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Yun-Feng Wang

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2026

ASFuzz: Detecting Linux Kernel Asynchronous Concurrency Bugs via Asynchronous Shadow Fuzzing

Concurrency bugs arising from asynchronous-synchronous interactions pose a serious and underexplored threat to OS kernel security. Conventional detection methods often fail to capture the delayed triggering nature of asynchronous tasks and their semantic relationships with synchronous execution contexts, limiting their ability to detect such bugs. We propose two key insights: explicit dependency modeling between execution paths, and contingent triggering of asynchronous tasks during associated synchronous execution. Based on this, we develop ASFuzz, an asynchronous fuzzing system that builds a Shadow Dependency Graph to capture scheduling relations, context-matched resource relations, and operation types. Leveraging the Shadow Dependency Graph, ASFuzz employs a dual-mode architecture: a coverage-guided phase explores synchronous paths and collects legitimate asynchronous-triggering seeds, while a shadow execution phase generates legal syscall programs that naturally schedule related asynchronous tasks during relevant synchronous contexts. An adaptive scheduler coordinates the process, balancing broad state exploration and targeted interleaving stress. We evaluated ASFuzz on Linux kernel versions 6.6 to 6.15. The results demonstrate that ASFuzz successfully uncovered 14 previously unknown concurrency-related bugs, including 10 asynchronous concurrency bugs; 5 of the 14 bugs have been assigned CVE identifiers. These findings validate the effectiveness of our dual insights and highlight ASFuzz’s capability in exposing deep and subtle asynchronous concurrency bugs through dependency-driven and context-sensitive testing.

Jianzhou Zhao, Yang-Yang Geng, Jing-Yi Wang et al. · 0 citations
Open access Aug 2026

MixSan: Enhancing Address-Based Memory Sanitizers with Fused Metadata and Hybrid Detection

During software testing, memory errors in C/C++ can silently corrupt the program state. Address-based memory sanitizers, while offering practical performance and compatibility, are fundamentally unable to distinguish spatial errors that skip redzones or temporal errors that occur after memory reuse. Moreover, their reuse-delay quarantine mechanisms impose significant space and time overhead. We propose a taxonomy of memory sanitizers based on validity encoding and violation detection. Guided by this taxonomy, we introduce fused metadata, a single 8-byte word that encodes an object’s end address and a 6-bit identity tag. MixSan, a prototype built on RangeSanitizer (RSan), stores the same identity tag in pointer high bits through Intel Linear Address Masking (LAM) U57 and validates both the tag and the spatial bound with a unified 3-ALU-op check. On SPEC CPU2006, MixSan incurs a 1.58× geomean runtime overhead, comparable to RSan’s 1.61× overhead. On the Larson allocator benchmark, MixSan and the uninstrumented tcmalloc both scale with thread count, whereas RSan throughput falls; MixSan’s multi-thread plateau is more than 30× that of RSan. In a custom 97-test suite targeting post-reuse temporal errors and redzone-skipping spatial errors, MixSan’s mean single-run detection rate is 98.41% over 104 independent executions per program, matching the theoretical 63/64 rate given a uniform 6-bit tag distribution, whereas ASan and RSan do not detect these constructed cases.

Xiao-Yun Lu, Qiang Wei, Yun-Feng Wang et al. · 0 citations

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