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.

View More Papers

Diffence: Fencing Membership Privacy With Diffusion Models

Yuefeng Peng (University of Massachusetts Amherst), Ali Naseh (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst)

Read More

Provably Unlearnable Data Examples

Derui Wang (CSIRO's Data61), Minhui Xue (CSIRO's Data61), Bo Li (The University of Chicago), Seyit Camtepe (CSIRO's Data61), Liming Zhu (CSIRO's Data61)

Read More

QMSan: Efficiently Detecting Uninitialized Memory Errors During Fuzzing

Matteo Marini (Sapienza University of Rome), Daniele Cono D'Elia (Sapienza University of Rome), Mathias Payer (EPFL), Leonardo Querzoni (Sapienza University of Rome)

Read More

MALintent: Coverage Guided Intent Fuzzing Framework for Android

Ammar Askar (Georgia Institute of Technology), Fabian Fleischer (Georgia Institute of Technology), Christopher Kruegel (University of California, Santa Barbara), Giovanni Vigna (University of California, Santa Barbara), Taesoo Kim (Georgia Institute of Technology)

Read More