Fangzhou Dong (Arizona State University), Arvind S Raj (Arizona State University), Efrén López-Morales (New Mexico State University), Siyu Liu (Arizona State University), Yan Shoshitaishvili (Arizona State University), Tiffany Bao (Arizona State University), Adam Doupé (Arizona State University), Muslum Ozgur Ozmen (Arizona State University), Ruoyu Wang (Arizona State University)

Programmable Logic Controllers (PLCs) are industrial computers that control devices with real-world physical effects, and safety vulnerabilities in these systems can lead to catastrophic consequences. While prior research has proposed techniques to detect safety issues in PLC state machines, most approaches require access to design specifications or source code—resources often unavailable to analysts or end users.

This paper targets a prevalent class of vulnerabilities, which we name Blind-Trust Vulnerabilities, caused by missing or incomplete safety checks on peripheral inputs. We introduce Ta’veren, a novel static analysis-based framework that identifies such vulnerabilities directly from PLC binaries without relying on firmware rehosting, which remains an open research problem in firmware analysis. Ta’veren recovers the finite state machines of the PLC binaries, enabling repeated safety analyses under various policy specifications. To abstract the state from program states to logic-related states, we leverage our insight that PLCs consistently use specific variables to represent internal states, thus allowing for aggressive state deduplication. This insight enables us to effectively deduplicate states without compromising soundness. We develop a prototype of Ta’veren and evaluate it on real-world PLC binaries. Our experiments show that Ta’veren efficiently recovers meaningful FSMs and uncovers critical safety violations with high effectiveness.

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DUALBREACH: Efficient Dual-Jailbreaking via Target-Driven Initialization and Multi-Target Optimization

Xinzhe Huang (Zhejiang University), Kedong Xiu (Zhejiang University), Tianhang Zheng (Zhejiang University), Churui Zeng (Zhejiang University), Wangze Ni (Zhejiang University), Zhan Qin (Zhejiang University), Kui Ren (Zhejiang University), Chun Chen (Zhejiang University)

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Robust Fraud Transaction Detection: A Two-Player Game Approach

Qi Tan (College of Computer Science and Software Engineering, Shenzhen University), Yi Zhao (School of Cyberspace Science and Technology, Beijing Institute of Technology), Laizhong Cui (College of Computer Science and Software Engineering, Shenzhen University), Qi Li (Institute for Network Science and Cyberspace, Tsinghua University), Ming Zhu (Department of Computer Science and Technology, Tsinghua University), Xing…

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ReFuzz: Reusing Tests for Processor Fuzzing with Contextual Bandits

Chen Chen (Texas A&M University, USA), Zaiyan Xu (Texas A&M University, USA), Mohamadreza Rostami (Technische Universitat Darmstadt, Germany), David Liu (Texas A&M University, USA), Dileep Kalathil (Texas A&M University, USA), Ahmad-Reza Sadeghi (Technische Universitat Darmstadt, Germany), Jeyavijayan (JV) Rajendran (Texas A&M University, USA)

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