Zhanpeng Liu (Peking University), Yi Rong (Tsinghua University), Chenyang Li (Peking University), Wende Tan (Tsinghua University), Yuan Li (Zhongguancun Laboratory), Xinhui Han (Peking University), Songtao Yang (Zhongguancun Laboratory), Chao Zhang (Tsinghua University)

Memory safety violations are a significant concern in real-world programs, prompting the development of various mitigation methods. However, existing cost-efficient defenses provide limited protection and can be bypassed by sophisticated attacks, necessitating the combination of multiple defenses. Unfortunately, combining these defenses often results in performance degradation and compatibility issues.

We present CCTAG, a lightweight architecture that simplifies the integration of diverse tag-based defense mechanisms. It offers configurable tag verification and modification rules to build various security policies, acting as basic protection primitives for defense applications. Its policy-centric mask design boosts flexibility and prevents conflicts, enabling multiple defense mechanisms to run concurrently. Our RISC-V prototype on an FPGA board demonstrates that CCTAG incurs minimal hardware overhead, with a slight increase in LUTs (6.77%) and FFs (8.02%). With combined protections including ret address protection, code pointer and vtable pointer integrity, and memory coloring, the SPEC CPU CINT2006 and CINT2017 benchmarks report low runtime overheads of 4.71% and 7.93%, respectively. Security assessments with CVEs covering major memory safety vulnerabilities and various exploitation techniques verify CCTAG’s effectiveness in mitigating real-world threats.

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Be Careful of What You Embed: Demystifying OLE Vulnerabilities

Yunpeng Tian (Huazhong University of Science and Technology), Feng Dong (Huazhong University of Science and Technology), Haoyi Liu (Huazhong University of Science and Technology), Meng Xu (University of Waterloo), Zhiniang Peng (Huazhong University of Science and Technology; Sangfor Technologies Inc.), Zesen Ye (Sangfor Technologies Inc.), Shenghui Li (Huazhong University of Science and Technology), Xiapu Luo…

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SCRUTINIZER: Towards Secure Forensics on Compromised TrustZone

Yiming Zhang (Southern University of Science and Technology and The Hong Kong Polytechnic University), Fengwei Zhang (Southern University of Science and Technology), Xiapu Luo (The Hong Kong Polytechnic University), Rui Hou (Institute of Information Engineering, Chinese Academy of Sciences), Xuhua Ding (Singapore Management University), Zhenkai Liang (National University of Singapore), Shoumeng Yan (Ant Group), Tao…

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Decoupling Permission Management from Cryptography for Privacy-Preserving Systems

Ruben De Smet (Department of Engineering Technology (INDI), Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel), Tom Godden (Department of Engineering Technology (INDI), Vrije Universiteit Brussel), Kris Steenhaut (Department of Engineering Technology (INDI), Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel), An Braeken (Department of Engineering Technology (INDI), Vrije Universiteit Brussel)

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TME-Box: Scalable In-Process Isolation through Intel TME-MK Memory Encryption

Martin Unterguggenberger (Graz University of Technology), Lukas Lamster (Graz University of Technology), David Schrammel (Graz University of Technology), Martin Schwarzl (Cloudflare, Inc.), Stefan Mangard (Graz University of Technology)

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Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)