Unified vision-language models(VLMs) have recently shown remarkable progress, enabling a single model to flexibly address diverse tasks through different instructions within a shared computational architecture. This instruction-based control mechanism creates unique security challenges, as adversarial inputs must remain effective across multiple task instructions that may be unpredictably applied to process the same malicious content. In this paper, we introduce CrossVLAD, a new benchmark dataset carefully curated from MSCOCO with GPT-4-assisted annotations for systematically evaluating cross-task adversarial attacks on unified VLMs. CrossVLAD centers on the object-change objective-consistently manipulating a target object's classification across four downstream tasks-and proposes a novel success rate metric that measures simultaneous misclassification across all tasks, providing a rigorous evaluation of adversarial transferability. To tackle this challenge, we present CRAFT (Cross-task Region-based Attack Framework with Token-alignment), an efficient region-centric attack method. Extensive experiments on Florence-2 and other popular unified VLMs demonstrate that our method outperforms existing approaches in both overall cross-task attack performance and targeted object-change success rates, highlighting its effectiveness in adversarially influencing unified VLMs across diverse tasks.

Many cloud providers, including Amazon, Google, Microsoft, and Alibaba Cloud, offer support for blockchain cloud services that rely on a runtime environment, such as the Ethereum Virtual Machine (EVM), to execute smart contracts and ensure consistency between participants. However, existing runtime systems suffer from two main limitations. Firstly, traditional runtime systems like EVM cannot guarantee privacy protection as all the data uploaded to the blockchain is visible to all participants. This restricts the use of blockchain in limited scenarios. Secondly, each computation on the runtime system must be synchronized to all nodes in the network, resulting in a significant increase in computational overhead, which can be challenging to implement for more complex applications. One approach to address these limitations is to utilize Trusted Execution Environments (TEE) for blockchain runtime, which can provide privacy protection and mitigate redundant synchronization operations. However, using TEE for blockchain may significantly increase cloud costs. To overcome these challenges, this paper proposes PL-EVM, a new runtime environment for smart contracts that utilizes jointcloud. PL-EVM achieves high-security guarantees by using TEE to protect privacy-sensitive data and incorporates dynamic migration and splitting mechanisms to achieve high efficiency and low costs. Our evaluation results show that PL-EVM can improve performance and reduce costs by 4% to 32.22%.