Trusted Execution Environments (TEEs) have become a cornerstone of confidential computing, attracting significant attention from academia and industry. To support secure and scalable application deployment on confidential clouds, TEE containers (Tcons) have been introduced as middleware to shield applications from malicious operating systems and orchestration layers while preserving usability. In this paper, we present the first comprehensive analysis of Tcons, focusing on three critical layers: OS interfaces, encrypted I/O, and orchestration mechanisms. To enable systematic evaluation, we design TBouncer, an automated analyzer that precisely exercises and benchmarks Tcon isolation boundaries. Our study uncovers fundamental flaws in existing Tcons, leading to exploitable vulnerabilities such as code execution, denial-of-service, and information leakage. In total, we identify six attack vectors, twelve new bugs, and three CVEs. These findings provide new insights into the underestimated attack surface of Tcons and highlight key directions for building more secure and trustworthy container solutions.

Filesystem isolation in container ecosystems is often weakened by cross-boundary path misresolution, causing path traversal (PaTra) vulnerabilities. These vulnerabilities stem from insecure host-container interactions and have become increasingly pervasive as cloud systems mount shared resources, such as GPUs and agent workspaces, into containers to support AI workloads. Existing defenses remain inadequate. Kernel-level protections are intrusive, can destabilize system calls, and have therefore not been accepted into the Linux mainline. Detection methods rely on static rule matching or manual code auditing. Static rules can flag path-related functions but fail to capture the semantics needed to determine whether a host-container interaction exists, causing many false positives. Manual review requires domain expertise, making it costly, inefficient, and difficult to scale. To address this threat, we present Bulkhead, an automated framework that integrates large language models (LLMs) with formal methods for semantic vulnerability discovery and remediation. Bulkhead uses a multi-agent system to identify and repair PaTra vulnerabilities through multi-dimensional knowledge patterns generalized from known cases. It first applies high-risk functional patterns to locate entry points for cross-boundary interactions in containerized code, then uses call-chain patterns to recover the corresponding execution paths at suitable depth. The Detection pipeline analyzes these call chains against the application scenarios and threat model, identifying vulnerabilities such as missing security checks and TOCTOU flaws in cross-boundary interactions, and generating proof-of-concept (PoC) exploits for validation. These PoCs then guide patch generation. To ensure remediation correctness, the Patch pipeline performs assertion-driven verification using predefined model-checking templates.

Software vulnerabilities have posed huge threats to the cyberspace security, and there is an increasing demand for automated vulnerability detection (VD). In recent years, deep learning-based (DL-based) vulnerability detection systems have been proposed for the purpose of automatic feature extraction from source code. Although these methods can achieve ideal performance on synthetic datasets, the accuracy drops a lot when detecting real-world vulnerability datasets. Moreover, these approaches limit their scopes within a single function, being not able to leverage the information between functions. In this paper, we attempt to extract the function's abstract behaviors, figure out the relationships between functions, and use this global information to assist DL-based VD to achieve higher performance. To this end, we build a Behavior Graph Model and use it to design a novel framework, namely VulBG. To examine the ability of our constructed Behavior Graph Model, we choose several existing DL-based VD models (e.g., TextCNN, ASTGRU, CodeBERT, Devign, and VulCNN) as our baseline models and conduct evaluations on two real-world datasets: the balanced $\text{FFMpeg}+\text{Qemu}$ dataset and the unbalanced $\text{Chrome} +\text{Debian}$ dataset. Experimental results indicate that VulBG enables all baseline models to detect more real vulnerabilities, thus improving the overall detection performance.

Filesystem isolation enforced by today's container technology has been found to be less effective in the presence of host-container interactions increasingly utilized by container tools. This weakened isolation has led to a type of path misresolution (Pamir) vulnerabilities, which have been considered to be highly risky and continuously reported over the years. In this paper, we present the first systematic study on the Pamir risk and the existing fixes to related vulnerabilities. Our research reveals that in spite of significant efforts being made to patch vulnerable container tools and address the risk, the Pamir vulnerabilities continue to be discovered, including a new vulnerability (CVE-2023-0778) we rediscovered from patched software. A key insight of our study is that the Pamir risk is inherently hard to prevent at the level of container tools, due to their heavy reliance on third-party components. While security inspections should be applied to all components to mediate host-container interactions, third-party component developers tend to believe that container tools should perform security checks before invoking their components, and are therefore reluctant to patch their code with the container-specific protection. Moreover, due to the large number of components today's container tools depend on, re-implementing all of them is impractical. Our study shows that kernel-based filesystem isolation is the only way to ensure isolation always in place during host-container interactions. In our research, we design and implement the first such an approach that extends the filesystem isolation to dentry objects, by enforcing access control on host-container interactions through the filesystem. Our design addresses the fundamental limitation of one-way isolation characterizing today's container, uses carefully-designed policies to ensure accurate and comprehensive interaction control, and implants the protection into the right kernel location to minimize the performance impact. We verify our approach using model checking, which demonstrates its effectiveness in eliminating the Pamir risk. Our evaluation further shows that our approach incurs negligible overheads, vastly outperforming all existing Pamir patches, and maintains compatibility with all mainstream container tools. We have released our code and filed a request to incorporate our technique into the Linux kernel.

The recent wave of in-browser cryptojacking has ebbed away, due to the new updates of mainstream cryptocurrrencies, which demand the level of mining resources browsers cannot afford. As replacements, resource-rich, loosely protected free Internet services, such as Continuous Integration (CI) platforms, have become attractive targets. In this paper, we report a systematic study on real-world illicit cryptomining on public CI platforms (called Cijacking). Unlike in-browser cryptojacking, Cijacks masquerade as CI jobs and are therefore more difficult to detect, since legitimate CI workflows such as container image building and testing also entail intensive computing. In our research, we leveraged the critical mining information the adversary has to specify, such as wallet addresses and mining pool domains, to recover the attack traces from GitHub repositories and the log files on CI platforms, leading to the discovery of 1,974 Cijacking instances, 30 campaigns across 12 different cryptocurrencies on 11 mainstream CI platforms. Further, our study unveils the evolution of attack strategies, in response to the protection put in place by the platforms, the duration of the mining jobs (as long as 33 months), and their lifecycle. Further discovered is the revenue of the attack, over ${\$}$20,000 per month. Since robust detection of cryptojacking is known to be hard, we developed a novel technique, called Cijitter, to strategically inject delays to the execution of a CI workflow to disproportionally penalize the mining jobs that need to work on a series of tasks under time constraints. Our analysis and evaluation, as conducted on both benchmarks and common CI jobs, show that our approach substantially suppresses the miner’s revenues, rendering them unprofitable, but only has small impacts on the performance of CI jobs and developer productivity (94.3% of CI jobs see a less than 10% delay).

As an emerging technique for confidential computing, trusted execution environment (TEE) receives a lot of attention. To better develop, deploy, and run secure applications on a TEE platform such as Intel's SGX, both academic and industrial teams have devoted much effort to developing reliable and convenient TEE containers. In this paper, we studied the isolation strategies of 15 existing TEE containers to protect secure applications from potentially malicious operating systems (OS) or untrusted applications, using a semi-automatic approach combining a feedback-guided analyzer with manual code review. Our analysis reveals the isolation protection each of these TEE containers enforces, and their security weaknesses. We observe that none of the existing TEE containers can fulfill the goal they set, due to various pitfalls in their design and implementation. We report the lessons learnt from our study for guiding the development of more secure containers, and further discuss the trend of TEE container designs. We also release our analyzer that helps evaluate the container middleware both from the enclave and from the kernel.

DDoS attacks are rampant in cloud environments and continually evolve into more sophisticated and intelligent modalities, such as low-rate DDoS attacks. But meanwhile, the cloud environment is also developing in constant. Now container technology and microservice architecture are widely applied in cloud environment and compose container-based cloud environment. Comparing with traditional cloud environments, the container-based cloud environment is more lightweight in virtualization and more flexible in scaling service. Naturally, a question that arises is whether these new features of container-based cloud environment will bring new possibilities to defeat DDoS attacks. In this paper, we establish a mathematical model based on queueing theory to analyze the strengths and weaknesses of the container-based cloud environment in defeating low-rate DDoS attack. Based on this, we propose a dynamic DDoS mitigation strategy, which can dynamically regulate the number of container instances serving for different users and coordinate the resource allocation for these instances to maximize the quality of service. And extensive simulations and testbed-based experiments demonstrate our strategy can make the limited system resources be utilized sufficiently to maintain the quality of service acceptable and defeat DDoS attack effectively in the container-based cloud environment.

Due to the lightweight features, the combination of container technology and microservice architecture makes container-based cloud environment more efficient and agile than VM-based cloud environment. However, it also greatly amplifies the dynamism and complexity of the cloud environment and increases the uncertainty of security issues in the system concurrently. In this case, the effectiveness of defense mechanisms with fixed strategies would fluctuate as the updates occur in cloud environment. We refer this problem as effectiveness drift problem of defense mechanisms, which is particularly acute in the proactive defense mechanisms, such as moving target defense (MTD). To tackle this problem, we present DSEOM, a framework that can automatically perceive updates of container-based cloud environment, rapidly evaluate the effectiveness change of MTD and dynamically optimize MTD strategies. Specifically, we establish a multi-dimensional attack graphs model to formalize various complex attack scenarios. Combining with this model, we introduce the concept of betweenness centrality to effectively evaluate and optimize the implementation strategies of MTD. In addition, we present a series of security and performance metrics to quantify the effectiveness of MTD strategies in DSEOM. And we conduct extensive experiments to illustrate the existence of the effectiveness drift problem and demonstrate the usability and scalability of DSEOM.