Concurrency Attacks

Just as errors in sequential programs can lead to security exploits, errors in concurrent programs can lead to concurrency attacks. Questions such as whether these attacks are real and what characteristics they have remain largely unknown. In this paper, we present a preliminary study of concurrency attacks and the security implications of real concurrency errors. Our study yields several interesting findings. For instance, we observe that the exploitability of a concurrency error depends on the duration of the timing window within which the error may occur. We further observe that attackers can increase this window through carefully crafted inputs. We also find that four out of five commonly used sequential defense mechanisms become unsafe when applied to concurrent programs. Based on our findings, we propose new defense directions and fixes to existing defenses

ORDER VIOLATION IN MULTITHREADED APPLICATIONS AND ITS DETECTION IN STATIC CODE ANALYSIS PROCESS

The subject presented in the paper concerns resource conflicts, which are the cause of order violation in multithreaded applications. The work focuses on developing conditions that can be implemented as a tool for allowing to detect these conflicts in the process of static code analysis. The research is based on known errors reported to developers of large applications such as Mozilla Firefox browser and MySQL relational database system. These errors could have been avoided by appropriate monitoring of the source code

Automatic Repair of Real Bugs: An Experience Report on the Defects4J Dataset

Defects4J is a large, peer-reviewed, structured dataset of real-world Java bugs. Each bug in Defects4J is provided with a test suite and at least one failing test case that triggers the bug. In this paper, we report on an experiment to explore the effectiveness of automatic repair on Defects4J. The result of our experiment shows that 47 bugs of the Defects4J dataset can be automatically repaired by state-of- the-art repair. This sets a baseline for future research on automatic repair for Java. We have manually analyzed 84 different patches to assess their real correctness. In total, 9 real Java bugs can be correctly fixed with test-suite based repair. This analysis shows that test-suite based repair suffers from under-specified bugs, for which trivial and incorrect patches still pass the test suite. With respect to practical applicability, it takes in average 14.8 minutes to find a patch. The experiment was done on a scientific grid, totaling 17.6 days of computation time. All their systems and experimental results are publicly available on Github in order to facilitate future research on automatic repair

Tortoise: Interactive System Configuration Repair

System configuration languages provide powerful abstractions that simplify managing large-scale, networked systems. Thousands of organizations now use configuration languages, such as Puppet. However, specifications written in configuration languages can have bugs and the shell remains the simplest way to debug a misconfigured system. Unfortunately, it is unsafe to use the shell to fix problems when a system configuration language is in use: a fix applied from the shell may cause the system to drift from the state specified by the configuration language. Thus, despite their advantages, configuration languages force system administrators to give up the simplicity and familiarity of the shell. This paper presents a synthesis-based technique that allows administrators to use configuration languages and the shell in harmony. Administrators can fix errors using the shell and the technique automatically repairs the higher-level specification written in the configuration language. The approach (1) produces repairs that are consistent with the fix made using the shell; (2) produces repairs that are maintainable by minimizing edits made to the original specification; (3) ranks and presents multiple repairs when relevant; and (4) supports all shells the administrator may wish to use. We implement our technique for Puppet, a widely used system configuration language, and evaluate it on a suite of benchmarks under 42 repair scenarios. The top-ranked repair is selected by humans 76% of the time and the human-equivalent repair is ranked 1.31 on average.Comment: Published version in proceedings of IEEE/ACM International Conference on Automated Software Engineering (ASE) 201

Automatically Fixing Syntax Errors Using the Levenshtein Distance

Abstract:To ensure high quality software, much emphasis is laid on software testing. While a number of techniques and tools already exist to identify and locate syntax errors, it is still the duty of programmers to manually fix each of these uncovered syntax errors. In this paper we propose an approach to automate the task of fixing syntax errors by using existing compilers and the levenshtein distance between the identified bug and the possible fixes. The levenshtein distance is a measure of the similarity between two strings. A prototype, called ASBF, has also been built and a number of tests carried out which show that the technique works well in most cases. ASBF is able to automatically fix syntax errors in any erroneous source file and can also process several erroneous files in a source folder. The tests carried out also show that the technique can also be applied to multiple programming languages. Currently ASBF can automatically fix software bugs in the Java and the Python programming languages. The tool also has auto-learning capabilities where it can automatically learn from corrections made manually by a user. It can thereafter couple this learning process with the levenshtein distance to improve its software bugcorrection capabilities.Keywords: Automatically fixing syntax errors, bug fixing, auto-learn, levenshtein distance, Java, Python(Article history: Received 16 September 2016 and accepted 9 December 2016

GPURepair: Automated Repair of GPU Kernels

This paper presents a tool for repairing errors in GPU kernels written in CUDA or OpenCL due to data races and barrier divergence. Our novel extension to prior work can also remove barriers that are deemed unnecessary for correctness. We implement these ideas in our tool called GPURepair, which uses GPUVerify as the verification oracle for GPU kernels. We also extend GPUVerify to support CUDA Cooperative Groups, allowing GPURepair to perform inter-block synchronization for CUDA kernels. To the best of our knowledge, GPURepair is the only tool that can propose a fix for intra-block data races and barrier divergence errors for both CUDA and OpenCL kernels and the only tool that fixes inter-block data races for CUDA kernels. We perform extensive experiments on about 750 kernels and provide a comparison with prior work. We demonstrate the superiority of GPURepair through its capability to fix more kernels and its unique ability to remove redundant barriers and handle inter-block data races.Comment: 19 pages, 1 algorithm, 3 figures, 22nd International Conference on Verification Model Checking and Abstract Interpretation (VMCAI 2021

Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn