DiVinE 3.0 – An Explicit-State Model Checker for Multithreaded C &amp; C++ Programs

Barnat, Jǐŕı; Brim, Luboš; Havel, Vojtěch; Havlíček, Jan; Kriho, Jan; Lenčo, Milan; Ročkai, Petr; Štill, Vladimír; Weiser, Jiří

doi:10.1007/978-3-642-39799-8_60

Cited by 78 publications

(47 citation statements)

References 9 publications

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“…However, as will be shown later in § VI-D, ZEUS is not tied to Seahorn; it can be used with any other verifier that operates upon LLVM bitcode, such as SMACK [72] or DIVINE [52].…”

Section: Methodsmentioning

confidence: 99%

ZEUS: Analyzing Safety of Smart Contracts

Kalra

Goel

Dhawan

et al. 2018

Proceedings 2018 Network and Distributed System Security Symposium

502

466

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Abstract-A smart contract is hard to patch for bugs once it is deployed, irrespective of the money it holds. A recent bug caused losses worth around $50 million of cryptocurrency. We present ZEUS-a framework to verify the correctness and validate the fairness of smart contracts. We consider correctness as adherence to safe programming practices, while fairness is adherence to agreed upon higher-level business logic. ZEUS leverages both abstract interpretation and symbolic model checking, along with the power of constrained horn clauses to quickly verify contracts for safety. We have built a prototype of ZEUS for Ethereum and Fabric blockchain platforms, and evaluated it with over 22.4K smart contracts. Our evaluation indicates that about 94.6% of contracts (containing cryptocurrency worth more than $0.5 billion) are vulnerable. ZEUS is sound with zero false negatives and has a low false positive rate, with an order of magnitude improvement in analysis time as compared to prior art.

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“…However, as will be shown later in § VI-D, ZEUS is not tied to Seahorn; it can be used with any other verifier that operates upon LLVM bitcode, such as SMACK [72] or DIVINE [52].…”

Section: Methodsmentioning

confidence: 99%

ZEUS: Analyzing Safety of Smart Contracts

Kalra

Goel

Dhawan

et al. 2018

Proceedings 2018 Network and Distributed System Security Symposium

502

466

View full text Add to dashboard Cite

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“…Both explicit-set and SAT-based approaches to verification of Simulink diagrams were implemented 1 in the DiVinE [4] verification environment, which already supported these diagrams as input, in the form of CESMI intermediate language. The implementation of set is a relatively straightforward extension of expl: instead of keeping the evaluations in separate states there are fewer multi-states for every explicit control state.…”

Section: A Implementationmentioning

confidence: 99%

Temporal Verification of Simulink Diagrams

Barnat

Bauch

Havel

2014

2014 IEEE 15th International Symposium on High-Assurance Systems Engineering

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Automatic verification of programs and computer systems with input variables represents a significant and wellmotivated challenge. The case of Simulink diagrams is especially difficult, because there the inputs are read iteratively and the number of input variables is in theory unbounded. We apply the techniques of explicit model checking to account for the temporal (control) aspects of verification and use set-based representation of data, thus handling both sources of nondeterminism present in the verification. Two different representations of sets are evaluated in scalability with respect to the range of input variables. Explicit (enumerating) sets are very fast for small ranges but fail to scale. Symbolic sets, represented as first-order formulae in the bit-vector theory and compared using satisfiability modulo theory solvers, scale well to arbitrary (though still bounded) range of input variables. Thus the proposed method allows complete automatic verification without the need to limit the nondeterminism of input.

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“…The problem of analyzing such a model against a temporal logic formula, known as formal analysis or model checking, has received a lot of attention during the past thirty years, and efficient algorithms and software tools are available [2], [3]. On the other hand, the formal synthesis problem, in which the goal is to design or control a system from a temporal logic specification, has not been studied extensively until recently.…”

Section: Introductionmentioning

confidence: 99%

Optimal Temporal Logic Control for Deterministic Transition Systems With Probabilistic Penalties

Svoreňová

Černá

Belta

2015

IEEE Trans. Automat. Contr.

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We consider an optimal control problem for a weighted deterministic transition system required to satisfy a constraint expressed as a Linear Temporal Logic (LTL) formula over its labels. By assuming that the executions of the system incur time-varying penalties modeled as Markov chains, our goal is to minimize the expected average cumulative penalty incurred between consecutive satisfactions of a desired property. Using concepts from theoretical computer science, we provide two solutions to this problem. First, we derive a provably correct optimal strategy within the class of strategies that do not exploit values of penalties sensed in real time. Second, we show that by taking advantage of locally sensing the penalties, we can construct heuristic strategies leading to lower collected penalty. While still ensuring satisfaction of the LTL constraint, we cannot guarantee optimality in the latter case. We provide a user-friendly implementation of the proposed algorithms and analysis of two case studies.

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DiVinE 3.0 – An Explicit-State Model Checker for Multithreaded C & C++ Programs

Cited by 78 publications

References 9 publications

ZEUS: Analyzing Safety of Smart Contracts

ZEUS: Analyzing Safety of Smart Contracts

Temporal Verification of Simulink Diagrams

Optimal Temporal Logic Control for Deterministic Transition Systems With Probabilistic Penalties

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