Large language models (LLMs) increasingly support multilingual understanding and generation. Meanwhile, efforts to interpret their internal mechanisms have emerged, offering insights to enhance multilingual performance. While multi-head self-attention (MHA) has proven critical in many areas, its role in multilingual capabilities remains underexplored. In this work, we study the contribution of MHA in supporting multilingual processing in LLMs. We propose Language Attention Head Importance Scores (LAHIS), an effective and efficient method that identifies attention head importance for multilingual capabilities via a single forward and backward pass through the LLM. Applying LAHIS to Aya-23-8B, Llama-3.2-3B, and Mistral-7B-v0.1, we reveal the existence of both language-specific and language-general heads. Language-specific heads enable cross-lingual attention transfer to guide the model toward target language contexts and mitigate off-target language generation issue, contributing to addressing challenges in multilingual LLMs. We also introduce a lightweight adaptation that learns a soft head mask to modulate attention outputs over language heads, requiring only 20 tunable parameters to improve XQuAD accuracy. Overall, our work enhances both the interpretability and multilingual capabilities of LLMs from the perspective of MHA.

As computer system software stacks become increasingly complex, the associated security risks also escalate. Trusted Execution Environments (TEEs) have emerged as a mainstream security solution to enhance system security. TEEs can be categorized into user-level TEEs, OS-level TEEs, and hybrid TEEs. However, these TEEs typically possess fixed security boundaries and isolation domains, limiting their adaptability to varying security requirements and dynamic scenarios. Moreover, the design of Trusted Computing Base (TCB) components in TEE frameworks often operates at the highest privilege levels of the architecture. This concentration of critical code at the highest privilege level increases the whole platform’s security risk due to the growing amount of code as more security functions are added. In this paper, we propose FlexClave, an extensible and secure TEE framework designed to address these issues. FlexClave leverages hardware primitives to create secure isolation boundaries tailored to different use cases. Additionally, our framework distributes TCB components across various privilege levels, reducing the concentration of security functions at the highest privilege levels and mitigating the risks associated with running extensive code in a single, highly privileged context. We implement two prototypes on ARMv9-A Fixed Virtual Platform and ARMv8 RK3399 SoC, each with two use cases (container and virtual machine), to evaluate the system’s security and performance.

Large Language Models (LLMs) often retain inaccurate or outdated information from pre-training, leading to incorrect predictions or biased outputs during inference. While existing model editing methods can address this challenge, they struggle with editing large amounts of factual information simultaneously and may compromise the general capabilities of the models. In this paper, our empirical study demonstrates that it is feasible to edit the internal representations of LLMs and replace the entities in a manner similar to editing natural language inputs. Based on this insight, we introduce the Latent Knowledge Scalpel (LKS), an LLM editor that manipulates the latent knowledge of specific entities via a lightweight hypernetwork to enable precise and large-scale editing. Experiments conducted on Llama-2 and Mistral show even with the number of simultaneous edits reaching 10,000, LKS effectively performs knowledge editing while preserving the general abilities of the edited LLMs. Code is available at: https://github.com/Linuxin-xxx/LKS.

Containers are widely used in multi-tenant cloud computing for their ease of deployment, minimal overhead, and fast start-up. However, the intrinsic shared kernel model of containers poses significant security threats, risking confidentiality and integrity from co-located containers or compromised OS. Researchers have proposed various methods to protect containers from untrusted OS, but few consider both the universality and efficiency. In this paper, we present ConMonitor---a lightweight and efficient container protection architecture. ConMonitor protects the security of container application data by introducing a compact virtualization software, called ConVisor, as a trusted computing base. ConVisor enforces isolation of the physical memory between containers and the kernel, and monitors the sensitive operations performed by the OS. To ensure the security of ConMonitor, we implement a Container Guardian to serve as an intermediary for the kernel, managing sensitive operations. Moreover, we also leverage the VMFUNC feature to achieve fast context switching, thereby mitigating the performance penalty associated with frequent context switching. We have implemented ConMonitor on Intel CPU with Virtualization Technology, and the evaluation results show that ConMonitor can protect the security of container applications with a negligible performance overhead.

Device emulation is a vital aspect of virtualization, yet remains vulnerable to security threats. Prior research has focused on monitoring I/O data flow or identifying internal device anomalies but often falls short in precision and automation. In this paper, we propose a novel method that leverages the normal operations of an emulated device to formulate an execution specification. The specification acts as a criterion to evaluate the device's behavior and state transitions. We implement SEDSpec, a prototype system that automatically generates the execution specification for an emulated device and devises three check strategies for identifying any deviations from this specification, thereby ensuring normal operations and enhancing the security of the emulated device. We evaluate SEDSpec with five different execution specifications. The results show that SEDSpec can detect anomalies caused by vulnerability exploitation while maintaining the devices' regular functioning with minimal performance overhead.

Container technology has become a cornerstone of cloud computing, offering notable benefits such as enhanced resource utilization and streamlined deployment processes. The adoption of container technology by leading cloud service providers has steadily increased over the years. However, during the image construction phase, the reuse of base images and the execution of certain commands often results in the retention of redundant files, leading to resource wastage and potential security vulnerabilities. In this research, we systematically review and analyze existing methodologies, identify shortcomings in current approaches, and propose an automated image debloating tool named SummSlim according to the characteristics of the container image construction process. We selected 195 official images from Docker Hub for testing and evaluated the effectiveness of SummSlim with a success rate of $98.46 \%$. Then we compare and analyze the images before and after debloating, and make some novel suggestions for developers. To the best of our knowledge, SummSlim is the first practically available universal image debloating tool.

Microservice application architecture is one of the most widely used service architectures in the industry. To prevent a compromised microservice from abusing other microservices, authorization policy is applied to regulate the access among them. However, configuring access control policy manually is challenging due to the complexity and dynamic nature of microservice applications. In this paper, we present Log2Policy, a novel approach to generate microservice authorization policy based on access logs. Our approach consists of three fundamental techniques: (1) a log-based topological graph generation mechanism that automatically infers the invocation logic among microservices, (2) a machine learning based attributes mining method that extracts the relevant attributes of requests, and (3) a policy upgrade mechanism based on traffic management that can significantly reduce the upgrade time. We have implemented a prototype of Log2Policy on mainstream microservice infrastructures and have evaluated it with several microservice applications. The results show that Log2Policy can generate fine-grained and effective access control rules and upgrade them with negligible overhead.