arXiv subjects

Ross Anderson

Publications and source records attributed to Ross Anderson.

At least 19 recordsLinked to original sources

arXiv2026

Convex duality contracts for production-grade mathematical optimization

Deploying mathematical optimization in autonomous production systems requires precise contracts for objects returned by an optimization solver. Unfortunately, conventions on dual solution and infeasibility certificates (rays) vary widely across solvers and classes of problems. This paper presents the theoretical framework used by MathOpt (a domain-specific language developed and used at Google) to unify these notions. We propose an abstract primal-dual pair based on a simplified Fenchel duality scheme that allows for the mechanical derivation of dual problems and associated contracts for all classes of problems currently supported by MathOpt (including those with linear and quadratic objectives plus linear, conic, quadratic, and two-sided linear constraints). We also show how these contracts can improve clarity of complementary-slackness based optimality conditions for certain classes of problems.

math.OC↗

arXiv2025

Yet Another Diminishing Spark: Low-level Cyberattacks in the Israel-Gaza Conflict

We report empirical evidence of web defacement and DDoS attacks carried out by low-level cybercrime actors in the Israel-Gaza conflict. Our quantitative measurements indicate an immediate increase in such cyberattacks following the Hamas-led assault and the subsequent declaration of war. However, the surges waned quickly after a few weeks, with patterns resembling those observed in the aftermath of the Russian invasion of Ukraine. The scale of attacks and discussions within the hacking community this time was both significantly lower than those during the early days of the Russia-Ukraine war, and attacks have been prominently one-sided: many pro-Palestinian supporters have targeted Israel, while attacks on Palestine have been much less significant. Beyond targeting these two, attackers also defaced sites of other countries to express their war support. Their broader opinions are also largely disparate, with far more support for Palestine and many objections expressed toward Israel.

cs.CR↗

arXiv2023

Automatic Bill of Materials

Ensuring the security of software supply chains requires reliable identification of upstream dependencies. We present the Automatic Bill of Materials, or ABOM, a technique for embedding dependency metadata in binaries at compile time. Rather than relying on developers to explicitly enumerate dependency names and versions, ABOM embeds a hash of each distinct input source code file into the binary emitted by a compiler. Hashes are stored in Compressed Bloom Filters, highly space-efficient probabilistic data structures, which enable querying for the presence of dependencies without the possibility of false negatives. If leveraged across the ecosystem, ABOMs provide a zero-touch, backwards-compatible, drop-in solution for fast supply chain attack detection in real-world, language-independent software.

cs.CR↗

arXiv2023

Human-Producible Adversarial Examples

Visual adversarial examples have so far been restricted to pixel-level image manipulations in the digital world, or have required sophisticated equipment such as 2D or 3D printers to be produced in the physical real world. We present the first ever method of generating human-producible adversarial examples for the real world that requires nothing more complicated than a marker pen. We call them $\textbf{adversarial tags}$. First, building on top of differential rendering, we demonstrate that it is possible to build potent adversarial examples with just lines. We find that by drawing just $4$ lines we can disrupt a YOLO-based model in $54.8\%$ of cases; increasing this to $9$ lines disrupts $81.8\%$ of the cases tested. Next, we devise an improved method for line placement to be invariant to human drawing error. We evaluate our system thoroughly in both digital and analogue worlds and demonstrate that our tags can be applied by untrained humans. We demonstrate the effectiveness of our method for producing real-world adversarial examples by conducting a user study where participants were asked to draw over printed images using digital equivalents as guides. We further evaluate the effectiveness of both targeted and untargeted attacks, and discuss various trade-offs and method limitations, as well as the practical and ethical implications of our work. The source code will be released publicly.

cs.CV↗

arXiv2023

Machine Learning needs Better Randomness Standards: Randomised Smoothing and PRNG-based attacks

Randomness supports many critical functions in the field of machine learning (ML) including optimisation, data selection, privacy, and security. ML systems outsource the task of generating or harvesting randomness to the compiler, the cloud service provider or elsewhere in the toolchain. Yet there is a long history of attackers exploiting poor randomness, or even creating it -- as when the NSA put backdoors in random number generators to break cryptography. In this paper we consider whether attackers can compromise an ML system using only the randomness on which they commonly rely. We focus our effort on Randomised Smoothing, a popular approach to train certifiably robust models, and to certify specific input datapoints of an arbitrary model. We choose Randomised Smoothing since it is used for both security and safety -- to counteract adversarial examples and quantify uncertainty respectively. Under the hood, it relies on sampling Gaussian noise to explore the volume around a data point to certify that a model is not vulnerable to adversarial examples. We demonstrate an entirely novel attack, where an attacker backdoors the supplied randomness to falsely certify either an overestimate or an underestimate of robustness for up to 81 times. We demonstrate that such attacks are possible, that they require very small changes to randomness to succeed, and that they are hard to detect. As an example, we hide an attack in the random number generator and show that the randomness tests suggested by NIST fail to detect it. We advocate updating the NIST guidelines on random number testing to make them more appropriate for safety-critical and security-critical machine-learning applications.

cs.LG↗

arXiv2023

When Vision Fails: Text Attacks Against ViT and OCR

Text-based machine learning models are vulnerable to an emerging class of Unicode-based adversarial examples capable of tricking a model into misreading text with potentially disastrous effects. The primary existing defense against these attacks is to preprocess potentially malicious text inputs using optical character recognition (OCR). In theory, OCR models will ignore any malicious Unicode characters and will extract the visually correct input to be fed to the model. In this work, we show that these visual defenses fail to prevent this type of attack. We use a genetic algorithm to generate visual adversarial examples (i.e., OCR outputs) in a black-box setting, demonstrating a highly effective novel attack that substantially reduces the accuracy of OCR and other visual models. Specifically, we use the Unicode functionality of combining characters (e.g., \~n which combines the characters n and ~) to manipulate text inputs so that small visual perturbations appear when the text is displayed. We demonstrate the effectiveness of these attacks in the real world by creating adversarial examples against production models published by Meta, Microsoft, IBM, and Google. We additionally conduct a user study to establish that the model-fooling adversarial examples do not affect human comprehension of the text, showing that language models are uniquely vulnerable to this type of text attack.

cs.CR↗

arXiv2023

The Curse of Recursion: Training on Generated Data Makes Models Forget

Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT introduced such language models to the general public. It is now clear that large language models (LLMs) are here to stay, and will bring about drastic change in the whole ecosystem of online text and images. In this paper we consider what the future might hold. What will happen to GPT-{n} once LLMs contribute much of the language found online? We find that use of model-generated content in training causes irreversible defects in the resulting models, where tails of the original content distribution disappear. We refer to this effect as Model Collapse and show that it can occur in Variational Autoencoders, Gaussian Mixture Models and LLMs. We build theoretical intuition behind the phenomenon and portray its ubiquity amongst all learned generative models. We demonstrate that it has to be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of content generated by LLMs in data crawled from the Internet.

cs.LG↗

arXiv2023

If it's Provably Secure, It Probably Isn't: Why Learning from Proof Failure is Hard

In this paper we're going to explore the ways in which security proofs can fail, and their broader lessons for security engineering. To mention just one example, Larry Paulson proved the security of SSL/TLS using his theorem prover Isabelle in 1999, yet it's sprung multiple leaks since then, from timing attacks to Heartbleed. We will go through a number of other examples in the hope of elucidating general principles. Proofs can be irrelevant, they can be opaque, they can be misleading and they can even be wrong. So we can look to the philosophy of mathematics for illumination. But the problem is more general. What happens, for example, when we have a choice between relying on mathematics and on physics? The security proofs claimed for quantum cryptosystems based on entanglement raise some pointed questions and may engage the philosophy of physics. And then there's the other varieties of assurance; we will recall the reliance placed on FIPS-140 evaluations, which API attacks suggested may have been overblown. Where the defenders focus their assurance effort on a subsystem or a model that cannot capture the whole attack surface they may just tell the attacker where to focus their effort. However, we think it's deeper and broader than that. The models of proof and assurance on which we try to rely have a social aspect, which we can try to understand from other perspectives ranging from the philosophy or sociology of science to the psychology of shared attention. These perspectives suggest, in various ways, how the management of errors and exceptions may be particularly poor. They do not merely relate to failure modes that the designers failed to consider properly or at all; they also relate to failure modes that the designers (or perhaps the verifiers) did not want to consider for institutional and cultural reasons.

cs.CR↗

arXiv2023

Boosting Big Brother: Attacking Search Engines with Encodings

Search engines are vulnerable to attacks against indexing and searching via text encoding manipulation. By imperceptibly perturbing text using uncommon encoded representations, adversaries can control results across search engines for specific search queries. We demonstrate that this attack is successful against two major commercial search engines - Google and Bing - and one open source search engine - Elasticsearch. We further demonstrate that this attack is successful against LLM chat search including Bing's GPT-4 chatbot and Google's Bard chatbot. We also present a variant of the attack targeting text summarization and plagiarism detection models, two ML tasks closely tied to search. We provide a set of defenses against these techniques and warn that adversaries can leverage these attacks to launch disinformation campaigns against unsuspecting users, motivating the need for search engine maintainers to patch deployed systems.

cs.CR↗

arXiv2023

No Easy Way Out: the Effectiveness of Deplatforming an Extremist Forum to Suppress Hate and Harassment

Legislators and policymakers worldwide are debating options for suppressing illegal, harmful and undesirable material online. Drawing on several quantitative data sources, we show that deplatforming an active community to suppress online hate and harassment, even with a substantial concerted effort involving several tech firms, can be hard. Our case study is the disruption of the largest and longest-running harassment forum Kiwi Farms in late 2022, which is probably the most extensive industry effort to date. Despite the active participation of a number of tech companies over several consecutive months, this campaign failed to shut down the forum and remove its objectionable content. While briefly raising public awareness, it led to rapid platform displacement and traffic fragmentation. Part of the activity decamped to Telegram, while traffic shifted from the primary domain to previously abandoned alternatives. The forum experienced intermittent outages for several weeks, after which the community leading the campaign lost interest, traffic was directed back to the main domain, users quickly returned, and the forum was back online and became even more connected. The forum members themselves stopped discussing the incident shortly thereafter, and the net effect was that forum activity, active users, threads, posts and traffic were all cut by about half. Deplatforming a community without a court order raises philosophical issues about censorship versus free speech; ethical and legal issues about the role of industry in online content moderation; and practical issues on the efficacy of private-sector versus government action. Deplatforming a dispersed community using a series of court orders against individual service providers appears unlikely to be very effective if the censor cannot incapacitate the key maintainers, whether by arresting them, enjoining them or otherwise deterring them.

cs.CR↗

arXiv2023

One Protocol to Rule Them All? On Securing Interoperable Messaging

European lawmakers have ruled that users on different platforms should be able to exchange messages with each other. Yet messaging interoperability opens up a Pandora's box of security and privacy challenges. While championed not just as an anti-trust measure but as a means of providing a better experience for the end user, interoperability runs the risk of making the user experience worse if poorly executed. There are two fundamental questions: how to enable the actual message exchange, and how to handle the numerous residual challenges arising from encrypted messages passing from one service provider to another -- including but certainly not limited to content moderation, user authentication, key management, and metadata sharing between providers. In this work, we identify specific open questions and challenges around interoperable communication in end-to-end encrypted messaging, and present high-level suggestions for tackling these challenges.

cs.CY↗

arXiv2023

Threat Models over Space and Time: A Case Study of E2EE Messaging Applications

Threat modelling is foundational to secure systems engineering and should be done in consideration of the context within which systems operate. On the other hand, the continuous evolution of both the technical sophistication of threats and the system attack surface is an inescapable reality. In this work, we explore the extent to which real-world systems engineering reflects the changing threat context. To this end we examine the desktop clients of six widely used end-to-end-encrypted mobile messaging applications to understand the extent to which they adjusted their threat model over space (when enabling clients on new platforms, such as desktop clients) and time (as new threats emerged). We experimented with short-lived adversarial access against these desktop clients and analyzed the results with respect to two popular threat elicitation frameworks, STRIDE and LINDDUN. The results demonstrate that system designers need to both recognise the threats in the evolving context within which systems operate and, more importantly, to mitigate them by rescoping trust boundaries in a manner that those within the administrative boundary cannot violate security and privacy properties. Such a nuanced understanding of trust boundary scopes and their relationship with administrative boundaries allows for better administration of shared components, including securing them with safe defaults.

cs.CR↗

arXiv2022

Chat Control or Child Protection?

Ian Levy and Crispin Robinson's position paper "Thoughts on child safety on commodity platforms" is to be welcomed for extending the scope of the debate about the extent to which child safety concerns justify legal limits to online privacy. Their paper's context is the laws proposed in both the UK and the EU to give the authorities the power to undermine end-to-end cryptography in online communications services, with a justification of preventing and detecting of child abuse and terrorist recruitment. Both jurisdictions plan to make it easier to get service firms to take down a range of illegal material from their servers; but they also propose to mandate client-side scanning - not just for known illegal images, but for text messages indicative of sexual grooming or terrorist recruitment. In this initial response, I raise technical issues about the capabilities of the technologies the authorities propose to mandate, and a deeper strategic issue: that we should view the child safety debate from the perspective of children at risk of violence, rather than from that of the security and intelligence agencies and the firms that sell surveillance software. The debate on terrorism similarly needs to be grounded in the context in which young people are radicalised. Both political violence and violence against children tend to be politicised and as a result are often poorly policed. Effective policing, particularly of crimes embedded in wicked social problems, must be locally led and involve multiple stakeholders; the idea of using 'artificial intelligence' to replace police officers, social workers and teachers is just the sort of magical thinking that leads to bad policy. The debate must also be conducted within the boundary conditions set by human rights and privacy law, and to be pragmatic must also consider reasonable police priorities.

cs.CY↗

arXiv2022

ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks

Early backdoor attacks against machine learning set off an arms race in attack and defence development. Defences have since appeared demonstrating some ability to detect backdoors in models or even remove them. These defences work by inspecting the training data, the model, or the integrity of the training procedure. In this work, we show that backdoors can be added during compilation, circumventing any safeguards in the data preparation and model training stages. The attacker can not only insert existing weight-based backdoors during compilation, but also a new class of weight-independent backdoors, such as ImpNet. These backdoors are impossible to detect during the training or data preparation processes, because they are not yet present. Next, we demonstrate that some backdoors, including ImpNet, can only be reliably detected at the stage where they are inserted and removing them anywhere else presents a significant challenge. We conclude that ML model security requires assurance of provenance along the entire technical pipeline, including the data, model architecture, compiler, and hardware specification.

cs.LG↗

arXiv2022

Talking Trojan: Analyzing an Industry-Wide Disclosure

While vulnerability research often focuses on technical findings and post-public release industrial response, we provide an analysis of the rest of the story: the coordinated disclosure process from discovery through public release. The industry-wide 'Trojan Source' vulnerability which affected most compilers, interpreters, code editors, and code repositories provided an interesting natural experiment, enabling us to compare responses by firms versus nonprofits and by firms that managed their own response versus firms that outsourced it. We document the interaction with bug bounty programs, government disclosure assistance, academic peer review, and press coverage, among other topics. We compare the response to an attack on source code with the response to a comparable attack on NLP systems employing machine-learning techniques. We conclude with recommendations to improve the global coordinated disclosure system.

cs.CR↗

arXiv2022

Getting Bored of Cyberwar: Exploring the Role of Low-level Cybercrime Actors in the Russia-Ukraine Conflict

There has been substantial commentary on the role of cyberattacks carried out by low-level cybercrime actors in the Russia-Ukraine conflict. We analyse 358k website defacement attacks, 1.7M UDP amplification DDoS attacks, 1764 posts made by 372 users on Hack Forums mentioning the two countries, and 441 Telegram announcements (with 58k replies) of a volunteer hacking group for two months before and four months after the invasion. We find the conflict briefly but notably caught the attention of low-level cybercrime actors, with significant increases in online discussion and both types of attacks targeting Russia and Ukraine. However, there was little evidence of high-profile actions; the role of these players in the ongoing hybrid warfare is minor, and they should be separated from persistent and motivated 'hacktivists' in state-sponsored operations. Their involvement in the conflict appears to have been short-lived and fleeting, with a clear loss of interest in discussing the situation and carrying out both website defacement and DDoS attacks against either Russia or Ukraine after just a few weeks.

cs.CR↗

arXiv2022

Attack of the Clones: Measuring the Maintainability, Originality and Security of Bitcoin 'Forks' in the Wild

Since Bitcoin appeared in 2009, over 6,000 different cryptocurrency projects have followed. The cryptocurrency world may be the only technology where a massive number of competitors offer similar services yet claim unique benefits, including scalability, fast transactions, and security. But are these projects really offering unique features and significant enhancements over their competitors? To answer this question, we conducted a large-scale empirical analysis of code maintenance activities, originality and security across 592 crypto projects. We found that about half of these projects have not been updated for the last six months; over two years, about three-quarters of them disappeared, or were reported as scams or inactive. We also investigated whether 11 security vulnerabilities patched in Bitcoin were also patched in other projects. We found that about 80% of 510 C-language-based cryptocurrency projects have at least one unpatched vulnerability, and the mean time taken to fix the vulnerability is 237.8 days. Among those 510 altcoins, we found that at least 157 altcoins are likely to have been forked from Bitcoin, about a third of them containing only slight changes from the Bitcoin version from which they were forked. As case studies, we did a deep dive into 20 altcoins (e.g., Litecoin, FujiCoin, and Feathercoin) similar to the version of Bitcoin used for the fork. About half of them did not make any technically meaningful change - failing to comply with the promises (e.g., about using Proof of Stake) made in their whitepapers.

cs.CR↗

arXiv2021

ExtremeBB: A Database for Large-Scale Research into Online Hate, Harassment, the Manosphere and Extremism

We introduce ExtremeBB, a textual database of over 53.5M posts made by 38.5k users on 12 extremist bulletin board forums promoting online hate, harassment, the manosphere and other forms of extremism. It enables large-scale analyses of qualitative and quantitative historical trends going back two decades: measuring hate speech and toxicity; tracing the evolution of different strands of extremist ideology; tracking the relationships between online subcultures, extremist behaviours, and real-world violence; and monitoring extremist communities in near real time. This can shed light not only on the spread of problematic ideologies but also the effectiveness of interventions. ExtremeBB comes with a robust ethical data-sharing regime that allows us to share data with academics worldwide. Since 2020, access has been granted to 49 licensees in 16 research groups from 12 institutions.

cs.SI↗