Category Archives: Academic papers

Rollercoaster: Communicating Efficiently and Anonymously in Large Groups

Academic papers, Privacy technology, Security engineering

End-to-end (E2E) encryption is now widely deployed in messaging apps such as WhatsApp and Signal and billions of people around the world have the contents of their message protected against strong adversaries. However, while the message contents are encrypted, their metadata still leaks sensitive information. For example, it is easy for an infrastructure provider to tell which customers are communicating, with whom and when.

Anonymous communication hides this metadata. This is crucial for the protection of individuals such as whistleblowers who expose criminal wrongdoing, activists organising a protest, or embassies coordinating a response to a diplomatic incident. All these face powerful adversaries for whom the communication metadata alone (without knowing the specific message text) can result in harm for the individuals concerned.

Tor is a popular tool that achieves anonymous communication by forwarding messages through multiple intermediate nodes or relays. At each relay the outermost layer of the message is decrypted and the inner message is forwarded to the next relay. An adversary who wants to figure out where A’s messages are finally delivered can attempt to follow a message as it passes through each relay. Alternatively, an adversary might confirm a suspicion that user A talks to user B by observing traffic patterns at A’s and B’s access points to the network instead. If indeed A and B are talking to each other, there will be a correlation between their traffic patterns. For instance, if an adversary observes that A sends three messages and three messages arrive at B shortly afterwards, this provides some evidence that A talks to B. The adversary can increase their certainty by collecting traffic over a longer period of time.

Mix networks such as Loopix use a different design, which defends against such traffic analysis attacks by using (i) traffic shaping and (ii) more intermediate nodes, so called mix nodes. In a simple mix network, each client only sends packets of a fixed length and at predefined intervals (e.g. 1 KiB every 5 seconds). When there is no payload to send, a cover packet is crafted that is indistinguishable to the adversary from a payload packet. If there is more than one payload packet to be sent, packets are queued and sent one by one on the predefined schedule. This traffic shaping ensures that an observer cannot gain any information from observing outgoing network packets. Moreover, mix nodes typically delay each incoming message by a random amount of time before forwarding it (with the delay chosen independently for each message), making it harder for an adversary to correlate a mix node’s incoming and outgoing messages, since they are likely to be reordered. In contrast, Tor relays forward messages as soon as possible in order to minimise latency.

Mix Networks work well for pairwise communication, but we found that group communication creates a unique challenge. Such group communication encompasses both traditional chat groups (e.g. WhatsApp groups or IRC) and collaborative editing (e.g. Google Docs, calendar sync, todo lists) where updates need to be disseminated to all other participants who are viewing or editing the content. There are many scenarios where anonymity requirements meet group communication, such as coordination between activists, diplomatic correspondence between embassies, and organisation of political campaigns.

The traffic shaping of mix networks makes efficient group communication difficult. The limited rate of outgoing messages means that sequentially sending a message to each group member can take a long time. For instance, assuming that the outgoing rate is 1 message every 5 seconds, it will take more than 8 minutes to send the message to all members in a group of size 100. During this process the sender’s output queue is blocked and they cannot send any other messages.

In our paper we propose a scheme named Rollercoaster that greatly improves the latency for group communication in mix networks. The basic idea is that group members who have already received a message can help distribute it to other members of the group. Like a chain reaction, the distribution of the message gains momentum as the number of recipients grows. In an ideal execution of this scheme, the number of users who have received a message doubles with every round, leading to substantially more efficient message delivery across the group.

Rollercoaster works well because there is typically plenty of spare capacity in the network. At any given time most clients will not be actively communicating and they are therefore mostly sending cover traffic. As a result, Rollercoaster actually improves the efficiency of the network and reduces the rate of cover traffic, which in turn reduces the overall required network bandwidth. At the same time, Rollercoaster does not require any changes to the existing Mix network protocol and can benefit from the existing user base and anonymity set.

The basic idea requires more careful consideration in a realistic environment where clients are offline or do not behave faithfully. A fault-tolerant version of our Rollercoaster scheme addresses these concerns by waiting for acknowledgement messages from recipients. If those acknowledgement messages are not received by the sender in a fixed period of time, forwarding roles are reassigned and another delivery attempt is made via a new route. We also show how a single number can seed the generation of a deterministic forwarding schedule. This allows efficient communication of different forwarding schedules and balances individual workloads within the group.

We presented our paper at USENIX Security ‘21 (paper, slides, and recording). It contains more extensions and optimisations than we can summarise here. There is also an extended version available as a tech report with more detailed security arguments in the appendices. The paper reference is:
Daniel Hugenroth, Martin Kleppmann, and Alastair R. Beresford. Rollercoaster: An Efficient Group-Multicast Scheme for Mix Networks. Proceedings of the 30th USENIX Security Symposium (USENIX Security), 2021.

Trojan Source: Invisible Vulnerabilities

Academic papers, Open-source security, Operating systems, Programming languages, Security engineering, Usability

Today we are releasing Trojan Source: Invisible Vulnerabilities, a paper describing cool new tricks for crafting targeted vulnerabilities that are invisible to human code reviewers.

Until now, an adversary wanting to smuggle a vulnerability into software could try inserting an unobtrusive bug in an obscure piece of code. Critical open-source projects such as operating systems depend on human review of all new code to detect malicious contributions by volunteers. So how might wicked code evade human eyes?

We have discovered ways of manipulating the encoding of source code files so that human viewers and compilers see different logic. One particularly pernicious method uses Unicode directionality override characters to display code as an anagram of its true logic. We’ve verified that this attack works against C, C++, C#, JavaScript, Java, Rust, Go, and Python, and suspect that it will work against most other modern languages.

This potentially devastating attack is tracked as CVE-2021-42574, while a related attack that uses homoglyphs – visually similar characters – is tracked as CVE-2021-42694. This work has been under embargo for a 99-day period, giving time for a major coordinated disclosure effort in which many compilers, interpreters, code editors, and repositories have implemented defenses.

This attack was inspired by our recent work on Imperceptible Perturbations, where we use directionality overrides, homoglyphs, and other Unicode features to break the text-based machine learning systems used for toxic content filtering, machine translation, and many other NLP tasks.

More information about the Trojan Source attack can be found at trojansource.codes, and proofs of concept can also be found on GitHub. The full paper can be found here.

Bugs in our pockets?

Academic papers, Internet censorship, Legal issues, News coverage, Operating systems, Politics, Privacy technology, Security economics, Security psychology

In August, Apple announced a system to check all our iPhones for illegal images, then delayed its launch after widespread pushback. Yet some governments continue to press for just such a surveillance system, and the EU is due to announce a new child protection law at the start of December.

Now, in Bugs in our Pockets: The Risks of Client-Side Scanning, colleagues and I take a long hard look at the options for mass surveillance via software embedded in people’s devices, as opposed to the current practice of monitoring our communications. Client-side scanning, as the agencies’ new wet dream is called, has a range of possible missions. While Apple and the FBI talked about finding still images of sex abuse, the EU was talking last year about videos and text too, and of targeting terrorism once the argument had been won on child protection. It can also use a number of possible technologies; in addition to the perceptual hash functions in the Apple proposal, there’s talk of machine-learning models. And, as a leaked EU internal report made clear, the preferred outcome for governments may be a mix of client-side and server-side scanning.

In our report, we provide a detailed analysis of scanning capabilities at both the client and the server, the trade-offs between false positives and false negatives, and the side effects – such as the ways in which adding scanning systems to citizens’ devices will open them up to new types of attack.

We did not set out to praise Apple’s proposal, but we ended up concluding that it was probably about the best that could be done. Even so, it did not come close to providing a system that a rational person might consider trustworthy.

Even if the engineering on the phone were perfect, a scanner brings within the user’s trust perimeter all those involved in targeting it – in deciding which photos go on the naughty list, or how to train any machine-learning models that riffle through your texts or watch your videos. Even if it starts out trained on images of child abuse that all agree are illegal, it’s easy for both insiders and outsiders to manipulate images to create both false negatives and false positives. The more we look at the detail, the less attractive such a system becomes. The measures required to limit the obvious abuses so constrain the design space that you end up with something that could not be very effective as a policing tool; and if the European institutions were to mandate its use – and there have already been some legislative skirmishes – they would open up their citizens to quite a range of avoidable harms. And that’s before you stop to remember that the European Court of Justice struck down the Data Retention Directive on the grounds that such bulk surveillance, without warrant or suspicion, was a grossly disproportionate infringement on privacy, even in the fight against terrorism. A client-side scanning mandate would invite the same fate.

But ‘if you build it, they will come’. If device vendors are compelled to install remote surveillance, the demands will start to roll in. Who could possibly be so cold-hearted as to argue against the system being extended to search for missing children? Then President Xi will want to know who has photos of the Dalai Lama, or of men standing in front of tanks; and copyright lawyers will get court orders blocking whatever they claim infringes their clients’ rights. Our phones, which have grown into extensions of our intimate private space, will be ours no more; they will be private no more; and we will all be less secure.

WEIS 2021 – Liveblog

Academic papers, Security economics, Security psychology

I’ll be trying to liveblog the twentieth Workshop on the Economics of Information Security (WEIS), which is being held online today and tomorrow (June 28/29). The event was introduced by the co-chairs Dann Arce and Tyler Moore. 38 papers were submitted, and 15 accepted. My summaries of the sessions of accepted papers will appear as followups to this post; there will also be a panel session on the 29th, followed by a rump session for late-breaking results. (Added later: videos of the sessions are linked from the start of the followups that describe them.)

Cybercrime gangs as tech startups

Academic papers, Cybercrime, Security economics

In our latest paper, we propose a better way of analysing cybercrime.

Crime has been moving online, like everything else, for the past 25 years, and for the past decade or so it’s accounted for more than half of all property crimes in developed countries. Criminologists have tried to apply their traditional tools and methods to measure and understand it, yet even when these research teams include technologists, it always seems that there’s something missing. The people who phish your bank credentials are just not the same people who used to burgle your house. They have different backgrounds, different skills and different organisation.

We believe a missing factor is entrepreneurship. Cyber-crooks are running tech startups, and face the same problems as other tech entrepreneurs. There are preconditions that create the opportunity. There are barriers to entry to be overcome. There are pathways to scaling up, and bottlenecks that inhibit scaling. There are competitive factors, whether competing crooks or motivated defenders. And finally there may be saturation mechanisms that inhibit growth.

One difference with regular entrepreneurship is the lack of finance: a malware gang can’t raise VC to develop a cool new idea, or cash out by means on an IPO. They have to use their profits not just to pay themselves, but also to invest in new products and services. In effect, cybercrooks are trying to run a tech startup with the financial infrastructure of an ice-cream stall.

We have developed this framework from years of experience dealing with many types of cybercrime, and it appears to prove a useful way of analysing new scams, so we can spot those developments which, like ransomware, are capable of growing into a real problem.

Our paper Silicon Den: Cybercrime is Entrepreneurship will appear at WEIS on Monday.

Security engineering and machine learning

Academic papers, Hardware & signals, Security economics, Security engineering, Seminars

Last week I gave my first lecture in Edinburgh since becoming a professor there in February. It was also the first talk I’ve given in person to a live audience since February 2020.

My topic was the interaction between security engineering and machine learning. Many of the things that go wrong with machine-learning systems were already familiar in principle, as we’ve been using Bayesian techniques in spam filters and fraud engines for almost twenty years. Indeed, I warned about the risks of not being able to explain and justify the decisions of neural networks in the second edition of my book, back in 2008.

However the deep neural network (DNN) revolution since 2012 has drawn in hundreds of thousands of engineers, most of them without this background. Many fielded systems are extremely easy to break, often using tricks that have been around for years. What’s more, new attacks specific to DNNs – adversarial samples – have been found to exist for pretty well all models. They’re easy to find, and often transferable from one model to another.

I describe a number of new attacks and defences that we’ve discovered in the past three years, including the Taboo Trap, sponge attacks, data ordering attacks and markpainting. I argue that we will usually have to think of defences at the system level, rather than at the level of individual components; and that situational awareness is likely to play an important role.

Here now is the video of my talk.

A new way to detect ‘deepfake’ picture editing

Academic papers, Internet censorship, News coverage, Security engineering, Useful software

Common graphics software now offers powerful tools for inpainting – using machine-learning models to reconstruct missing pieces of an image. They are widely used for picture editing and retouching, but like many sophisticated tools they can also be abused. They can remove someone from a picture of a crime scene, or remove a watermark from a stock photo. Could we make such abuses more difficult?

We introduce Markpainting, which uses adversarial machine-learning techniques to fool the inpainter into making its edits evident to the naked eye. An image owner can modify their image in subtle ways which are not themselves very visible, but will sabotage any attempt to inpaint it by adding visible information determined in advance by the markpainter.

One application is tamper-resistant marks. For example, a photo agency that makes stock photos available on its website with copyright watermarks can markpaint them in such a way that anyone using common editing software to remove a watermark will fail; the copyright mark will be markpainted right back. So watermarks can be made a lot more robust.

In the fight against fake news, markpainting news photos would mean that anyone trying to manipulate them would risk visible artefacts. So bad actors would have to check and retouch photos manually, rather than trying use inpainting tools to automate forgery at scale.

This paper has been accepted at ICML.

Robots, manners and stress

Academic papers, Legal issues, Politics, Security engineering

Humans and other animals have evolved to be aware of whether we’re under threat. When we’re on safe territory with family and friends we relax, but when we sense that a rival or a predator might be nearby, our fight-or-flight response kicks in. Situational awareness is vital, as it’s just too stressful to be alert all the time.

We’ve started to realise that this is likely to be just as important in many machine-learning applications. Take as an example machine vision in an automatic driver assistance system, whose goal is automatic lane keeping and automatic emergency braking. Such systems use deep neural networks, as they perform way better than the alternatives; but they can be easily fooled by adversarial examples. Should we worry? Sure, a bad person might cause a car crash by projecting a misleading image on a motorway bridge – but they could as easily steal some traffic cones from the road works. Nobody sits up at night worrying about that. But the car industry does actually detune vision systems from fear of deceptive attacks!

We therefore started a thread of research aimed at helping machine-learning systems detect whether they’re under attack. Our first idea was the Taboo Trap. You raise your kids to observe social taboos – to behave well and speak properly – and yet once you send them to school they suddenly know words that would make your granny blush. The taboo violation shows they’ve been exposed to ‘adversarial inputs’, as an ML engineer would call them. So we worked out how to train a neural network to avoid certain taboo values, both of outputs (forbidden utterances) and intermediate activations (forbidden thoughts). The taboos can be changed every time you retrain the network, giving the equivalent of a cryptographic key. Thus even though adversarial samples will always exist, you can make them harder to find; an attacker can’t just find one that works against one model of car and use it against every other model. You can take a view, based on risk, of how many different keys you need.

We then showed how you can also attack the availability of neural networks using sponge examples – inputs designed to soak up as much energy, and waste as much time, as possible. An alarm can be simpler to build in this case: just monitor how long your classifier takes to run.

Are there broader lessons? We suspect so. As robots develop situational awareness, like humans, and react to real or potential attacks by falling back to a more cautious mode of operation, a hostile environment will cause the equivalent of stress. Sometimes this will be deliberate; one can imagine constant low-level engagement between drones at tense national borders, just as countries currently probe each others’ air defences. But much of the time it may well be a by-product of poor automation design coupled with companies hustling aggressively for consumers’ attention.

This suggests a missing factor in machine-learning research: manners. We’ve evolved manners to signal to others that our intent is not hostile, and to negotiate the many little transactions that in a hostile environment might lead to a tussle for dominance. Yet these are hard for robots. Food-delivery robots can become unpopular for obstructing and harassing other pavement users; and one of the show-stoppers for automated driving is the difficulty that self-driving cars have in crossing traffic, or otherwise negotiating precedence with other road users. And even in the military, manners have a role – from the chivalry codes of medieval knights to the more modern protocols whereby warships and warplanes warn other craft before opening fire. If we let loose swarms of killer drones with no manners, conflict will be more likely.

Our paper Situational Awareness and Machine Learning – Robots, Manners and Stress was invited as a keynote for two co-located events: IEEE CogSIMA and the NATO STO SCI-341 Research Symposium on Situation awareness of Swarms and Autonomous systems. We got so many conflicting demands from the IEEE that we gave up on making a video of the talk for them, and our paper was pulled from their proceedings. However we decided to put the paper online for the benefit of the NATO folks, who were blameless in this matter.

COVID-19 test provider websites and Cybersecurity: COVID briefing #22

Academic papers, Cybercrime, Web security

This week’s COVID briefing paper (COVIDbriefing-22.pdf) resumes the Cybercrime Centre’s COVID briefing series, which began in July 2020 with the aim of sharing short on-going updates on the impacts of the pandemic on cybercrime.

The reason for restarting this series is a recent personal experience while navigating through the government’s requirements on COVID-19 testing for international travel. I observed great variation in the quality of website design and cannot help but put on my academic hat to report on what I found.

The quality of some websites is so poor that it hard to distinguish them from fraudulent sites — that is they have many of the features and characteristics that consumers have been warned to pay attention to. Compounded with the requirement to provide personally identifiable information there is a risk that fraudulent sites will indeed spring up and it will be unsurprising if consumers are fooled.

The government needs to set out minimum standards for the websites of firms that they approve to provide COVID-19 testing — especially with the imminent growth in demand that will come as the UK’s travel rules are eased.