Shixin Song (Massachusetts Institute of Technology), Joseph Zhang (Massachusetts Institute of Technology), Mengjia Yan (Massachusetts Institute of Technology)

Address Space Layout Randomization (ASLR) is one of the most prominently deployed mitigations against memory corruption attacks. ASLR randomly shuffles program virtual addresses to prevent attackers from knowing the location of program contents in memory. Microarchitectural side channels have been shown to defeat ASLR through various hardware mechanisms. We systematically analyze existing microarchitectural attacks and identify multiple leakage paths. Given the vast attack surface exposed by ASLR, it is challenging to effectively prevent leaking the ASLR secret against microarchitectural attacks.

Motivated by this, we present Oreo, a software-hardware co-design mitigation that strengthens ASLR against these attacks. Oreo uses a new memory mapping interface to remove secret randomized bits in virtual addresses before translating them to their corresponding physical addresses. This extra step hides randomized virtual addresses from microarchitecture structures, preventing side channels from leaking ASLR secrets. Oreo is transparent to user programs and incurs low overhead. We prototyped and evaluated our design on Linux using the hardware simulator gem5.

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ABElity: Attribute Based Encryption for Securing RIC Communication in...

K Sowjanya (Indian Institute of Technology Delhi), Rahul Saini (Eindhoven University of Technology), Dhiman Saha (Indian Institute of Technology Bhilai), Kishor Joshi (Eindhoven University of Technology), Madhurima Das (Indian Institute of Technology Delhi)

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NDSS Symposium 2025 Welcome and Opening Remarks

General Chairs: David Balenson, USC Information Sciences Institute and Heng Yin, University of California, Riverside Program Chairs: Christina Pöpper, New York University Abu Dhabi and Hamed Okhravi, MIT Lincoln Laboratory Artifact Evaluation Chairs: Daniele Cono D’Elia, Sapienza University and Mathy Vanhoef, KU Leuven

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CLIBE: Detecting Dynamic Backdoors in Transformer-based NLP Models

Rui Zeng (Zhejiang University), Xi Chen (Zhejiang University), Yuwen Pu (Zhejiang University), Xuhong Zhang (Zhejiang University), Tianyu Du (Zhejiang University), Shouling Ji (Zhejiang University)

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