The rapid development of the Industrial Internet of Things (IIoT) and its application across various sectors has led to increased interconnectivity and data sharing between devices and sensors. While this has brought convenience to users, it has also raised concerns about information security, including data security and identity authentication. IIoT devices are particularly vulnerable to attacks due to their lack of robust key management systems, efficient authentication processes, high fault tolerance, and other issues. To address these challenges, technologies such as blockchain and the formal analysis of security protocols can be utilized. And blockchain-based Industrial Internet of Things (BIIoT) is the new direction. These technologies leverage the strengths of cryptography and logical reasoning to provide secure data communication and ensure reliable identity authentication and verification, thereby becoming a crucial support for maintaining the security of the Industrial Internet. In this paper, based on the theory of the strand space attack model, we improved the Fiber Channel Password Authentication Protocol (FACP) security protocol in the network environment based on symmetric cryptography and asymmetric cryptography. Specifically, in view of the problem that the challenge value cannot reach a consensus under the symmetric cryptography system, and the subject identity cannot reach a consensus under the asymmetric cryptography system, an improved protocol is designed and implemented to meet the authentication requirements, and the corresponding attack examples are shown. Finally, the effectiveness and security of the protocol were verified by simulating different networking environments. The improved protocol has shown an increase in efficiency compared with the original protocol across three different network configurations. There was a 6.43% increase in efficiency when centralized devices were connected to centralized devices, a 5.81% increase in efficiency when centralized devices were connected to distributed devices, and a 6.32% increase in efficiency when distributed devices were connected to distributed devices. Experimental results show that this protocol can enhance the security and efficiency of communication between devices and between devices and nodes (servers, disks) in commonly used Ethernet passive optical network (EPON) environments without affecting the identity authentication function.

Existing studies on backdoor attacks in large language models (LLMs) have contributed significantly to the literature by exploring trigger-based strategies—such as rare tokens or syntactic anomalies—that, however, limit both their stealth and generalizability, rendering them susceptible to detection. In this study, we propose HDPAttack, a novel hidden backdoor prompt attack method which is designed to overcome these limitations by leveraging the semantic and structural properties of prompts as triggers rather than relying on explicit markers. Not symmetric to traditional approaches, HDPAttack injects carefully crafted fake demonstrations into the training data, semantically re-expressing prompts to generate examples that exhibit high consistency in input semantics and corresponding labels. This method guides models to learn latent trigger patterns embedded in their deep representations, thereby enabling backdoor activation through natural language prompts without altering user inputs or introducing conspicuous anomalies. Experimental results across datasets (SST-2, SMS, AGNews, Amazon) reveal that HDPAttack achieved an average attack success rate of 99.87%, outperforming baseline methods by 2–20% while incurring a classification accuracy loss of ≤1%. These findings set a new benchmark for undetectable backdoor attacks and underscore the urgent need for advancements in prompt-based defense strategies.

Trust evaluation in Industrial Internet of Things (IIoT) edge networks must account for both device behavior and the operational importance of industrial assets. Existing models often apply uniform scoring rules, which may limit their response to semantic attacks and whitewashing behavior while increasing the processing burden on edge devices. This paper presents an Asset-Aware Resilient Trust (ART) framework. ART separates dynamic behavioral credibility from physical asset criticality through a dual-plane architecture. Cross-layer evidence is collected from communication, identity, physical, and semantic interactions. A Fuzzy Triggered-Entropy Weight Method (Fuzzy T-EWM) recalculates evidence weights only when the observed fluctuation exceeds a preset threshold. Trust scores are updated using a Fast-Drop Slow-Rise rule, together with a tolerance margin for routine network jitter. The simulation results show that ART detects stealthy False Data Injection attacks, limits trust recovery after whitewashing behavior, and reduces accumulated computational overhead by 76.4% compared with the Standard EWM baseline. The credibility-weighted aggregation mechanism also limits collusive recommendation manipulation during cold-start evaluation. These results support differentiated trust regulation for IIoT edge networks.