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The increasing complexity of System-on-Chip (SoC) designs and the rise of third-party vendors in the semiconductor industry have led to unprecedented security concerns. Traditional formal methods struggle to address software-exploited hardware bugs, and existing solutions for hardware-software co-verification often fall short. This paper presents Microscope, a novel framework for inferring software instruction patterns that can trigger hardware vulnerabilities in SoC designs. Microscope enhances the Structural Causal Model (SCM) with hardware features, creating a scalable Hardware Structural Causal Model (HW-SCM). A domain-specific language (DSL) in SMT-LIB represents the HW-SCM and predefined security properties, with incremental SMT solving deducing possible instructions. Microscope identifies causality to determine whether a hardware threat could result from any software events, providing a valuable resource for patching hardware bugs and generating test input. Extensive experimentation demonstrates Microscope's capability to infer the causality of a wide range of vulnerabilities and bugs located in SoC-level benchmarks. 

In adverse environments, the number of fertilizable female gametophytes (FGs) in plants is reduced, leading to increased survival of the remaining offspring. How the maternal plant perceives internal growth cues and external stress conditions to alter FG development remains largely unknown. We report that homeostasis of the stress signaling molecule nitric oxide (NO) plays a key role in controlling FG development under both optimal and stress conditions. NO homeostasis is precisely regulated by S-nitrosoglutathione reductase (GSNOR). Prior to fertilization, GSNOR protein is exclusively accumulated in sporophytic tissues and indirectly controls FG development in Arabidopsis (Arabidopsis thaliana). In GSNOR null mutants, NO species accumulated in the degenerating sporophytic nucellus, and auxin efflux into the developing FG was restricted, which inhibited FG development, resulting in reduced fertility. Importantly, restoring GSNOR expression in maternal, but not gametophytic tissues, or increasing auxin efflux substrate significantly increased the proportion of normal FGs and fertility. Furthermore, GSNOR overexpression or added auxin efflux substrate increased fertility under drought and salt stress. These data indicate that NO homeostasis is critical to normal auxin transport and maternal control of FG development, which in turn determine seed yield. Understanding this aspect of fertility control could contribute to mediating yield loss under adverse conditions.

 

This paper presents LLM4SecHW, a novel framework for hardware debugging that leverages domain-specific Large Language Model (LLM). Despite the success of LLMs in automating various software development tasks, their application in the hardware security domain has been limited due to the constraints of commercial LLMs and the scarcity of domain-specific data. To address these challenges, we propose a unique approach to compile a dataset of open-source hardware design defects and their remediation steps, utilizing version control data. This dataset provides a substantial foundation for training machine learning models for hardware. LLM4SecHW employs fine-tuning of medium-sized LLMs based on this dataset, enabling the identification and rectification of bugs in hardware designs. This pioneering approach offers a reference workflow for the application of fine-tuning domain-specific LLMs in other research areas. We evaluate the performance of our proposed system on various open-source hardware designs, demonstrating its efficacy in accurately identifying and correcting defects. Our work brings a new perspective on automating the quality control process in hardware design. 

Hardware IP verification requires collaboration from several parties, including the 3PIP vendor, IP user, and EDA tool vendor, all of whom could threaten the design's integrity and confidentiality. Various frameworks and tools, including the IEEE 1735 standard, have been developed to address these concerns. However, these solutions fall short of the zero trust model's requirements. To overcome this, we propose a novel zero trust formal verification framework that incorporates secure multiparty computation to ensure the privacy of all the parties involved in the verification process. The efficiency of the framework is demonstrated by checking various open-source IP-level benchmarks.