I’m in the FutureID3 workshop in Jesus College, Cambridge, and will try to liveblog the talks in followups to this post.
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.
I’m at Financial Crypto 2019 and will try to liveblog some of the sessions in followups to this post.
I can offer a 3.5-year PhD studentship on radio-frequency side-channel security, starting in October 2019, to applicants interested in hardware security, radio communication, and digital signal processing. Due to the funding source, this studentship is restricted to UK nationals, or applicants who have been resident in the UK for the past 10 years. Contact me for details.
In previous work we have shown how stolen bitcoins can be traced if we simply apply existing law. If bitcoins are “mixed”, that is to say if multiple actors pool together their coins in one transaction to obfuscate which coins belong to whom, then the precedent in Clayton’s Case says that FIFO ordering must be used to track which fragments of coin are tainted. If the first input satoshi (atomic unit of Bitcoin) was stolen then the first output satoshi should be marked stolen, and so on.
This led us to design Taintchain, a system for tracing stolen coins through the Bitcoin network. However, we quickly discovered a problem: while it was now possible to trace coins, it was harder to spot patterns. A decent way of visualizing the data is important to make sense of the patterns of splits and joins that are used to obfuscate bitcoin transactions. We therefore designed a visualization tool that interactively expands the taint graph based on user input. We first came up with a way to represent transactions and their associated taints in a temporal graph. After realizing the sheer number of hops that some satoshis go through and the high outdegree of some transactions, we came up with a way to do graph generation on-the-fly while assuming some restrictions on maximum hop length and outdegree.
Using this tool, we were able to spot many of the common tricks used by bitcoin launderers. A summary of our findings can be found in the short paper here.
As recently posted, we currently advertising a post (details here) where “we expect that the best candidate will be someone from a sociology or criminology background who already has some experience analysing large datasets relating to cybercrime” — and now we have a second post for someone with a more technical background.
We seek an enthusiastic researcher to join us in collecting new types of cybercrime data, maintaining existing datasets and doing innovative research using our data. The person we appoint will define their own goals and objectives and pursue them independently, or as part of a team.
An ideal candidate would identify cybercrime datasets that can be collected, build the collection systems and then do cutting edge research on this data – whilst encouraging other academics to take our data and make their own contributions to the field.
We are not necessarily looking for existing experience in researching cybercrime, although this would be a bonus as would a solid technical background in networking and/or malware analysis. We do seek a candidate with strong programming skills — and experience with scripting languages and databases would be much preferred. Good knowledge of English and communication skills are important.
Details of this second post, and what we’re looking for are in the job advert here: http://www.jobs.cam.ac.uk/job/19543/.
We have a further “post-doc” position in the Cambridge Cybercrime Centre: https://www.cambridgecybercrime.uk.
We are looking for an enthusiastic researcher to join us to work on our datasets of posts made in “underground forums”. In addition to pursuing their own research interests regarding cybercrime, they will help us achieve a better understanding of the research opportunities that these datasets open up. In particular, we want to focus on establishing what types of tools and techniques will assist researchers (particularly those without a computer science background) to extract value from these enormous sets (10’s of millions of posts) of data. We will also be looking to extend our collection and need help to understand the most useful way to proceed.
We have an open mind as to who we might appoint, but expect that the best candidate will be someone from a sociology or criminology background who already has some experience analysing large datasets relating to cybercrime. The appointee should be looking to develop their own research, but also be prepared to influence how cybercrime research by non-technical researchers can be enabled by effective use of the extremely large datasets that we are making available.
Details of the posts, and what we’re looking for are in the job advert here: http://www.jobs.cam.ac.uk/job/19318/.
I am at the 2018 Open Source Summit Europe in Edinburgh where I’ll be speaking about Hyperledger projects. In follow-ups to this post, I’ll live-blog security related talks and workshops.
The first workshop of the summit I attended was a crash course introduction to EdgeX Foundry by Jim White, the organization’s chief architect. EdgeX Foundry is an open source, vendor neutral software framework for IoT edge computing. One of the primary challenges that it faces is the sheer number of protocols and standards that need to be supported in the IoT space, both on the south side (various sensors and actuators) as well as the north side (cloud providers, on-premise servers). To do this, EdgeX uses a microservices based architecture where all components interact via configurable APIs and developers can choose to customize any component. While this architecture does help to alleviate the scaling issue, it raises interesting questions with respect to API security and device management. How do we ensure the integrity of the control and command modules when those modules themselves are federated across multiple third-party-contributed microservices? The team at EdgeX is aware of these issues and is a major theme of focus for their upcoming releases.
I am at the Symposium on Post-Bitcoin Cryptocurrencies in Vienna and will try to liveblog the talks in follow-ups to this post.
The introduction was by Bernhard Haslhofer of AIT, who maintains the graphsense.info toolkit and runs the Titanium project on bitcoin forensics jointly with Rainer Boehme of Innsbruck. Rainer then presented an economic analysis arguing that criminal transactions were pretty well the only logical app for bitcoin as it’s permissionless and trustless; if you have access to the courts then there are better ways of doing things. However in the post-bitcoin world of ICOs and smart contracts, it’s not just the anti-money-laundering agencies who need to understand cryptocurrency but the securities regulators and the tax collectors. Yet there is a real policy tension. Governments hype blockchains; Austria uses them to auction sovereign bonds. Yet the only way in for the citizen is through the swamp. How can the swamp be drained?
As mobile phone masts went up across the world’s jungles, savannas and mountains, so did poaching. Wildlife crime syndicates can not only coordinate better but can mine growing public data sets, often of geotagged images. Privacy matters for tigers, for snow leopards, for elephants and rhinos – and even for tortoises and sharks. Animal data protection laws, where they exist at all, are oblivious to these new threats, and no-one seems to have started to think seriously about information security.
So we have been doing some work on this, and presented some initial ideas via an invited talk at Usenix Security in August. A video of the talk is now online.
The most serious poaching threats involve insiders: game guards who go over to the dark side, corrupt officials, and (now) the compromise of data and tools assembled for scientific and conservation purposes. Aggregation of data makes things worse; I might not care too much about a single geotagged photo, but a corpus of thousands of such photos tells a poacher where to set his traps. Cool new AI tools for recognising individual animals can make his work even easier. So people developing systems to help in the conservation mission need to start paying attention to computer security. Compartmentation is necessary, but there are hundreds of conservancies and game reserves, many of which are mutually mistrustful; there is no central authority at Fort Meade to manage classifications and clearances. Data sharing is haphazard and poorly understood, and the limits of open data are only now starting to be recognised. What sort of policies do we need to support, and what sort of tools do we need to create?
This is joint work with Tanya Berger-Wolf of Wildbook, one of the wildlife data aggregation sites, which is currently redeveloping its core systems to incorporate and test the ideas we describe. We are also working to spread the word to both conservators and online service firms.