10,924 research outputs found

    Multi-Stage Group Key Distribution and PAKEs: Securing Zoom Groups against Malicious Servers without New Security Elements

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    Video conferencing apps like Zoom have hundreds of millions of daily users, making them a high-value target for surveillance and subversion. While such apps claim to achieve some forms of end-to-end encryption, they usually assume an incorruptible server that is able to identify and authenticate all the parties in a meeting. Concretely this means that, e.g., even when using the “end-to-end encrypted” setting, malicious Zoom servers could eavesdrop or impersonate in arbitrary groups. In this work, we show how security against malicious servers can be improved by changing the way in which such protocols use passwords (known as passcodes in Zoom) and integrating a password-authenticated key exchange (PAKE) protocol. To formally prove that our approach achieves its goals, we formalize a class of cryptographic protocols suitable for this setting, and define a basic security notion for them, in which group security can be achieved assuming the server is trusted to correctly authorize the group members. We prove that Zoom indeed meets this notion. We then propose a stronger security notion that can provide security against malicious servers, and propose a transformation that can achieve this notion. We show how we can apply our transformation to Zoom to provably achieve stronger security against malicious servers, notably without introducing new security elements

    A foundation for synthesising programming language semantics

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    Programming or scripting languages used in real-world systems are seldom designed with a formal semantics in mind from the outset. Therefore, the first step for developing well-founded analysis tools for these systems is to reverse-engineer a formal semantics. This can take months or years of effort. Could we automate this process, at least partially? Though desirable, automatically reverse-engineering semantics rules from an implementation is very challenging, as found by Krishnamurthi, Lerner and Elberty. They propose automatically learning desugaring translation rules, mapping the language whose semantics we seek to a simplified, core version, whose semantics are much easier to write. The present thesis contains an analysis of their challenge, as well as the first steps towards a solution. Scaling methods with the size of the language is very difficult due to state space explosion, so this thesis proposes an incremental approach to learning the translation rules. I present a formalisation that both clarifies the informal description of the challenge by Krishnamurthi et al, and re-formulates the problem, shifting the focus to the conditions for incremental learning. The central definition of the new formalisation is the desugaring extension problem, i.e. extending a set of established translation rules by synthesising new ones. In a synthesis algorithm, the choice of search space is important and non-trivial, as it needs to strike a good balance between expressiveness and efficiency. The rest of the thesis focuses on defining search spaces for translation rules via typing rules. Two prerequisites are required for comparing search spaces. The first is a series of benchmarks, a set of source and target languages equipped with intended translation rules between them. The second is an enumerative synthesis algorithm for efficiently enumerating typed programs. I show how algebraic enumeration techniques can be applied to enumerating well-typed translation rules, and discuss the properties expected from a type system for ensuring that typed programs be efficiently enumerable. The thesis presents and empirically evaluates two search spaces. A baseline search space yields the first practical solution to the challenge. The second search space is based on a natural heuristic for translation rules, limiting the usage of variables so that they are used exactly once. I present a linear type system designed to efficiently enumerate translation rules, where this heuristic is enforced. Through informal analysis and empirical comparison to the baseline, I then show that using linear types can speed up the synthesis of translation rules by an order of magnitude

    SoK:Prudent Evaluation Practices for Fuzzing

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    Fuzzing has proven to be a highly effective approach to uncover software bugs over the past decade. After AFL popularized the groundbreaking concept of lightweight coverage feedback, the field of fuzzing has seen a vast amount of scientific work proposing new techniques, improving methodological aspects of existing strategies, or porting existing methods to new domains. All such work must demonstrate its merit by showing its applicability to a problem, measuring its performance, and often showing its superiority over existing works in a thorough, empirical evaluation. Yet, fuzzing is highly sensitive to its target, environment, and circumstances, e.g., randomness in the testing process. After all, relying on randomness is one of the core principles of fuzzing, governing many aspects of a fuzzer's behavior. Combined with the often highly difficult to control environment, the reproducibility of experiments is a crucial concern and requires a prudent evaluation setup. To address these threats to validity, several works, most notably Evaluating Fuzz Testing by Klees et al., have outlined how a carefully designed evaluation setup should be implemented, but it remains unknown to what extent their recommendations have been adopted in practice. In this work, we systematically analyze the evaluation of 150 fuzzing papers published at the top venues between 2018 and 2023. We study how existing guidelines are implemented and observe potential shortcomings and pitfalls. We find a surprising disregard of the existing guidelines regarding statistical tests and systematic errors in fuzzing evaluations. For example, when investigating reported bugs, we find that the search for vulnerabilities in real-world software leads to authors requesting and receiving CVEs of questionable quality. Extending our literature analysis to the practical domain, we attempt to reproduce claims of eight fuzzing papers. These case studies allow us to assess the practical reproducibility of fuzzing research and identify archetypal pitfalls in the evaluation design. Unfortunately, our reproduced results reveal several deficiencies in the studied papers, and we are unable to fully support and reproduce the respective claims. To help the field of fuzzing move toward a scientifically reproducible evaluation strategy, we propose updated guidelines for conducting a fuzzing evaluation that future work should follow

    The Fallacy of Systemic Racism in the American Criminal Justice System

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    Critics of the criminal justice system have repeatedly charged it with systemic racism. It is a tenet of the “war” on the “War on Drugs,” it is a justification used by the so-called “progressive prosecutors” to reject the “Broken Windows” theory of law enforcement, and it is an article of faith of the “Defund the Police!” movement. Even President Joe Biden and his chief lieutenants leveled the same allegation early in this administration. Although the President has eschewed the belief that Americans are a racist people, others have not, proclaiming that virtually anyone who is white is a racist. Yet, few people have defined what they mean by that term. This Article examines what it could mean and tests the truth of the systemic racism claim under each possible definition. None stands up to scrutiny. One argument is that the American citizens who run our many institutions are motivated by racial animus. But the evidence is that racial animus is no longer tolerated in society, and what is more, the criminal justice system strives to identify it when it does occur and to remedy it. Another argument says that the overtly racist beliefs and practices of the past have created lingering racist effects, but this argument cherry-picks historical facts (when it does not ignore them altogether) and fails to grapple with the country’s historic and ongoing efforts to eliminate racial discrimination. It also assumes a causal relationship between past discrimination and present disparities that is unsupported and often contradicted by the evidence. Yet another argument relies psychological research to claim that white Americans are animated by a subconscious racial animus. That research, however, has been debunked. Still another argument says that the criminal justice system is systemically racist because it has disparate effects across racial groups, but this argument looks only at the offenders’ side of the criminal justice system and fails to consider the effect of the criminal justice system on victims. Proponents of the systemic racism theory often proffer “solutions” to it. This Article examines those too and finds that many would, in fact, harm the very people they aim to help. In the context of the “War on Drugs,” where so much of the rhetoric is focused, the authors examine these arguments and solutions. The bottom line is this: the claim of systemic racism in the criminal justice system is unjustified

    Fragments and frame classes:Towards a uniform proof theory for modal fixed point logics

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    This thesis studies the proof theory of modal fixed point logics. In particular, we construct proof systems for various fragments of the modal mu-calculus, interpreted over various classes of frames. With an emphasis on uniform constructions and general results, we aim to bring the relatively underdeveloped proof theory of modal fixed point logics closer to the well-established proof theory of basic modal logic. We employ two main approaches. First, we seek to generalise existing methods for basic modal logic to accommodate fragments of the modal mu-calculus. We use this approach for obtaining Hilbert-style proof systems. Secondly, we adapt existing proof systems for the modal mu-calculus to various classes of frames. This approach yields proof systems which are non-well-founded, or cyclic.The thesis starts with an introduction and some mathematical preliminaries. In Chapter 3 we give hypersequent calculi for modal logic with the master modality, building on work by Ori Lahav. This is followed by an Intermezzo, where we present an abstract framework for cyclic proofs, in which we give sufficient conditions for establishing the bounded proof property. In Chapter 4 we generalise existing work on Hilbert-style proof systems for PDL to the level of the continuous modal mu-calculus. Chapter 5 contains a novel cyclic proof system for the alternation-free two-way modal mu-calculus. Finally, in Chapter 6, we present a cyclic proof system for Guarded Kleene Algebra with Tests and take a first step towards using it to establish the completeness of an algebraic counterpart

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Die unsicheren KanÀle

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    Zeitgenössische IT-Sicherheit operiert in einer Überbietungslogik zwischen Sicherheitsvorkehrungen und Angriffsszenarien. Diese paranoid strukturierte Form negativer Sicherheit lĂ€sst sich vom Ursprung der IT-Sicherheit in der modernen Kryptografie ĂŒber Computerviren und -wĂŒrmer, Ransomware und Backdoors bis hin zum AIDS-Diskurs der 1980er Jahre nachzeichnen. Doch Sicherheit in und mit digital vernetzten Medien lĂ€sst sich auch anders denken: Marie-Luise Shnayien schlĂ€gt die Verwendung eines reparativen, queeren Sicherheitsbegriffs vor, dessen Praktiken zwar nicht auf der Ebene des Technischen angesiedelt sind, aber dennoch nicht ohne ein genaues Wissen desselben auskommen

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum
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