Bugs in our pockets?

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.

EPSRC and InnovateUK launch £8M Digital Security by Design – CHERI/Morello Software Ecosystem funding call

For a bit over a decade, SRI International and the University of Cambridge have been working to develop CHERI (Capability Hardware Enhanced RISC Instructions), a set of processor-architecture security extensions targeting vulnerability mitigation through memory safety and software compartmentalisation. In 2019, the UK’s Industrial Strategy Challenge Fund announced the £187M Digital Security by Design (DSbD) programme, which is supporting the creation Arm’s experimental CHERI-based Morello processor, System-on-Chip (SoC), and board shipping in early 2022, as well as dozens of industrial and academic projects to explore and develop CHERI-based software security. This week, UKRI will be launching an £8M funding call via EPSRC and InnovateUK to support UK-based academic and industrial CHERI/Morello software ecosystem development work. They are particularly interested in supporting work in the areas of OS and developer toolchain, libraries and packages, language runtimes, frameworks and middleware, and platform services on open-source operating systems — all key areas to expand the breadth and maturity of CHERI-enabled software. There is a virtual briefing event taking place on 5 October 2021, with proposals due on 8 December 2021.

CHERI Software Release for Summer 2021

The CHERI project at SRI International and the University of Cambridge are pleased to announce our second CHERI reference software-stack release. The release supports the CHERI-RISC-V and Arm Morello architectures. A complete set of development tools, including compiler, OS, debugger, and emulator are included in the release.

For this second release, the software has been packaged to be used in an easily accessible Docker image, hosted on Docker Hub. The focus of the release has been on CheriBSD, with the merging of pure capability kernels and Morello support to the development mainline. There are also many continued improvements to tooling, including updates to cheribuild (our unified build system), the CHERI Clang/LLVM compiler suite, the CHERI-extended GNU Debugger (gdb), and the QEMU full-system emulator (which now supports both the CHERI-RISC-V and Morello architectures).

The CHERI protection model provides architectural primitives to protect computer systems from widely-exploited security vulnerabilities. CHERI revises the hardware/software architectural interface with hardware support for capabilities that can be used for fine-grained memory protection and scalable software compartmentalization. Supported by DARPA (the US
Defense Advanced Research Projects agency) as well as UKRI (UK Research and Innovation) and its Digital Security by Design (DSbD) program, CHERI is the work of a large research team at the University of Cambridge, SRI International, Arm and many industrial and academic collaborators throughout the world.

The release along with full release notes are now available on line at https://cheri-dist.cl.cam.ac.uk/

Is Apple’s NeuralMatch searching for abuse, or for people?

Apple stunned the tech industry on Thursday by announcing that the next version of iOS and macOS will contain a neural network to scan photos for sex abuse. Each photo will get an encrypted ‘safety voucher’ saying whether or not it’s suspect, and if more than about ten suspect photos are backed up to iCloud, then a clever cryptographic scheme will unlock the keys used to encrypt them. Apple staff or contractors can then look at the suspect photos and report them.

We’re told that the neural network was trained on 200,000 images of child sex abuse provided by the US National Center for Missing and Exploited Children. Neural networks are good at spotting images “similar” to those in their training set, and people unfamiliar with machine learning may assume that Apple’s network will recognise criminal acts. The police might even be happy if it recognises a sofa on which a number of acts took place. (You might be less happy, if you own a similar sofa.) Then again, it might learn to recognise naked children, and flag up a snap of your three-year-old child on the beach. So what the new software in your iPhone actually recognises is really important.

Now the neural network described in Apple’s documentation appears very similar to the networks used in face recognition (hat tip to Nicko van Someren for spotting this). So it seems a fair bet that the new software will recognise people whose faces appear in the abuse dataset on which it was trained.

So what will happen when someone’s iPhone flags ten pictures as suspect, and the Apple contractor who looks at them sees an adult with their clothes on? There’s a real chance that they’re either a criminal or a witness, so they’ll have to be reported to the police. In the case of a survivor who was victimised ten or twenty years ago, and whose pictures still circulate in the underground, this could mean traumatic secondary victimisation. It might even be their twin sibling, or a genuine false positive in the form of someone who just looks very much like them. What processes will Apple use to manage this? Not all US police forces are known for their sensitivity, particularly towards minority suspects.

But that’s just the beginning. Apple’s algorithm, NeuralMatch, stores a fingerprint of each image in its training set as a short string called a NeuralHash, so new pictures can easily be added to the list. Once the tech is built into your iPhone, your MacBook and your Apple Watch, and can scan billions of photos a day, there will be pressure to use it for other purposes. The other part of NCMEC’s mission is missing children. Can Apple resist demands to help find runaways? Could Tim Cook possibly be so cold-hearted as to refuse at add Madeleine McCann to the watch list?

After that, your guess is as good as mine. Depending on where you are, you might find your photos scanned for dissidents, religious leaders or the FBI’s most wanted. It also reminds me of the Rasterfahndung in 1970s Germany – the dragnet search of all digital data in the country for clues to the Baader-Meinhof gang. Only now it can be done at scale, and not just for the most serious crimes either.

Finally, there’s adversarial machine learning. Neural networks are fairly easy to fool in that an adversary can tweak images so they’re misclassified. Expect to see pictures of cats (and of Tim Cook) that get flagged as abuse, and gangs finding ways to get real abuse past the system. Apple’s new tech may end up being a distributed person-search machine, rather than a sex-abuse prevention machine.

Such a technology requires public scrutiny, and as the possession of child sex abuse images is a strict-liability offence, academics cannot work with them. While the crooks will dig out NeuralMatch from their devices and play with it, we cannot. It is possible in theory for Apple to get NeuralMatch to ignore faces; for example, it could blur all the faces in the training data, as Google does for photos in Street View. But they haven’t claimed they did that, and if they did, how could we check? Apple should therefore publish full details of NeuralMatch plus a set of NeuralHash values trained on a public dataset with which we can legally work. It also needs to explain how the system it deploys was tuned and tested; and how dragnet searches of people’s photo libraries will be restricted to those conducted by court order so that they are proportionate, necessary and in accordance with the law. If that cannot be done, the technology must be abandoned.

WEIS 2021 – Liveblog

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

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

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.

Hiring for iCrime

We are hiring two Research Assistants/Associates to work on the ERC-funded Interdisciplinary Cybercrime Project (iCrime). We are looking to appoint one computer scientist and one social scientist to work in an interdisciplinary team reporting to Dr Alice Hutchings.

iCrime incorporates expertise from criminology and computer science to research cybercrime offenders, their crime type, the place (such as online black markets), and the response. We will map out the pathways of cybercrime offenders and the steps and skills required to successfully undertake complex forms of cybercrime. We will analyse the social dynamics and economies surrounding cybercrime markets and forums. We will use our findings to inform crime prevention initiatives and use experimental designs to evaluate their effects.

Within iCrime, we will develop tools to identify and measure criminal infrastructure at scale. We will use and develop unique datasets and design novel methodologies. This is particularly important as cybercrime changes dynamically. Overall, our approach will be evaluative, critical, and data driven.

If you’re a computer scientist, please follow the link at: https://www.jobs.cam.ac.uk/job/30100/

If you’re a social scientist, please follow the link at: https://www.jobs.cam.ac.uk/job/30099/

Please read the formal advertisements for the details about exactly who and what we’re looking for and how to apply — and please pay special attention to our request for a covering letter!

10/06/21 Edited to add new links

A new way to detect ‘deepfake’ picture editing

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.