To mitigate potential memory safety vulnerabilities, recently there have been significant advances in sanitizers for pre-production bug detection. However, the limited inability to balance performance and detection accuracy still holds. The main reason is due to excessive reliance on shadow memory and a large number of memory access checks at runtime, incurring a significant performance overhead (if fine-grained memory safety detection is performed, the overhead will be even greater).

  In this paper, we propose a novel Object-Level Address Sanitizer OLASan to reduce performance overhead further while implementing accurate memory violations (including intra-object overflow) detection. Unlike previous sanitizers ignoring the correlation between memory access and objects, OLASan aggregates multiple memory accesses of same object at function level to perform on-demand targeted sanitization, thus avoiding examining most memory accesses at runtime. Specifically, OLASan characterizes various memory access patterns to identify those which can be aggregated, and implements memory safety checks with customized memory tagging.

 We implement OLASan atop the LLVM framework and evaluate it on SPEC CPU benchmarks. Evaluations show that OLASan outperforms the state-of-the-art methods with 51.18%, 25.20% and 6.52% less runtime overhead than ASan, ASan−− and GiantSan respectively. Moreover, aided by customized memory tagging, OLASan achieves zero false negatives for the first time when testing Juliet suites. Finally, we confirm that OLASan also offers comparable detection capabilities on real bugs.