Yubo Du (University of Pittsburgh), Youtao Zhang (University of Pittsburgh), Jun Yang (University of Pittsburgh)

Low-level programming languages like C and C++ offer dynamic memory management capabilities but are vulnerable to Use-After-Free (UAF) vulnerabilities due to improper deallocation handling. These vulnerabilities, arising from accessing memory through dangling pointers, pose significant risks. While various defense mechanisms have been proposed, existing solutions often face challenges such as high performance overhead, excessive memory usage, or inadequate security guarantees, limiting their practicality. Pointer Nullification (PN) has gained attention as a promising UAF mitigation technique by tracking pointers and nullifying them upon buffer deallocation. However, existing PN techniques incur inefficiencies due to precisely associating each pointer with its target buffer, leading to expensive metadata lookups. Moreover, they overlook spatial locality in pointer storage, resulting in a larger number of registrations than necessary. This paper introduces Fast Pointer Nullification (FPN), a new PN-based defense that organizes metadata at the region level to eliminate costly search operations and uses block-based registration to efficiently capture pointer locality. Experiments on SPEC CPU benchmarks and real-world applications demonstrate that FPN provides strong security guarantees while significantly reducing performance and memory overhead compared to prior PN techniques.

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Memory Backdoor Attacks on Neural Networks

Eden Luzon (Ben-Gurion University, Institute of Software Systems and Security), Guy Amit (Ben-Gurion University, Institute of Software Systems and Security), Roy Weiss (Ben-Gurion University, Institute of Software Systems and Security), Torsten Krauß (University of Würzburg), Alexandra Dmitrienko (University of Würzburg), Yisroel Mirsky (Ben-Gurion University, Institute of Software Systems and Security)

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QNBAD: Quantum Noise-induced Backdoor Attacks against Zero Noise Extrapolation

Cheng Chu (Indiana University Bloomington), Qian Lou (University of Central Florida), Fan Chen (Indiana University Bloomington), Lei Jiang (Indiana University Bloomington)

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Q-MLLM: Vector Quantization for Robust Multimodal Large Language Model...

Wei Zhao (Singapore Management University), Zhe Li (Singapore Management University), Yige Li (Singapore Management University), Jun Sun (Singapore Management University)

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