We describe and evaluate an extensible bug-finding tool, Sys, designed to automatically find security bugs in huge codebases, even when easy-to-find bugs have been already picked clean by years of aggressive automatic checking. Sys uses a two-step approach to find such tricky errors. First, it breaks down large---tens of millions of lines---systems into small pieces using user-extensible static checkers to quickly find and mark potential errorsites. Second, it uses user-extensible symbolic execution to deeply examine these potential errorsites for actual bugs. Both the checkers and the system itself are small (6KLOC total). Sys is flexible, because users must be able to exploit domain- or system-specific knowledge in order to detect errors and suppress false positives in real codebases. Sys finds many security bugs (51 bugs, 43 confirmed) in well-checked code---the Chrome and Firefox web browsers---and code that some symbolic tools struggle with---the FreeBSD operating system. Sys's most interesting results include: an exploitable, cash bountied CVE in Chrome that was fixed in seven hours (and whose patch was backported in two days); a trio of bountied bugs with a CVE in Firefox; and a bountied bug in Chrome's audio support.
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Systemizing Interprocedural Static Analysis of Large-scale Systems Code with Graspan
There is more than a decade-long history of using static analysis to find bugs in systems such as Linux. Most of the existing static analyses developed for these systems are simple checkers that find bugs based on pattern matching. Despite the presence of many sophisticated interprocedural analyses, few of them have been employed to improve checkers for systems code due to their complex implementations and poor scalability. In this article, we revisit the scalability problem of interprocedural static analysis from a “Big Data” perspective. That is, we turn sophisticated code analysis into Big Data analytics and leverage novel data processing techniques to solve this traditional programming language problem. We propose Graspan , a disk-based parallel graph system that uses an edge-pair centric computation model to compute dynamic transitive closures on very large program graphs. We develop two backends for Graspan, namely, Graspan-C running on CPUs and Graspan-G on GPUs, and present their designs in the article. Graspan-C can analyze large-scale systems code on any commodity PC, while, if GPUs are available, Graspan-G can be readily used to achieve orders of magnitude speedup by harnessing a GPU’s massive parallelism. We have implemented fully context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases written in multiple languages such as Linux and Apache Hadoop demonstrates that their Graspan implementations are language-independent, scale to millions of lines of code, and are much simpler than their original implementations. Moreover, we show that these analyses can be used to uncover many real-world bugs in large-scale systems code.
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- Award ID(s):
- 2007737
- NSF-PAR ID:
- 10284450
- Date Published:
- Journal Name:
- ACM Transactions on Computer Systems
- Volume:
- 38
- Issue:
- 1-2
- ISSN:
- 0734-2071
- Page Range / eLocation ID:
- 1 to 39
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The Linux Kernel is a world-class operating system controlling most of our computing infrastructure: mobile devices, Internet routers and services, and most of the supercomputers. Linux is also an example of low-level software with no comprehensive regression test suite (for good reasons). The kernel’s tremendous societal importance imposes strict stability and correctness requirements. These properties make Linux a challenging and relevant target for static automated program repair (APR).
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locking API misuse bugs in the Linux Kernel. We attack the key challenge of static APR, namely, the lack of detailed program specification, by combining static analysis with machine learning to complement the information presented by the static analyzer. In experiments on historical real-world bugs in the kernel, we were able to automatically re-produce or propose equivalent patches in 85% of the human-made patches, and automatically rank them among the top three candidates for 64% of the cases and among the top five for 74%.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3466820
