Efficient Formal Verification for the Linux Kernel

Formal verification of the Linux kernel has been receiving increasing attention in recent years, with the development of many models, from memory subsystems to the synchronization primitives of the real-time kernel. The effort in developing formal verification methods is justified considering the large code-base, the complexity in synchronization required in a monolithic kernel and the support for multiple architectures, along with the usage of Linux on critical systems, from high-frequency trading to self-driven cars. Despite recent developments in the area, none of the proposed approaches are suitable and flexible enough to be applied in an efficient way to a running kernel. Aiming to fill such a gap, this paper proposes a formal verification approach for the Linux kernel, based on automata models. It presents a method to auto-generate verification code from an automaton, which can be integrated into a module and dynamically added into the kernel for efficient on-the-fly verification of the system, using in-kernel tracing features. Finally, a set of experiments demonstrate verification of three models, along with performance analysis of the impact of the verification, in terms of latency and throughput of the system, showing the efficiency of the approach

Evidence-enabled verification for the Linux kernel

Formal verification of large software has been an elusive target, riddled with problems of low accuracy and high computational complexity. With growing dependence on software in embedded and cyber-physical systems where vulnerabilities and malware can lead to disasters, an efficient and accurate verification has become a crucial need. The verification should be rigorous, computationally efficient, and automated enough to keep the human effort within reasonable limits, but it does not have to be completely automated. The automation should actually enable and simplify human cross-checking which is especially important when the stakes are high. Unfortunately, formal verification methods work mostly as automated black boxes with very little support for cross-checking. This thesis is about a different way to approach the software verification problem. It is about creating a powerful fusion of automation and human intelligence by incorporating algorithmic innovations to address the major challenges to advance the state of the art for accurate and scalable software verification where complete automation has remained intractable. The key is a mathematically rigorous notion of verification-critical evidence that the machine abstracts from software to empower human to reason with. The algorithmic innovation is to discover the patterns the developers have applied to manage complexity and leverage them. A pattern-based verification is crucial because the problem is intractable otherwise. We call the overall approach Evidence-Enabled Verification (EEV). This thesis presents the EEV with two challenging applications: (1) EEV for Lock/Unlock Pairing to verify the correct pairing of mutex lock and spin lock with their corresponding unlocks on all feasible execution paths, and (2) EEV for Allocation/Deallocation Pairing to verify the correct pairing of memory allocation with its corresponding deallocations on all feasible execution paths. We applied the EEV approach to verify recent versions of the Linux kernel. The results include a comparison with the state-of-the-art Linux Driver Verification (LDV) tool, effectiveness of the proposed visual models as verification-critical evidence, representative examples of verification, the discovered bugs, and limitations of the proposed approach

Verification of the Tree-Based Hierarchical Read-Copy Update in the Linux Kernel

Read-Copy Update (RCU) is a scalable, high-performance Linux-kernel synchronization mechanism that runs low-overhead readers concurrently with updaters. Production-quality RCU implementations for multi-core systems are decidedly non-trivial. Giving the ubiquity of Linux, a rare "million-year" bug can occur several times per day across the installed base. Stringent validation of RCU's complex behaviors is thus critically important. Exhaustive testing is infeasible due to the exponential number of possible executions, which suggests use of formal verification. Previous verification efforts on RCU either focus on simple implementations or use modeling languages, the latter requiring error-prone manual translation that must be repeated frequently due to regular changes in the Linux kernel's RCU implementation. In this paper, we first describe the implementation of Tree RCU in the Linux kernel. We then discuss how to construct a model directly from Tree RCU's source code in C, and use the CBMC model checker to verify its safety and liveness properties. To our best knowledge, this is the first verification of a significant part of RCU's source code, and is an important step towards integration of formal verification into the Linux kernel's regression test suite.Comment: This is a long version of a conference paper published in the 2018 Design, Automation and Test in Europe Conference (DATE

Automatic Verification of Message-Based Device Drivers

We develop a practical solution to the problem of automatic verification of the interface between device drivers and the OS. Our solution relies on a combination of improved driver architecture and verification tools. It supports drivers written in C and can be implemented in any existing OS, which sets it apart from previous proposals for verification-friendly drivers. Our Linux-based evaluation shows that this methodology amplifies the power of existing verification tools in detecting driver bugs, making it possible to verify properties beyond the reach of traditional techniques.Comment: In Proceedings SSV 2012, arXiv:1211.587

Fast and Precise Symbolic Analysis of Concurrency Bugs in Device Drivers

© 2015 IEEE.Concurrency errors, such as data races, make device drivers notoriously hard to develop and debug without automated tool support. We present Whoop, a new automated approach that statically analyzes drivers for data races. Whoop is empowered by symbolic pairwise lockset analysis, a novel analysis that can soundly detect all potential races in a driver. Our analysis avoids reasoning about thread interleavings and thus scales well. Exploiting the race-freedom guarantees provided by Whoop, we achieve a sound partial-order reduction that significantly accelerates Corral, an industrial-strength bug-finder for concurrent programs. Using the combination of Whoop and Corral, we analyzed 16 drivers from the Linux 4.0 kernel, achieving 1.5 - 20× speedups over standalone Corral

Deductive Verification of Unmodified Linux Kernel Library Functions

This paper presents results from the development and evaluation of a deductive verification benchmark consisting of 26 unmodified Linux kernel library functions implementing conventional memory and string operations. The formal contract of the functions was extracted from their source code and was represented in the form of preconditions and postconditions. The correctness of 23 functions was completely proved using AstraVer toolset, although success for 11 functions was achieved using 2 new specification language constructs. Another 2 functions were proved after a minor modification of their source code, while the final one cannot be completely proved using the existing memory model. The benchmark can be used for the testing and evaluation of deductive verification tools and as a starting point for verifying other parts of the Linux kernel.Comment: 18 pages, 2 tables, 6 listings. Accepted to ISoLA 2018 conference. Evaluating Tools for Software Verification trac

Learning Concise Models from Long Execution Traces

Abstract models of system-level behaviour have applications in design exploration, analysis, testing and verification. We describe a new algorithm for automatically extracting useful models, as automata, from execution traces of a HW/SW system driven by software exercising a use-case of interest. Our algorithm leverages modern program synthesis techniques to generate predicates on automaton edges, succinctly describing system behaviour. It employs trace segmentation to tackle complexity for long traces. We learn concise models capturing transaction-level, system-wide behaviour--experimentally demonstrating the approach using traces from a variety of sources, including the x86 QEMU virtual platform and the Real-Time Linux kernel