Model-Based Security Testing

Security testing aims at validating software system requirements related to security properties like confidentiality, integrity, authentication, authorization, availability, and non-repudiation. Although security testing techniques are available for many years, there has been little approaches that allow for specification of test cases at a higher level of abstraction, for enabling guidance on test identification and specification as well as for automated test generation. Model-based security testing (MBST) is a relatively new field and especially dedicated to the systematic and efficient specification and documentation of security test objectives, security test cases and test suites, as well as to their automated or semi-automated generation. In particular, the combination of security modelling and test generation approaches is still a challenge in research and of high interest for industrial applications. MBST includes e.g. security functional testing, model-based fuzzing, risk- and threat-oriented testing, and the usage of security test patterns. This paper provides a survey on MBST techniques and the related models as well as samples of new methods and tools that are under development in the European ITEA2-project DIAMONDS.Comment: In Proceedings MBT 2012, arXiv:1202.582

Four-quark flux distribution and binding in lattice SU(2)

The full spatial distribution of the color fields of two and four static quarks is measured in lattice SU(2) field theory at separations up to 1 fm at beta=2.4. The four-quark case is equivalent to a qbar q qbar q system in SU(2) and is relevant to meson-meson interactions. By subtracting two-body flux tubes from the four-quark distribution we isolate the flux contribution connected with the four-body binding energy. This contribution is further studied using a model for the binding energies. Lattice sum rules for two and four quarks are used to verify the results.Comment: 46 pages including 71 eps figures. 3D color figures are available at www.physics.helsinki.fi/~ppennane/pics

Targeted Greybox Fuzzing with Static Lookahead Analysis

Automatic test generation typically aims to generate inputs that explore new paths in the program under test in order to find bugs. Existing work has, therefore, focused on guiding the exploration toward program parts that are more likely to contain bugs by using an offline static analysis. In this paper, we introduce a novel technique for targeted greybox fuzzing using an online static analysis that guides the fuzzer toward a set of target locations, for instance, located in recently modified parts of the program. This is achieved by first semantically analyzing each program path that is explored by an input in the fuzzer's test suite. The results of this analysis are then used to control the fuzzer's specialized power schedule, which determines how often to fuzz inputs from the test suite. We implemented our technique by extending a state-of-the-art, industrial fuzzer for Ethereum smart contracts and evaluate its effectiveness on 27 real-world benchmarks. Using an online analysis is particularly suitable for the domain of smart contracts since it does not require any code instrumentation---instrumentation to contracts changes their semantics. Our experiments show that targeted fuzzing significantly outperforms standard greybox fuzzing for reaching 83% of the challenging target locations (up to 14x of median speed-up)

Harvey: A Greybox Fuzzer for Smart Contracts

We present Harvey, an industrial greybox fuzzer for smart contracts, which are programs managing accounts on a blockchain. Greybox fuzzing is a lightweight test-generation approach that effectively detects bugs and security vulnerabilities. However, greybox fuzzers randomly mutate program inputs to exercise new paths; this makes it challenging to cover code that is guarded by narrow checks, which are satisfied by no more than a few input values. Moreover, most real-world smart contracts transition through many different states during their lifetime, e.g., for every bid in an auction. To explore these states and thereby detect deep vulnerabilities, a greybox fuzzer would need to generate sequences of contract transactions, e.g., by creating bids from multiple users, while at the same time keeping the search space and test suite tractable. In this experience paper, we explain how Harvey alleviates both challenges with two key fuzzing techniques and distill the main lessons learned. First, Harvey extends standard greybox fuzzing with a method for predicting new inputs that are more likely to cover new paths or reveal vulnerabilities in smart contracts. Second, it fuzzes transaction sequences in a targeted and demand-driven way. We have evaluated our approach on 27 real-world contracts. Our experiments show that the underlying techniques significantly increase Harvey's effectiveness in achieving high coverage and detecting vulnerabilities, in most cases orders-of-magnitude faster; they also reveal new insights about contract code.Comment: arXiv admin note: substantial text overlap with arXiv:1807.0787

FairFuzz: Targeting Rare Branches to Rapidly Increase Greybox Fuzz Testing Coverage

In recent years, fuzz testing has proven itself to be one of the most effective techniques for finding correctness bugs and security vulnerabilities in practice. One particular fuzz testing tool, American Fuzzy Lop or AFL, has become popular thanks to its ease-of-use and bug-finding power. However, AFL remains limited in the depth of program coverage it achieves, in particular because it does not consider which parts of program inputs should not be mutated in order to maintain deep program coverage. We propose an approach, FairFuzz, that helps alleviate this limitation in two key steps. First, FairFuzz automatically prioritizes inputs exercising rare parts of the program under test. Second, it automatically adjusts the mutation of inputs so that the mutated inputs are more likely to exercise these same rare parts of the program. We conduct evaluation on real-world programs against state-of-the-art versions of AFL, thoroughly repeating experiments to get good measures of variability. We find that on certain benchmarks FairFuzz shows significant coverage increases after 24 hours compared to state-of-the-art versions of AFL, while on others it achieves high program coverage at a significantly faster rate

Maximal variance reduction for stochastic propagators with applications to the static quark spectrum

We study a new method -- maximal variance reduction -- for reducing the variance of stochastic estimators for quark propagators. We find that while this method is comparable to usual iterative inversion for light-light mesons, a considerable improvement is achieved for systems containing at least one infinitely heavy quark. Such systems are needed for heavy quark effective theory. As an illustration of the effectiveness of the method we present results for the masses of the ground state and excited states of $\bar{Q}q$ mesons and $\bar{Q}qq$ baryons. We compare these results with the experimental spectra involving $b$ quarks.Comment: 31 pages with 7 postscript file

A Comprehensive Survey on Database Management System Fuzzing: Techniques, Taxonomy and Experimental Comparison

Database Management System (DBMS) fuzzing is an automated testing technique aimed at detecting errors and vulnerabilities in DBMSs by generating, mutating, and executing test cases. It not only reduces the time and cost of manual testing but also enhances detection coverage, providing valuable assistance in developing commercial DBMSs. Existing fuzzing surveys mainly focus on general-purpose software. However, DBMSs are different from them in terms of internal structure, input/output, and test objectives, requiring specialized fuzzing strategies. Therefore, this paper focuses on DBMS fuzzing and provides a comprehensive review and comparison of the methods in this field. We first introduce the fundamental concepts. Then, we systematically define a general fuzzing procedure and decompose and categorize existing methods. Furthermore, we classify existing methods from the testing objective perspective, covering various components in DBMSs. For representative works, more detailed descriptions are provided to analyze their strengths and limitations. To objectively evaluate the performance of each method, we present an open-source DBMS fuzzing toolkit, OpenDBFuzz. Based on this toolkit, we conduct a detailed experimental comparative analysis of existing methods and finally discuss future research directions.Comment: 34 pages, 22 figure

Security Testing: A Survey

Identifying vulnerabilities and ensuring security functionality by security testing is a widely applied measure to evaluate and improve the security of software. Due to the openness of modern software-based systems, applying appropriate security testing techniques is of growing importance and essential to perform effective and efficient security testing. Therefore, an overview of actual security testing techniques is of high value both for researchers to evaluate and refine the techniques and for practitioners to apply and disseminate them. This chapter fulfills this need and provides an overview of recent security testing techniques. For this purpose, it first summarize the required background of testing and security engineering. Then, basics and recent developments of security testing techniques applied during the secure software development lifecycle, i.e., model-based security testing, code-based testing and static analysis, penetration testing and dynamic analysis, as well as security regression testing are discussed. Finally, the security testing techniques are illustrated by adopting them for an example three-tiered web-based business application