Chanyoung Park (UNIST), Hyungon Moon (UNIST)

Defeating use-after-free exploits presents a challenging problem, one for which a universal solution remains elusive. Recent efforts towards efficient prevention of use-after-free exploits have found that delaying the reuse of freed memory can both be effective and efficient in many cases. Previous studies have proposed two primary approaches: one where reuse is postponed until the allocator can confidently ascertain the absence of any dangling pointers to the freed memory, and another that refrains from reusing a freed heap chunk until the program's termination. We make an intriguing observation from our in-depth analysis of these two approaches and their reported performance impacts. When compared to the design that delays the reuse until the program terminates the strategy that delays the reuse just until no dangling pointer references the freed chunk suffers from a significant performance overhead for some workloads. The change in the reuse of each heap chunk affects the distribution of allocated chunks in the heap, and the performance of some benchmarks. This study proposes HushVac, an allocator that performs delayed reuse in such a way that the distribution of heap chunks becomes more friendly to such workloads. An evaluation of HushVac showed that the average performance overhead of HushVac (4.7%) was lower than that of the state-of-the-art (11.4%) when running the SPEC CPU 2006 benchmark suite. Specifically, the overhead of HushVac on the distribution-sensitive benchmark was about 35.2% while the prior work has an overhead of 110%.

View More Papers

ShapFuzz: Efficient Fuzzing via Shapley-Guided Byte Selection

Kunpeng Zhang (Shenzhen International Graduate School, Tsinghua University), Xiaogang Zhu (Swinburne University of Technology), Xi Xiao (Shenzhen International Graduate School, Tsinghua University), Minhui Xue (CSIRO's Data61), Chao Zhang (Tsinghua University), Sheng Wen (Swinburne University of Technology)

Read More

Separation is Good: A Faster Order-Fairness Byzantine Consensus

Ke Mu (Southern University of Science and Technology, China), Bo Yin (Changsha University of Science and Technology, China), Alia Asheralieva (Loughborough University, UK), Xuetao Wei (Southern University of Science and Technology, China & Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, SUSTech, China)

Read More

Securing Automotive Software Supply Chains (Long)

Marina Moore, Aditya Sirish A Yelgundhalli (New York University), Justin Cappos (NYU)

Read More

SOCs lead AI adoption: Transitioning Lessons to the C-Suite

Eric Dull, Drew Walsh, Scott Riede (Deloitte and Touche)

Read More