Semantic attacks against programmable logic controllers (PLCs) can manipulate industrial control systems (ICS) and cause severe physical disruptions. Yet, existing researches remain target-agnostic: they fail to determine which PLC tags or physical process are most critical when exploited. To address this gap, we introduce Pipe4Stux, a semantic attack framework that generates effective PLC attacks by leveraging key tags and attack semantics. Pipe4Stux first constructs a lexical analyzer and a data dependency subgraph from PLC control logic to capture the relationships among variables and operations. It then identifies key tags, derives semantic features of attacks, and formulates process-aware attack tactic. Experimental results on real ICS scenarios demonstrate that Pipe4Stux enables destructive and reproducible semantic attacks, revealing the potential impact of compromising key tags during specific physical states. In addition, Pipe4Stux provides the first systematic and objective methodology for semantic attack synthesis, offering practical guidance for defense. The framework can also support continuous monitoring by designating key tags as alarm variables within supervisory control and data acquisition (SCADA) systems such as WINCC.

PLC, as the core of industrial control systems, has been turned into a focal point of research for attackers targeting industrial control systems. However, current researched methods for attacking PLCs suffer from issues such as lack of precision and limited specificity. This paper proposes a novel attack method called AOIFF. Specially, AOIFF extracts the binary control logic code from a running PLC and reverses the binary code into assemble code. And then awareness of industrial field information is extracted from assemble code. Finally, it is based on awareness that attack code is generated and injected into a PLC, which can disrupt the normal control logic and then launch precise attacks on industrial control systems. Experimental results demonstrate that AOIFF can effectively perceive information in industrial field and initiate precise and targeted attacks on industrial control systems. Additionally, AOIFF achieves excellent results in the reverse engineering of binary code, enabling effective analysis of binary code.

The study of strongly frustrated magnetic systems has drawn great attentions from both theoretical and experimental physics. Efficient simulations of these models are essential for understanding their exotic properties. Here we present PEPS++, a novel computational paradigm for simulating frustrated magnetic systems and other strongly correlated quantum many-body systems. PEPS++ can accurately solve these models at the extreme scale with low cost and high scalability on modern heterogeneous supercomputers. We implement PEPS++ on Sunway TaihuLight based on a carefully designed tensor computation library for manipulating high-rank tensors and optimize it by invoking various high-performance matrix and tensor operations. By solving a 2D strongly frustrated$J_1$-$J_2$model with over ten million cores, PEPS++ demonstrates the capability of simulating strongly correlated quantum many-body problems at unprecedented scales with accuracy and time-to-solution far beyond the previous state of the art.