Zhen Huang (Shanghai Jiao Tong University), Yidi Kao (Auburn University), Sanchuan Chen (Auburn University), Guoxing Chen (Shanghai Jiao Tong University), Yan Meng (Shanghai Jiao Tong University), Haojin Zhu (Shanghai Jiao Tong University)

Trusted Execution Environment (TEE) has been adopted to secure computation outsourced to untrusted clouds, and the associated remote attestation mechanism enables the user to verify the integrity of the outsourced computation at launch time. However, memory corruption attacks break TEE’s security guarantees without being detected after launch-time attestation. While control-flow attestation (CFA) schemes aim to detect runtime compromises, most existing CFA schemes lack concrete verification methods and can be bypassed by data-only attacks. In this paper, we propose the concept of External-Input Attestation to attest all writes to TEE-protected applications, based on the observation that memory corruption attacks typically start with unintended writes. This approach ensures a trusted enclave state by verifying all writes match expectations, transforming security issues, such as control-flow hijacking, into reliability issues, such as a software crash due to unexpected input. For efficient reference measurement derivation and verification, the current version of External-Input Attestation is limited to enclaved applications whose inputs are known to the verifier. This design is validated by implementing and evaluating prototypes on AMD SEV-SNP and Penglai, where security and performance evaluations show a minimal performance overhead in case studies, including secure model training, model inference, database workloads, and key management.

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Yunhao Liu (Tsinghua University & Zhongguancun Laboratory), Jessie Hui Wang (Tsinghua University & Zhongguancun Laboratory), Yuedong Xu (Fudan University), Zongpeng Li (Tsinghua University), Yangyang Wang (Tsinghua University & Zhongguancun Laboratory), Jilong Wang (Tsinghua University & Zhongguancun Laboratory)

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PriMod4AI: Lifecycle-Aware Privacy Threat Modeling for AI Systems using...

Gautam Savaliya (Deggendorf Institute of Technology, Germany), Robert Aufschlager (Deggendorf Institute of Technology, Germany), Abhishek Subedi (Deggendorf Institute of Technology, Germany), Michael Heigl (Deggendorf Institute of Technology, Germany), Martin Schramm (Deggendorf Institute of Technology, Germany)

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Position Paper: Towards Ubiquitous and Automated User Privacy Configuration

Song Liao (Texas Tech University), Jingwen Yan (Clemson University), Yichen Liu (University of Illinois Urbana-Champaign), David Kotz (Dartmouth College), Luyi Xing (University of Illinois Urbana-Champaign), Long Cheng (Clemson University)

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