgium (which were then re-sold). At the time, not many details were available, but it seemed that the techniques the criminals used were much more sophisticated than our proof-of-concept demonstration.

We now know more about what actually happened, as well as the banks’ response, thanks to a paper by the researchers who performed the forensic analysis that formed part of the criminal investigation of this case. It shows just how sophisticated criminals could be, given sufficient motivation, contrary to the expectations in the original banking industry response.

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