Category Archives: Operating systems

Arm releases experimental CHERI-enabled Morello board as part of £187M UKRI Digital Security by Design programme

CHERI, CL frontpage, Open-source security, Operating systems, Processors, Programming languages, Uncategorized

Professor Robert N. M. Watson (Cambridge), Professor Simon W. Moore (Cambridge), Professor Peter Sewell (Cambridge), Dr Jonathan Woodruff (Cambridge), Brooks Davis (SRI), and Dr Peter G. Neumann (SRI)

After over a decade of research creating the CHERI protection model, hardware, software, and formal models and proofs, developed over three DARPA research programmes, we are at a truly exciting moment. Today, Arm announced first availability of its experimental CHERI-enabled Morello processor, System-on-Chip, and development board – an industrial quality and industrial scale demonstrator of CHERI merged into a high-performance processor design. Not only does Morello fully incorporate the features described in our CHERI ISAv8 specification to provide fine-grained memory protection and scalable software compartmentalisation, but it also implements an Instruction-Set Architecture (ISA) with formally verified security properties. The Arm Morello Program is supported by the £187M UKRI Digital Security by Design (DSbD) research programme, a UK government and industry-funded effort to transition CHERI towards mainstream use.

Continue reading Arm releases experimental CHERI-enabled Morello board as part of £187M UKRI Digital Security by Design programme →

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.

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.

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.

SRI and Cambridge release CHERI software stack for Arm Morello

CL frontpage, Open-source security, Operating systems, Processors, ,

For the last ten years, SRI International and the University of Cambridge have been working to develop CHERI (Capability Hardware Enhanced RISC Instructions), a DARPA-sponsored processor architecture security technology implementing efficient fine-grained memory protection and scalable software compartmentalization. You can learn more about CHERI in our Introduction to CHERI technical report, which describes the architectural, microarchitectural, formal modelling, and software approaches we have created.

For the last six of those years, we have been collaborating closely with Arm to create an adaptation of CHERI to the ARMv8-A architecture, which is slated to appear in Arm’s prototype Morello processor, System-on-Chip (SoC), and board in Q1 2022. Richard Grisenthwaite, Arm’s Principal Architect, announced this joint work at the UKRI Digital Security by Design (DSbD) workshop in September 2019. DSbD is a UKRI / Industrial Strategy Challenge Fund (ISCF) research programme contributing to the creation of the Morello board, and CHERI is the Digital Security by Design Technology that underlies the programme. Our collaboration with Arm has been an enormously exciting experience, involving daily engagement Arm’s architects, microarchitects, and software designers. This included hosting several members of Arm’s team at our lab in Cambridge over multiple years, as we brought together our long-term research on architectural and software security with their experience in industrial architecture, processor designs, and transition.

Today, Richard Grisenthwaite announced that Arm is releasing their first simulator for the Morello architecture, the Morello FVP (Fixed Virtual Platform), and also an open-source software stack that includes their adaptation of our CHERI Clang/LLVM to Morello and early work on Morello support for Android. These build on the Morello architecture specification, released in late September 2020. SRI and Cambridge are releasing a first developer preview release of the CHERI reference software stack ported to Morello – intended to show a rich integration of CHERI into a contemporary OS design, as well as demonstration applications. This stack includes CheriBSD, a BSD-licensed reference design and open-source applications adapted to CHERI including OpenSSH, nginx, and WebKit.

For this first developer preview release, we have focused on bringing CHERI C/C++ memory protection to Morello. Our CheriABI process environment, which allows the full UNIX userspace to run with fine-grained spatial memory safety, is fully functional on Morello. This work has been the recent subject of a report from the Microsoft Security Response Center (MSRC), Microsoft’s internal red team and security response organization, describing how CHERI has to potential to deterministically prevent over 2/3 of critical Microsoft software security vulnerabilities. CheriBSD/Morello brings that work over from our research CHERI-MIPS and CHERI-RISC-V platforms to Arm’s Morello. We demonstrated CheriBSD/Morello mitigating several memory-safety vulnerabilities in the EPSRC Digital Security by Design (DSbD) workshop yesterday, talking to 9 UK universities that have been funded to do research building on CHERI and Morello.

We have an aggressive planned quarterly release schedule through the end of 2021 when a full release will ship alongside the Morello board, adapting various CheriBSD security features to Morello:

DateReleaseKey features
October 2020Developer PreviewCheriABI pure-capability userspace implementing spatial memory safety.
December 2020Update 1Pure-capability kernel implementing spatial memory safety.
March 2021Update 2Userspace heap temporal memory safety based on Cornucopia (in collaboration with Microsoft Research).
June 2021Update 3Userspace software compartmentalization based on the CHERI co-process model.
October 2021Update 4Userspace software compartmentalization based on a run-time linker model.
Late 2021Full releaseAny updates required to operate well on the shipping Morello board.
CHERI software stack – working release schedule for 2020-2021

Getting started with CheriBSD/Morello is easy (if you have a tolerance for experimental architectural simulators, experimental operating systems, and experimental compilers!). Visit our CHERI Morello software web page to learn more about this work, and then our CheriBSD/Morello distribution page to download our build environment. You can automatically install Arm’s FVP, cross-develop in our docker-based SDK on macOS or Linux, and SSH into the simulated host to try things out.

CHERI is the work of a large research team at SRI International and the University of Cambridge, as well as numerous industrial collaborators at Arm, Google, Microsoft, and elsewhere. My co-investigators, Peter G. Neumann (SRI), Simon W. Moore (Cambridge), Peter Sewell (Cambridge), and I are immensely grateful for their contributions: CHERI would simply not have been possible without your collective effort – thank you! We are also grateful to our sponsors over an extended period, including DARPA, UKRI, Google, and Arm.

UKRI Digital Security by Design: A £190M research programme around Arm’s Morello – an experimental ARMv8-A CPU, SoC, and board with CHERI support

Open-source security, Operating systems, Processors, Programming languages, Uncategorized, ,

PIs: Robert N. M. Watson (Cambridge), Simon W. Moore (Cambridge), Peter Sewell (Cambridge), and Peter G. Neumann (SRI)

Since 2010, SRI International and the University of Cambridge, supported by DARPA, have been developing CHERI: a capability-system extension to RISC Instruction-Set Architectures (ISAs) supporting fine-grained memory protection and scalable compartmentalization .. while retaining incremental deployability within current C and C++ software stacks. This ten-year research project has involved hardware-software-semantic co-design: FPGA prototyping, compiler development, operating-system development, and application adaptation, as well as formal modeling and proof. Extensively documented in technical reports and research papers, we have iterated on CHERI as we evaluated and improved microarchitectural overheads, performance, software compatibility, and security.

As we know, mainstream computer systems are still chronically insecure. One of the main reasons for this is that conventional hardware architectures and C/C++ language abstractions, dating back to the 1970s, provide only coarse-grained memory protection. Without memory safety, many coding errors turn into exploitable security vulnerabilities. In our ASPLOS 2019 paper on CheriABI (best paper award), we demonstrated that a complete UNIX userspace and application suite could be protected by strong memory safety with minimal source-code disruption and acceptable performance overheads. Scalable software compartmentalization offers mitigation for future unknown classes of vulnerabilities by enabling greater use of design patterns such as software sandboxing. Our An Introduction to CHERI technical report introduces our approach including the architecture, microarchitectural contributions, formal models, software protection model, and practical software adaptation. The CHERI ISA v7 specification is the authoritative reference to the architecture, including both the architecture-neutral protection model and its concrete mappings into the 64-bit MIPS and 32/64-bit RISC-V ISAs. Our Rigorous Engineering technical report describes our modelling and mechanised proof of key security properties.

Today, we are very excited to be able to talk about another long-running aspect of our DARPA-supported work: A collaboration since 2014 with engineers at Arm to create an experimental adaptation of CHERI to the ARMv8-A architecture. This widely used ISA is the foundation for the vast majority of mobile phones and tablets, including those running iOS and Android. The £170M UKRI program Digital Security by Design (DSbD) was announced in late September 2019 to explore potential applications of CHERI — with a £70M investment by UKRI, and a further £117M from industry including involvement by Arm, Microsoft, and Google. Today, UKRI and Arm announced that the Arm Morello board will become available from 2021: Morello is a prototype 7nm high-end multi-core superscalar ARMv8-A processor (based on Arm’s Neoverse N1), SoC, and board implementing experimental CHERI extensions. As part of this effort, the UK Engineering and Physical Sciences Research Council (EPSRC) has also announced a new £8M programme to fund UK academics to work with Morello. Arm will release their Morello adaptation of our CHERI Clang/LLVM toolchain, and we will release a full adaptation of our open-source CHERI reference software stack to Morello (including our CheriBSD operating system and application suite) as foundations for research and prototyping on Morello. Watch the DSbD workshop videos from Robert Watson (Cambridge), Richard Grisenthwaite (Arm), and Manuel Costa (Microsoft) on CHERI and Morello, which are linked below, for more information.

This is an incredible opportunity to validate the CHERI approach, with accompanying systems software and formal verification, through an industrial scale and industrial quality hardware design, and to broaden the research community around CHERI to explore its potential impact. You can read the announcements about Morello here:

Recordings of several talks on CHERI and Morello are now available from the ISCF Digital Security by Design Challenge Collaborators’ Workshop (26 September 2019), including:

  • Robert Watson (Cambridge)’s talk on CHERI, and on our transition collaboration with Arm (video) (slides)
  • Richard Grisenthwaite (Arm)’s talk on the Morello board and CHERI transition (video) (slides)
  • Manuel Costa (Microsoft)’s talk on memory safety and potential opportunities arising with CHERI and Morello (video)

In addition, we are maintaining a CHERI DSbD web page with background information on CHERI, announcements regarding Morello, links to DSbD funding calls, and information regarding software artefacts, formal models, and so on. We will continue to update that page as the programme proceeds.

This has been possible through the contributions of the many members of the CHERI research team over the last ten years, including: Hesham Almatary, Jonathan Anderson, John Baldwin, Hadrien Barrel, Thomas Bauereiss, Ruslan Bukin, David Chisnall, James Clarke, Nirav Dave, Brooks Davis, Lawrence Esswood, Nathaniel W. Filardo, Khilan Gudka, Brett Gutstein, Alexandre Joannou, Robert Kovacsics, Ben Laurie, A. Theo Markettos, J. Edward Maste, Marno van der Maas, Alfredo Mazzinghi, Alan Mujumdar, Prashanth Mundkur, Steven J. Murdoch, Edward Napierala, Kyndylan Nienhuis, Robert Norton-Wright, Philip Paeps, Lucian Paul-Trifu, Alex Richardson, Michael Roe, Colin Rothwell, Peter Rugg, Hassen Saidi, Stacey Son, Domagoj Stolfa, Andrew Turner, Munraj Vadera, Jonathan Woodruff, Hongyan Xia, and Bjoern A. Zeeb.

Approved for public release; distribution is unlimited. This work was supported by the Defense Advanced Research Projects Agency (DARPA) and the Air Force Research Laboratory (AFRL), under contract FA8750-10-C-0237 (CTSRD), with additional support from FA8750-11-C-0249 (MRC2), HR0011-18-C-0016 (ECATS), and FA8650-18-C-7809 (CIFV) as part of the DARPA CRASH, MRC, and SSITH research programs. The views, opinions, and/or findings contained in this report are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. We also acknowledge the EPSRC REMS Programme Grant (EP/K008528/1), the ERC ELVER Advanced Grant (789108), the Isaac Newton Trust, the UK Higher Education Innovation Fund (HEIF), Thales E-Security, Microsoft Research Cambridge, Arm Limited, Google, Google DeepMind, HP Enterprise, and the Gates Cambridge Trust.

The lifetime of an Android API vulnerability

Open-source security, Operating systems, ,

By Daniel Carter, Daniel Thomas, and Alastair Beresford

Security updates are an important mechanism for protecting users and their devices from attack, and therefore it’s important vendors produce security updates, and that users apply them. Producing security updates is particularly difficult when more than one vendor needs to make changes in order to secure a system.

We studied one such example in previous research (open access). The specific vulnerability (CVE-2012-6636) affected Android devices and allowed JavaScript running inside a WebView of an app (e.g. an advert) to run arbitrary code inside the app itself, with all the permissions of app. The vulnerability could be exploited remotely by an attacker who bought ads which supported JavaScript. In addition, since most ads at the time were served over HTTP, the vulnerability could also be exploited if an attacker controlled a network used by the Android device (e.g. WiFi in a coffee shop). The fix required both the Android operating system, and all apps installed on the handset, to support at least Android API Level 17. Thus, the deployment of an effective solution for users was especially challenging.

When we published our paper in 2015, we predicted that this vulnerability would not be patched on 95% of devices in the Android ecosystem until January 2018 (plus or minus a standard deviation of 1.23 years). Since this date has now passed, we decided to check whether our prediction was correct.

To perform our analysis we used data on deployed API versions taken from (almost) monthly snapshots of Google’s Android Distribution Dashboard which we have been tracking. The good news is that we found the operating system update requirements crossed the 95% threshold in May 2017, seven months earlier than our best estimate, and within one standard deviation of our prediction. The most recent data for May 2019 shows deployment has reached 98.2% of devices in use. Nevertheless, fixing this aspect of the vulnerability took well over 4 years to reach 95% of devices.

Proportion of devices safe from the JavaScript-to-Java vulnerability. For details how this is calculated, see our previous paper.
Proportion of devices safe from the JavaScript-to-Java vulnerability. For details how this is calculated, see our previous paper.

Google delivered a further fix in Android 4.4.3 that blocked access to the getClass method from JavaScript, considerably reducing the risk of exploitation even from apps which were not updated. A conservative estimate of the deployment of this further fix is shown on the graph, reaching 95% adoption in April 2019. On the app side of things, Google has also been encouraging app developers to update. From 1st November 2018, updates to apps on Google Play must target API level 26 or higher and from November 1st 2019 updates to apps must target API level 28 or higher. This change forces the app-side changes necessary to fix this vulnerability. Unfortunately we don’t have good data on the distribution of apps installed on handsets, but we expect that most Android devices are now secure against this vulnerability.

Our work is not done however, and we are still looking into the security of mobile devices. This summer we are extending the work from our other 2015 paper Security Metrics for the Android Ecosystem where we analysed the composition of Android vulnerabilities. Last time we used distributions of deployed Android versions on devices from Device Analyzer (an Android measurement app we deployed to Google Play), the device management system of a FTSE 100 company, and User-Agent string data from an ISP in Rwanda. If you might be able to share similar data with us to support our latest research work then please get in touch: contact@androidvulnerabilities.org.

Could a gaming app steal your bank PIN?

Academic papers, Banking security, Hardware & signals, Operating systems, Privacy technology, Security engineering

Have you ever wondered whether one app on your phone could spy on what you’re typing into another? We have. Five years ago we showed that you could use the camera to measure the phone’s motion during typing and use that to recover PINs. Then three years ago we showed that you could use interrupt timing to recover text entered using gesture typing. So what other attacks are possible?

Our latest paper shows that one of the apps on the phone can simply record the sound from its microphones and work out from that what you’ve been typing.

Your phone’s screen can be thought of as a drum – a membrane supported at the edges. It makes slightly different sounds depending on where you tap it. Modern phones and tablets typically have two microphones, so you can also measure the time difference of arrival of the sounds. The upshot is that can recover PIN codes and short words given a few measurements, and in some cases even long and complex words. We evaluate the new attack against previous ones and show that the accuracy is sometimes even better, especially against larger devices such as tablets.

This paper is based on Ilia Shumailov’s MPhil thesis project.

Struck by a Thunderbolt

Academic papers, Hardware & signals, Operating systems, Processors

At the Network and Distributed Systems Security Symposium in San Diego today we’re presenting Thunderclap, which describes a set of new vulnerabilities involving the security of computer peripherals and the open-source research platform used to discover them. This is a joint work with Colin Rothwell, Brett Gutstein, Allison Pearce, Peter Neumann, Simon Moore and Robert Watson.

We look at the security of input/output devices that use the Thunderbolt interface, which is available via USB-C ports in many modern laptops. Our work also covers PCI Express (PCIe) peripherals which are found in desktops and servers.

Such ports offer very privileged, low-level, direct memory access (DMA), which gives peripherals much more privilege than regular USB devices. If no defences are used on the host, an attacker has unrestricted memory access, and can completely take control of a target computer: they can steal passwords, banking logins, encryption keys, browser sessions and private files, and they can also inject malicious software that can run anywhere in the system.

We studied the defences of existing systems in the face of malicious DMA-enabled peripheral devices and found them to be very weak.

The primary defence is a component called the Input-Output Memory Management Unit (IOMMU), which, in principle, can allow devices to access only the memory needed to do their job and nothing else. However, we found existing operating systems do not use the IOMMU effectively.

To begin with, most systems don’t enable the IOMMU at all. Windows 7, Windows 8, and Windows 10 Home and Pro didn’t support the IOMMU. Windows 10 Enterprise can optionally use it, but in a very limited way that leaves most of the system undefended. Linux and FreeBSD do support using the IOMMU, but this support is not enabled by default in most distributions. MacOS is the only OS we studied that uses the IOMMU out of the box.

This state of affairs is not good, and our investigations revealed significant further vulnerabilities even when the IOMMU is enabled.

We built a fake network card that is capable of interacting with the operating system in the same way as a real one, including announcing itself correctly, causing drivers to attach, and sending and receiving network packets. To do this, we extracted a software model of an Intel E1000 from the QEMU full-system emulator and ran it on an FPGA. Because this is a software model, we can easily add malicious behaviour to find and exploit vulnerabilities.

We found the attack surface available to a network card was much richer and more nuanced than was previously thought. By examining the memory it was given access to while sending and receiving packets, our device was able to read traffic from networks that it wasn’t supposed to. This included VPN plaintext and traffic from Unix domain sockets that should never leave the machine.

On MacOS and FreeBSD, our network card was able to start arbitrary programs as the system administrator, and on Linux it had access to sensitive kernel data structures. Additionally, on MacOS devices are not protected from one another, so a network card is allowed to read the display contents and keystrokes from a USB keyboard.

Worst of all, on Linux we could completely bypass the enabled IOMMU, simply by setting a few option fields in the messages that our malicious network card sent.

Such attacks are very plausible in practice. The combination of power, video, and peripheral-device DMA over Thunderbolt 3 ports facilitates the creation of malicious charging stations or displays that function correctly but simultaneously take control of connected machines.

We’ve been collaborating with vendors about these vulnerabilities since 2016, and a number of mitigations have been shipped. We have also been working with vendors, helping them to use our Thunderclap tools to explore this vulnerability space and audit their systems for problems.

MacOS fixed the specific vulnerability we used to get administrator access in macOS 10.12.4 in 2016, although the more general scope of such attacks remain relevant. More recently, new laptops that ship with Windows 10 version 1803 or later have a feature called Kernel DMA Protection for Thunderbolt 3, which at least enables the IOMMU for Thunderbolt devices (but not PCI Express ones). Since this feature requires firmware support, older laptops that were shipped before 1803 remain vulnerable. Recently, Intel committed patches to Linux to enable the IOMMU for Thunderbolt devices, and to disable the ATS feature that allowed our IOMMU bypass. These are part of the Linux kernel 5.0 which is currently in the release process.

One major laptop vendor told us they would like to study these vulnerabilities in more detail before adding Thunderbolt to new product lines.

More generally, since this is a new space of many vulnerabilities, rather than a specific example, we believe all operating systems are vulnerable to similar attacks, and that more substantial design changes will be needed to remedy these problems. We noticed similarities between the vulnerability surface available to malicious peripherals in the face of IOMMU protections and that of the kernel system call interface, long a source of operating system vulnerabilities. The kernel system call interface has been subjected to much scrutiny, security analysis, and code hardening over the years, which must now be applied to the interface between peripherals and the IOMMU.

As well as asking vendors to improve the security of their systems, we advise users to update their systems and to be cautious about attaching unfamiliar USB-C devices to their machines – especially those in public places.

We have placed more background on our work and a list of FAQs on our website, thunderclap.io. There, we have also open sourced the Thunderclap research platform to allow other researchers to reproduce and extend our work, and to aid vendors in performing security evaluation of their products.

Thunderclap: Exploring Vulnerabilities in Operating System IOMMU Protection via DMA from Untrustworthy Peripherals A. Theodore Markettos, Colin Rothwell, Brett F. Gutstein, Allison Pearce, Peter G. Neumann, Simon W. Moore, Robert N. M. Watson. Proceedings of the Network and Distributed Systems Security Symposium (NDSS), 24-27 February 2019, San Diego, USA.