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S&P2026顶会

QuickSafe: Targeted Hardening Against Memory Corruption

Johannes Blaser, Floris Gorter, Klaus von Gleissenthall, Herbert Bos

2026年份

摘要

Despite decades of research, memory safety solutions see limited adoption, as they often incur high overheads, are complex to deploy, or cover only a narrow scope of bugs. In this paper, we present QuickSafe — a targeted approach to harden programs against exploitation of known but unresolved memory errors with minimal overhead. QuickSafe yields a stopgap patch that is immediately available, while the bug awaits eventual resolution. Where most existing automatic patch generators rely on inserting runtime constraint checks in the code to stop exploits, QuickSafe instead isolates memory objects associated with a known bug from the rest of the program. Object isolation can be implemented in different ways, depending on hardware support and desired security guarantees. To assess the viability, we present two such implementations. On traditional architectures, we allocate vulnerable objects on dedicated pages flanked by inaccessible guard pages. On platforms that support Memory Tagging Extensions (MTE), we offer stronger guarantees by enforcing disjoint tag domains. To reliably identify the objects associated with a given memory error, we introduce TagASan — an extension of AddressSanitizer (ASan) that uses tagged pointers to trace faulting accesses back to their originating allocation sites. As an additional contribution, we present a new dataset of 223 real-world memory errors across ten prominent projects to measure the performance of automatic patch generators. We evaluate QuickSafe on (1) this new benchmark suite, (2) the Juliet Test Suite, and (3) buggy benchmarks from SPEC CPU2006/2017. Using the guard-page-based isolation backend, QuickSafe protects against the exploitation of all evaluated bugs, incurring a geomean memory overhead of 2.46 % and a geomean runtime overhead of 2.67 % - with the vast majority of applications slowing down by only around 1 %. We apply the MTE-based isolation strategy to a representative subset of the dataset, confirming its effectiveness and showing negligible runtime overhead of ≈0.12%\approx 0.12 \%.

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