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Container isolation is implemented through OS-level virtualization, such as Linux namespaces. Unfortunately, these mechanisms are extremely challenging to implement correctly and, in practice, suffer from functional interference bugs, which compromise container security. In particular, functional interference bugs allow an attacker to extract information from another container running on the same machine or impact its integrity by modifying kernel resources that are incorrectly isolated. Despite their impact, functional interference bugs in OS-level virtualization have received limited attention in part due to the challenges in detecting them. Instead of causing memory errors or crashes, many functional interference bugs involve hard-to-catch logic errors that silently produce semantically incorrect results. This paper proposes KIT, a dynamic testing framework that discovers functional interference bugs in OS-level virtualization mechanisms, such as Linux namespaces. The key idea of KIT is to detect inter-container functional interference by comparing the system call traces of a container across two executions, where it runs with and without the preceding execution of another container. To achieve high efficiency and accuracy, KIT includes two critical components: an efficient algorithm to generate test cases that exercise inter-container data flows and a system call trace analysis framework that detects functional interference bugs and clusters bug reports. KIT discovered 9 functional interference bugs in Linux kernel 5.13, of which 6 have been confirmed. All bugs are caused by logic errors, showing that this approach is able to detect hard-to-catch semantic bugs.
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Book Chapter Active Learning Exercises Involving Building and Design
Chemical analysis relies on calibrated glassware and sophisticated instrumentation. This chapter describes three approaches to student-built models of these tools. In the first, students participate in a semester-long activity to develop quantitative chemical containers (QCCs) to allow those with autism spectrum disorder to work in a laboratory setting. Students determine accuracy, precision, and uncertainty for each container and create technical specifications documents that describe the prototype and its ability to maintain its structure and its volume. Students pitch their designs, use Tinkercad and other software to create their QCCs, print the prototypes on 3D printers, and troubleshoot the robustness of their container. In the second approach, students receive a parts kit for a home-built spectrophotometer. The kit allows for construction of different instruments at varying degrees of sophistication. After design and construction, students characterize the performance of their instrument and/or demonstrate its applicability in an analysis problem. In the third approach, students develop, evaluate, and revise scientific models to explain and predict physical-chemical phenomena in instrumental analysis. The activity promotes understanding at the macroscopic and molecular levels. Different strategies to implement model-based learning are presented, including hand-made models, animated videos, and do-it-yourself instrument prototypes. The flexible activities presented in this chapter may be implemented in classroom or laboratory sections, using different levels of inquiry according to the time and resources available. Additionally, these strategies actively engage students in group work and social interactions that improve motivation and appreciation for the chemical sciences.
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- Award ID(s):
- 1736093
- PAR ID:
- 10338684
- Editor(s):
- Wenzel, T.
- Date Published:
- Journal Name:
- ACS symposium series
- Volume:
- 1409
- ISSN:
- 0097-6156
- Page Range / eLocation ID:
- 181-204
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1021/bk-2022-1409
