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Understanding and Detecting Concurrency Attacks

Gu, Rui; Gan, Bo; Ning, Yi; Cui, Heming; Yang, Junfeng

Just like bugs in single-threaded programs can lead to vulnerabilities, bugs in multithreaded programs can also lead to concurrency attacks. Unfortunately, there is little quantitative data on how well existing tools can detect these attacks. This paper presents the first quantitative study on concurrency attacks and their implications on tools. Our study on 10 widely used programs reveals 26 concurrency attacks with broad threats (e.g., OS privilege escalation), and we built scripts to successfully exploit 10 attacks. Our study further reveals that, only extremely small portions of inputs and thread interleavings (or schedules) can trigger these attacks, and existing concurrency bug detectors work poorly because they lack help to identify the vulnerable inputs and schedules. Our key insight is that the reports in existing detectors have implied moderate hints on what inputs and schedules will likely lead to attacks and what will not (e.g., benign bug reports). With this insight, this paper presents a new directed concurrency attack detection approach and its implementation, OWL. It extracts hints from the reports with static analysis, augments existing detectors by pruning out the benign inputs and schedules, and then directs detectors and its own runtime vulnerability verifiers to work on the remaining, likely vulnerable inputs and schedules. Evaluation shows that OWL reduced 94.3% reports caused by benign inputs or schedules and detected 7 known concurrency attacks. OWL also detected 3 previously unknown concurrency attacks, including a use-after-free attack in SSDB confirmed as CVE-2016-1000324, an integer overflow, HTML integrity violation in Apache and three new MySQL data races confirmed with bug ID 84064, 84122, 84241. All OWL source code, exploit scripts, and results are available at https://github.com/ruigulala/ConAnalysis.

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Publisher
Department of Computer Science, Columbia University
Publication Origin
New York
Series
Columbia University Computer Science Technical Reports, 013-17
Academic Units
Computer Science
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