As researchers continue to optimize AI agents for more effective task execution within operating systems, they often overlook a critical security concern: the ability of these agents to detect ''impostors'' within their environment. Through an analysis of the agents' operational context, we identify a significant threat-attackers can disguise malicious attacks as environmental elements, injecting active disturbances into the agents' execution processes to manipulate their decision-making. We define this novel threat as the Active Environment Injection Attack (AEIA). Focusing on the interaction mechanisms of the Android OS, we conduct a risk assessment of AEIA and identify two critical security vulnerabilities: (1) Adversarial content injection in multimodal interaction interfaces, where attackers embed adversarial instructions within environmental elements to mislead agent decision-making; and (2) Reasoning gap vulnerabilities in the agent's task execution process, which increase susceptibility to AEIA attacks during reasoning. To evaluate the impact of these vulnerabilities, we propose AEIA-MN, an attack scheme that exploits interaction vulnerabilities in mobile operating systems to assess the robustness of MLLM-based agents. Experimental results show that even advanced MLLMs are highly vulnerable to this attack, achieving a maximum attack success rate of 93% on the AndroidWorld benchmark by combining two vulnerabilities.

The rapid development of the Industrial Internet of Things (IIoT) has led to an explosion of industrial data. Due to computing and storage capacity limitations, IIoT devices often outsource the collected data to remote cloud servers. Unfortunately, cloud storage and cloud sharing services are not as reliable as they claim to be. Existing schemes aim to check data integrity in the cloud through cloud auditing. However, they suffer from a number of security and privacy vulnerabilities. The challenge of designing a secure storage auditing framework for industrial IoT comes from two aspects: 1) lack of physical protection of data owner IIoT devices; 2) privacy issues due to auditing of sensitive shared data. Inspired by the aforementioned challenges, we design the secure storage audit framework to support flexible cloud data sharing in IIoT: S2A-P2FS. The first contribution in our work is the Polynomial Prefix Message Authentication Code(P2MAC) design. We design an innovative P2MAC data structure as a label, which can simultaneously achieve efficient data verification in cloud data storage and privacy protection in flexible cloud data sharing for cloud auditing. The second contribution is the design of a unique Physical Unclonable Function(PUF) for IIoT. Harsh industrial conditions hinder the stable operation of PUFs. To protect the trustness of IIoT data owners, we propose a robust PUF-based physical protection mechanism for IIoT devices. The key point is that the required key is not stored in the memory of IIoT but hidden within its physical structure. A security analysis was conducted to demonstrate the robustness of S2A-P2FS against known vulnerabilities. A prototype was implemented in a real-world IIoT scenario. Experimental results indicate that, compared to state-of-the-art schemes, S2A-P2FS achieves over a 3x speedup in computational time and requires only 67.5% of the communication cost.

Blockchain technology, known for its decentralized and immutable nature, serves as the foundation for various applications. As a prominent application of blockchain, decentralized storage is powered by blockchain technology and is expected to provide a reliable and cost-effective alternative to traditional centralized storage. A major challenge in blockchain-powered decentralized storage is how to guarantee the quality of storage services in decentralized storage nodes (DSNs). Storage auditing can ensure the integrity and security of the stored data. Unfortunately, it incurs additional computational costs for data owners and extra storage overheads for DSNs, which thereby cannot be directly applied to decentralized storage networks consisting of nodes with various computation and storage capacity. In this article, we overcome these problems and minimize additional burdens in storage auditing. We propose EDCOMA, a computation and storage efficient auditing scheme for blockchain-based decentralized storage, in which a double compression method is designed to compress data authenticators using both data and polynomial commitment. To prevent replay attacks on double compression launched by DSNs, we introduce zero knowledge proof and design a compression arithmetic circuit to guarantee the execution of compression operations in DSNs. We analyze the security of EDCOMA under the random oracle model and conduct extensive experiments to evaluate the performance of EDCOMA. Experimental results affirm that EDCOMA outperforms state-of-the-art approaches in both computational and storage efficiency.