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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Ran Elgedawy (The University of Tennessee, Knoxville), John Sadik (The University of Tennessee, Knoxville), Anuj Gautam (The University of Tennessee, Knoxville), Trinity Bissahoyo (The University of Tennessee, Knoxville), Christopher Childress (The University of Tennessee, Knoxville), Jacob Leonard (The University of Tennessee, Knoxville), Clay Shubert (The University of Tennessee, Knoxville), Scott Ruoti (The University of Tennessee,…

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Passive Inference Attacks on Split Learning via Adversarial Regularization

Xiaochen Zhu (National University of Singapore & Massachusetts Institute of Technology), Xinjian Luo (National University of Singapore & Mohamed bin Zayed University of Artificial Intelligence), Yuncheng Wu (Renmin University of China), Yangfan Jiang (National University of Singapore), Xiaokui Xiao (National University of Singapore), Beng Chin Ooi (National University of Singapore)

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