Category Archives: Security economics

Social-science angles of security

The Dynamics of Industry-wide Disclosure

Academic papers, Open-source security, Programming languages, Security economics, Usability

Last year, we disclosed two related vulnerabilities that broke a wide range of systems. In our Bad Characters paper, we showed how to use Unicode tricks – such as homoglyphs and bidi characters – to mislead NLP systems. Our Trojan Source paper showed how similar tricks could be used to make source code look one way to a human reviewer, and another way to a compiler, opening up a wide range of supply-chain attacks on critical software. Prior to publication, we disclosed our findings to four suppliers of large NLP systems, and nineteen suppliers of software development tools. So how did industry respond?

We were invited to give the keynote talk this year at LangSec, and the video is now available. In it we describe not just the Bad Characters and Trojan Source vulnerabilities, but the large natural experiment created by their disclosure. The Trojan Source vulnerability affected most compilers, interpreters, code editors and code repositories; this enabled us to compare responses by firms versus nonprofits and by firms that managed their own response versus those who outsourced it. The interaction between bug bounty programs, government disclosure assistance, peer review and press coverage was interesting. Most of the affected development teams took action, though some required a bit of prodding.

The response by the NLP maintainers was much less enthusiastic. By the time we gave this talk, only Google had done anything – though we now hear that Microsoft is now also working on a fix. The reasons for this responsibility gap need to be understood better. They may include differences in culture between C coders and data scientists; the greater costs and delays in the build-test-deploy cycle for large ML models; and the relative lack of press interest in attacks on ML systems. If many of our critical systems start to include ML components that are less maintainable, will the ML end up being the weakest link?

European Commission prefers breaking privacy to protecting kids

Academic papers, Internet censorship, Legal issues, News coverage, Politics, Security economics

Today, May 11, EU Commissioner Ylva Johannson announced a new law to combat online child sex abuse. This has an overt purpose, and a covert purpose.

The overt purpose is to pressure tech companies to take down illegal material, and material that might possibly be illegal, more quickly. A new agency is to be set up in the Hague, modeled on and linked to Europol, to maintain an official database of illegal child sex-abuse images. National authorities will report abuse to this new agency, which will then require hosting providers and others to take suspect material down. The new law goes into great detail about the design of the takedown process, the forms to be used, and the redress that content providers will have if innocuous material is taken down by mistake. There are similar provisions for blocking URLs; censorship orders can be issued to ISPs in Member States.

The first problem is that this approach does not work. In our 2016 paper, Taking Down Websites to Prevent Crime, we analysed the takedown industry and found that private firms are much better at taking down websites than the police. We found that the specialist contractors who take down phishing websites for banks would typically take six hours to remove an offending website, while the Internet Watch Foundation – which has a legal monopoly on taking down child-abuse material in the UK – would often take six weeks.

We have a reasonably good understanding of why this is the case. Taking down websites means interacting with a great variety of registrars and hosting companies worldwide, and they have different ways of working. One firm expects an encrypted email; another wants you to open a ticket; yet another needs you to phone their call centre during Peking business hours and speak Mandarin. The specialist contractors have figured all this out, and have got good at it. However, police forces want to use their own forms, and expect everyone to follow police procedure. Once you’re outside your jurisdiction, this doesn’t work. Police forces also focus on process more than outcome; they have difficulty hiring and retaining staff to do detailed technical clerical work; and they’re not much good at dealing with foreigners.

Our takedown work was funded by the Home Office, and we recommended that they run a randomised controlled trial where they order a subset of UK police forces to use specialist contractors to take down criminal websites. We’re still waiting, six years later. And there’s nothing in UK law that would stop them running such a trial, or that would stop a Chief Constable outsourcing the work.

So it’s really stupid for the European Commission to mandate centralised takedown by a police agency for the whole of Europe. This will be make everything really hard to fix once they find out that it doesn’t work, and it becomes obvious that child abuse websites stay up longer, causing real harm.

Oh, and the covert purpose? That is to enable the new agency to undermine end-to-end encryption by mandating client-side scanning. This is not evident on the face of the bill but is evident in the impact assessment, which praises Apple’s 2021 proposal. Colleagues and I already wrote about that in detail, so I will not repeat the arguments here. I will merely note that Europol coordinates the exploitation of communications systems by law enforcement agencies, and the Dutch National High-Tech Crime Unit has developed world-class skills at exploiting mobile phones and chat services. The most recent case of continent-wide bulk interception was EncroChat; although reporting restrictions prevent me telling the story of that, there have been multiple similar cases in recent years.

So there we have it: an attack on cryptography, designed to circumvent EU laws against bulk surveillance by using a populist appeal to child protection, appears likely to harm children instead.

Security engineering course

Banking security, Cybercrime, Operating systems, Privacy technology, Security economics, Security engineering

This week sees the start of a course on security engineering that Sam Ainsworth and I are teaching. It’s based on the third edition of my Security Engineering book, and is a first cut at a ‘film of the book’.

Each week we will put two lectures online, and here are the first two. Lecture 1 discusses our adversaries, from nation states through cyber-crooks to personal abuse, and the vulnerability life cycle that underlies the ecosystem of attacks. Lecture 2 abstracts this empirical experience into more formal threat models and security policies.

Although our course is designed for masters students and fourth-year undergrads in Edinburgh, we’re making the lectures available to everyone. I’ll link the rest of the videos in followups here, and eventually on the book’s web page.

WEIS 2022 call for papers

Call for papers, Cybercrime, Politics, Security economics, Security psychology

The 2022 Workshop on the Economics of Information Security will be held at Tulsa, Oklahoma, on 21-22 June 2022. Paper submissions are due by 28 February 2022. After two virtual events we’re eager to get back to meeting in person if we possibly can.

The program chairs for 2022 are Sadia Afroz and Laura Brandimarte, and here is the call for papers.

We originally set this as 20-21, being unaware that June 20 is the Juneteenth holiday in the USA. Sorry about that.

Anyway, we hope to see lots of you in Tulsa!

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.

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

Cybercrime, Internet censorship, Legal issues, News coverage, Operating systems, Privacy technology, Security economics, Security engineering, Security psychology

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

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.