TenSure (Mahathevan et al., 2026) is a modular fuzzing framework designed to uncover correctness bugs in Sparse Tensor Compilers (STCs). It generates randomized sparse tensor kernels, executes them across compiler backends, and detects behavioral inconsistencies. TenSure is backend-agnostic and supports dynamic loading of multiple STC implementations.
More details can be found in the GitHub repository.
References
2026
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TENSURE: Fuzzing Sparse Tensor Compilers
Kabilan Mahathevan, Yining Zhang, Muhammad Ali Gulzar, and Kirshanthan Sundararajah
In Fuzzing Workshop (FUZZING) 2026, Network and Distributed System Security (NDSS).
More Information can be
found here , 2026
Sparse Tensor Compilers (STCs) have emerged as critical infrastructure for optimizing high-dimensional data analytics and machine learning workloads. The STCs must synthesize complex, irregular control flow for various compressed storage formats directly from high-level declarative specifications, thereby making them highly susceptible to subtle correctness defects. Existing testing frameworks, which rely on mutating computation graphs restricted to a standard vocabulary of operators, fail to exercise the arbitrary loop synthesis capabilities of these compilers. Furthermore, generic grammar-based fuzzers struggle to generate valid inputs due to the strict rules governing how indices are reused across multiple tensors.
In this paper, we present TENSURE, the first extensible black-box fuzzing framework specifically designed for the testing of STCs. TENSURE leverages Einstein Summation (Einsum) notation as a general input abstraction, enabling the generation of complex, unconventional tensor contractions that expose corner cases in the code-generation phases of STCs. We propose a novel constraint-based generation algorithm that guarantees 100% semantic validity of synthesized kernels, significantly outperforming the 3.3% validity rate of baseline grammar fuzzers. To enable metamorphic testing without a trusted reference, we introduce a set of semantic-preserving mutation operators that exploit algebraic commutativity and heterogeneity in storage formats. Our evaluation on two state-of-the-art systems, TACO and Finch, reveals widespread fragility, particularly in TACO, where TENSURE exposed crashes or silent miscompilations in a majority of generated test cases. These findings underscore the critical need for specialized testing tools in the sparse compilation ecosystem.