Levi Taiji Li (University of Utah), Ningyu He (Peking University), Haoyu Wang (Huazhong University of Science and Technology), Mu Zhang (University of Utah)

In this paper, we propose VETEOS, a static vetting tool for the "Groundhog Day" vulnerabilities in EOSIO contracts. In a "Groundhog Day" attack, culprits leverage the distinctive rollback issue in EOSIO contracts, which allows them to persistently execute identical contract code with varying inputs. By using the information exposed in prior executions, these attackers unlawfully amass insights about the target contract, thereby figuring out a reliable method to generate unauthorized profits. To tackle this problem, we formally define this unique vulnerability as a control and data dependency problem, and develop a custom static analysis tool, VETEOS, that can precisely discover such bugs directly from EOSIO WebAssembly (WASM) bytecode. VETEOS has detected 735 new vulnerabilities in the wild and outperforms the state-of-the-art EOSIO contract analyzer.

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Stacking up the LLM Risks: Applied Machine Learning Security

Dr. Gary McGraw, Berryville Institute of Machine Learning

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EM Eye: Characterizing Electromagnetic Side-channel Eavesdropping on Embedded Cameras

Yan Long (University of Michigan), Qinhong Jiang (Zhejiang University), Chen Yan (Zhejiang University), Tobias Alam (University of Michigan), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University), Kevin Fu (Northeastern University)

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Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering

Rui Zhu (Indiana University Bloominton), Di Tang (Indiana University Bloomington), Siyuan Tang (Indiana University Bloomington), Zihao Wang (Indiana University Bloomington), Guanhong Tao (Purdue University), Shiqing Ma (University of Massachusetts Amherst), XiaoFeng Wang (Indiana University Bloomington), Haixu Tang (Indiana University, Bloomington)

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