242 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Software doping – Theory and detection

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    Software is doped if it contains a hidden functionality that is intentionally included by the manufacturer and is not in the interest of the user or society. This thesis complements this informal definition by a set of formal cleanness definitions that characterise the absence of software doping. These definitions reflect common expectations on clean software behaviour and are applicable to many types of software, from printers to cars to discriminatory AI systems. We use these definitions to propose white-box and black-box analysis techniques to detect software doping. In particular, we present a provably correct, model-based testing algorithm that is intertwined with a probabilistic-falsification-based test input selection technique. We identify and explain how to overcome the challenges that are specific to real-world software doping tests and analyses. The most prominent example of software doping in recent years is the Diesel Emissions Scandal. We demonstrate the strength of our cleanness definitions and analysis techniques by applying them to emission cleaning systems of diesel cars. All our car related research is unified in a Car Data Platform. The mobile app LolaDrives is one building block of this platform; it supports conducting real-driving emissions tests and provides feedback to the user in how far a trip satisfies driving conditions that are defined by official regulations.Software ist gedopt wenn sie eine versteckte FunktionalitĂ€t enthĂ€lt, die vom Hersteller beabsichtigt ist und deren Existenz nicht im Interesse des Benutzers oder der Gesellschaft ist. Die vorliegende Arbeit ergĂ€nzt diese nicht formale Definition um eine Menge von Cleanness-Definitionen, die die Abwesenheit von Software Doping charakterisieren. Diese Definitionen spiegeln allgemeine Erwartungen an "sauberes" Softwareverhalten wider und sie sind auf viele Arten von Software anwendbar, vom Drucker ĂŒber Autos bis hin zu diskriminierenden KI-Systemen. Wir verwenden diese Definitionen um sowohl white-box, als auch black-box Analyseverfahren zur VerfĂŒgung zu stellen, die in der Lage sind Software Doping zu erkennen. Insbesondere stellen wir einen korrekt bewiesenen Algorithmus fĂŒr modellbasierte Tests vor, der eng verflochten ist mit einer Test-Input-Generierung basierend auf einer Probabilistic-Falsification-Technik. Wir identifizieren HĂŒrden hinsichtlich Software-Doping-Tests in der echten Welt und erklĂ€ren, wie diese bewĂ€ltigt werden können. Das bekannteste Beispiel fĂŒr Software Doping in den letzten Jahren ist der Diesel-Abgasskandal. Wir demonstrieren die FĂ€higkeiten unserer Cleanness-Definitionen und Analyseverfahren, indem wir diese auf Abgasreinigungssystem von Dieselfahrzeugen anwenden. Unsere gesamte auto-basierte Forschung kommt in der Car Data Platform zusammen. Die mobile App LolaDrives ist eine Kernkomponente dieser Plattform; sie unterstĂŒtzt bei der DurchfĂŒhrung von Abgasmessungen auf der Straße und gibt dem Fahrer Feedback inwiefern eine Fahrt den offiziellen Anforderungen der EU-Norm der Real-Driving Emissions entspricht

    Exposing Attention Glitches with Flip-Flop Language Modeling

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    Why do large language models sometimes output factual inaccuracies and exhibit erroneous reasoning? The brittleness of these models, particularly when executing long chains of reasoning, currently seems to be an inevitable price to pay for their advanced capabilities of coherently synthesizing knowledge, pragmatics, and abstract thought. Towards making sense of this fundamentally unsolved problem, this work identifies and analyzes the phenomenon of attention glitches, in which the Transformer architecture's inductive biases intermittently fail to capture robust reasoning. To isolate the issue, we introduce flip-flop language modeling (FFLM), a parametric family of synthetic benchmarks designed to probe the extrapolative behavior of neural language models. This simple generative task requires a model to copy binary symbols over long-range dependencies, ignoring the tokens in between. We find that Transformer FFLMs suffer from a long tail of sporadic reasoning errors, some of which we can eliminate using various regularization techniques. Our preliminary mechanistic analyses show why the remaining errors may be very difficult to diagnose and resolve. We hypothesize that attention glitches account for (some of) the closed-domain hallucinations in natural LLMs.Comment: v2: NeurIPS 2023 camera-ready + data releas

    Computer Aided Verification

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    This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book

    Temporally Coherent Backmapping of Molecular Trajectories From Coarse-Grained to Atomistic Resolution

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    Coarse-graining offers a means to extend the achievable time and length scales of molecular dynamics simulations beyond what is practically possible in the atomistic regime. Sampling molecular configurations of interest can be done efficiently using coarse-grained simulations, from which meaningful physicochemical information can be inferred if the corresponding all-atom configurations are reconstructed. However, this procedure of backmapping to reintroduce the lost atomistic detail into coarse-grain structures has proven a challenging task due to the many feasible atomistic configurations that can be associated with one coarse-grain structure. Existing backmapping methods are strictly frame-based, relying on either heuristics to replace coarse-grain particles with atomic fragments and subsequent relaxation or parametrized models to propose atomic coordinates separately and independently for each coarse-grain structure. These approaches neglect information from previous trajectory frames that is critical to ensuring temporal coherence of the backmapped trajectory, while also offering information potentially helpful to producing higher-fidelity atomic reconstructions. In this work, we present a deep learning-enabled data-driven approach for temporally coherent backmapping that explicitly incorporates information from preceding trajectory structures. Our method trains a conditional variational autoencoder to nondeterministically reconstruct atomistic detail conditioned on both the target coarse-grain configuration and the previously reconstructed atomistic configuration. We demonstrate our backmapping approach on two exemplar biomolecular systems: alanine dipeptide and the miniprotein chignolin. We show that our backmapped trajectories accurately recover the structural, thermodynamic, and kinetic properties of the atomistic trajectory data

    Parameterized aspects of team-based formalisms and logical inference

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    Parameterized complexity is an interesting subfield of complexity theory that has received a lot of attention in recent years. Such an analysis characterizes the complexity of (classically) intractable problems by pinpointing the computational hardness to some structural aspects of the input. In this thesis, we study the parameterized complexity of various problems from the area of team-based formalisms as well as logical inference. In the context of team-based formalism, we consider propositional dependence logic (PDL). The problems of interest are model checking (MC) and satisfiability (SAT). Peter Lohmann studied the classical complexity of these problems as a part of his Ph.D. thesis proving that both MC and SAT are NP-complete for PDL. This thesis addresses the parameterized complexity of these problems with respect to a wealth of different parameterizations. Interestingly, SAT for PDL boils down to the satisfiability of propositional logic as implied by the downwards closure of PDL-formulas. We propose an interesting satisfiability variant (mSAT) asking for a satisfiable team of size m. The problem mSAT restores the ‘team semantic’ nature of satisfiability for PDL-formulas. We propose another problem (MaxSubTeam) asking for a maximal satisfiable team if a given team does not satisfy the input formula. From the area of logical inference, we consider (logic-based) abduction and argumentation. The problem of interest in abduction (ABD) is to determine whether there is an explanation for a manifestation in a knowledge base (KB). Following Pfandler et al., we also consider two of its variants by imposing additional restrictions over the size of an explanation (ABD and ABD=). In argumentation, our focus is on the argument existence (ARG), relevance (ARG-Rel) and verification (ARG-Check) problems. The complexity of these problems have been explored already in the classical setting, and each of them is known to be complete for the second level of the polynomial hierarchy (except for ARG-Check which is DP-complete) for propositional logic. Moreover, the work by Nord and Zanuttini (resp., Creignou et al.) explores the complexity of these problems with respect to various restrictions over allowed KBs for ABD (ARG). In this thesis, we explore a two-dimensional complexity analysis for these problems. The first dimension is the restrictions over KB in Schaefer’s framework (the same direction as Nord and Zanuttini and Creignou et al.). What differentiates the work in this thesis from an existing research on these problems is that we add another dimension, the parameterization. The results obtained in this thesis are interesting for two reasons. First (from a theoretical point of view), ideas used in our reductions can help in developing further reductions and prove (in)tractability results for related problems. Second (from a practical point of view), the obtained tractability results might help an agent designing an instance of a problem come up with the one for which the problem is tractable

    Principled Flow Tracking in IoT and Low-Level Applications

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    Significant fractions of our lives are spent digitally, connected to and dependent on Internet-based applications, be it through the Web, mobile, or IoT. All such applications have access to and are entrusted with private user data, such as location, photos, browsing habits, private feed from social networks, or bank details.In this thesis, we focus on IoT and Web(Assembly) apps. We demonstrate IoT apps to be vulnerable to attacks by malicious app makers who are able to bypass the sandboxing mechanisms enforced by the platform to stealthy exfiltrate user data. We further give examples of carefully crafted WebAssembly code abusing the semantics to leak user data.We are interested in applying language-based technologies to ensure application security due to the formal guarantees they provide. Such technologies analyze the underlying program and track how the information flows in an application, with the goal of either statically proving its security, or preventing insecurities from happening at runtime. As such, for protecting against the attacks on IoT apps, we develop both static and dynamic methods, while for securing WebAssembly apps we describe a hybrid approach, combining both.While language-based technologies provide strong security guarantees, they are still to see a widespread adoption outside the academic community where they emerged.In this direction, we outline six design principles to assist the developer in choosing the right security characterization and enforcement mechanism for their system.We further investigate the relative expressiveness of two static enforcement mechanisms which pursue fine- and coarse-grained approaches for tracking the flow of sensitive information in a system.\ua0Finally, we provide the developer with an automatic method for reducing the manual burden associated with some of the language-based enforcements

    Safe Programming Over Distributed Streams

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    The sheer scale of today\u27s data processing needs has led to a new paradigm of software systems centered around requirements for high-throughput, distributed, low-latency computation.Despite their widespread adoption, existing solutions have yet to provide a programming model with safe semantics -- and they disagree on basic design choices, in particular with their approach to parallelism. As a result, naive programmers are easily led to introduce correctness and performance bugs. This work proposes a reliable programming model for modern distributed stream processing, founded in a type system for partially ordered data streams. On top of the core type system, we propose language abstractions for working with streams -- mechanisms to build stream operators with (1) type-safe compositionality, (2) deterministic distribution, (3) run-time testing, and (4) static performance bounds. Our thesis is that viewing streams as partially ordered conveniently exposes parallelism without compromising safety or determinism. The ideas contained in this work are implemented in a series of open source software projects, including the Flumina, DiffStream, and Data Transducers libraries
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