Anxiety: A Dynamic Symbolic Execution Framework

Gerasimov, A. I.; Vartanov, Sergey; Ермаков, М. К.; Kruglov, Leonid; Kutz, Daniil; Novikov, Alexander; Asryan, Seryozha

doi:10.1109/ispras.2017.00010

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…The first list represents the fuzzer's test-input, and a constraint solver will utilize a tainted file with the subsequent list. Gerasimov et al [116] incorporate static analysis, Anhiety [158], as a CE engine, and an AFL fuzzer to detect software vulnerabilities. During the fuzzing process, indirect function calls are involved and passed to the static analyzer.…”

Section: ) Mutation-based Hybrid Fuzzersmentioning

confidence: 99%

Exploratory Review of Hybrid Fuzzing for Automated Vulnerability Detection

Rustamov

Kim

et al. 2021

IEEE Access

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Recently, software testing has become a significant component of information security. The most reliable technique for automated software testing is a fuzzing tool that feeds programs with random test-input and detects software vulnerabilities that are critical to security. Similarly, symbolic execution has gained the most attention as an efficient testing tool for producing smart test-inputs and discovering hardto-reach bugs using search-based heuristics and compositional approaches. The combination of fuzzing and symbolic execution makes software testing more efficient by mitigating the limitations in each other. Although several studies have been conducted on hybrid fuzzing in recent years, a comprehensive and consistent review of hybrid fuzzing techniques has not been explored. To add coherence to the extensive literature on hybrid fuzzing and to make it reach a large audience, this study provides an overview of key concepts along with the taxonomy of existing hybrid fuzzing tools, problems, and solutions that have been developed in this sphere. It also includes evaluations of the proposed approaches and a number of suggestions for the development of hybrid fuzzing in the future.

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Section: ) Mutation-based Hybrid Fuzzersmentioning

confidence: 99%

Exploratory Review of Hybrid Fuzzing for Automated Vulnerability Detection

Rustamov

Kim

et al. 2021

IEEE Access

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“…Of course, while solving (1) problem one can detect a certain number of errors, but it is more efficient to separate these tasks. Based on the experience of developing dynamic symbolic execution tools [15][16][17], we developed a new dynamic symbolic execution tool (Sydr) that addresses the problem of expanding coverage. We are going to extend Sydr to solve problem of finding errors in future.…”

Section: Introductionmentioning

confidence: 99%

Sydr: Cutting Edge Dynamic Symbolic Execution

Vishnyakov,

Fedotov,

Kuts

et al. 2020

Preprint

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The security development lifecycle (SDL) is becoming an industry standard. Dynamic symbolic execution (DSE) has enormous amount of applications in computer security (fuzzing, vulnerability discovery, reverse-engineering, etc.). We propose several performance and accuracy improvements for dynamic symbolic execution. Skipping non-symbolic instructions allows to build a path predicate 1.2-3.5 times faster. Symbolic engine simplifies formulas during symbolic execution. Path predicate slicing eliminates irrelevant conjuncts from solver queries. We handle each jump table (switch statement) as multiple branches and describe the method for symbolic execution of multi-threaded programs. The proposed solutions were implemented in Sydr tool. Sydr performs inversion of branches in path predicate. Sydr combines DynamoRIO dynamic binary instrumentation tool with Triton symbolic engine. We evaluated Sydr features on 64-bit Linux executables.

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“…56 https://www.perforce.com/products/klocwork 57 https://www.esecforte.com/products/hp-fortify/ 58 https://www.cs.waikato.ac.nz/ml/weka/ 59 http://go.barik.net/fixbugs 60 https://www.esecforte.com/products/hp-fortify/ 61 https://www.absint.com/astree/index.htm…”

mentioning

confidence: 99%

Survey of Approaches for Postprocessing of Static Analysis Alarms

Muske

Serebrenik

2022

ACM Comput. Surv.

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Static analysis tools have showcased their importance and usefulness in automated detection of defects. However, the tools are known to generate a large number of alarms which are warning messages to the user. The large number of alarms and cost incurred by their manual inspection have been identified as two major reasons for underuse of the tools in practice. To address these concerns plentitude of studies propose postprocessing of alarms: processing the alarms after they are generated. These studies differ greatly in their approaches to postprocess alarms. A comprehensive overview of the postprocessing approaches is, however, missing. In this article, we review 130 primary studies that propose postprocessing of alarms. The studies are collected by combining keywords-based database search and snowballing. We categorize approaches proposed by the collected studies into six main categories: clustering, ranking, pruning, automated elimination of false positives, combination of static and dynamic analyses, and simplification of manual inspection. We provide overview of the categories and sub-categories identified for them, their merits and shortcomings, and different techniques used to implement the approaches. Furthermore, we provide (1) guidelines for selection of the postprocessing techniques by the users/designers of static analysis tools; and (2) directions that can be explored by the researchers.

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Anxiety: A Dynamic Symbolic Execution Framework

Cited by 9 publications

References 19 publications

Exploratory Review of Hybrid Fuzzing for Automated Vulnerability Detection

Exploratory Review of Hybrid Fuzzing for Automated Vulnerability Detection

Sydr: Cutting Edge Dynamic Symbolic Execution

Survey of Approaches for Postprocessing of Static Analysis Alarms

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