Talks

Identifying the impact scope and scale is critical for software supply chain vulnerability assessment. However, existing studies face substantial limitations. First, prior studies either work at coarse package-level granularity producing many false positives or fail to accomplish whole-ecosystem vulnerability propagation analysis. Second, although vulnerability assessment indicators like CVSS characterize individual vulnerabilities, no metric exists to specifically quantify the dynamic impact of vulnerability propagation across software supply chains. To address these limitations and enable accurate and comprehensive vulnerability impact assessment, we propose a novel approach: (i) a hierarchical worklist-based algorithm for whole-ecosystem and call-graph-level vulnerability propagation analysis and (ii) the Vulnerability Propagation Scoring System (VPSS), a dynamic metric to quantify the scope and evolution of vulnerability impacts in software supply chains. We implement a prototype of our approach in the Java Maven ecosystem and evaluate it on 100 real-world vulnerabilities. Experimental results demonstrate that our approach enables effective ecosystem-wide vulnerability propagation analysis, and provides a practical, quantitative measure of vulnerability impact through VPSS.

Linux kernel vulnerability reproduction is a critical task in system security. To reproduce a kernel vulnerability, the vulnerable environment and the Proof of Concept (PoC) program are needed. Most existing research focuses on the generation of PoC, while the construction of environment is overlooked. However, establishing an effective vulnerable environment to trigger a vulnerability is challenging. Firstly, it is hard to guarantee that the selected kernel version for reproduction is vulnerable, as the vulnerability version claims in online databases can occasionally be incorrect. Secondly, many vulnerabilities cannot be reproduced in kernels built with default configurations. Intricate non-default kernel configurations must be set to include and trigger a kernel vulnerability, but less information is available on how to recognize these configurations.

To solve these challenges, we propose a patch-based approach to identify real vulnerable kernel versions and a graph-based approach to identify necessary configs for activating a specific vulnerability. We implement these approaches in a tool, KernJC, automating the generation of vulnerable environments for kernel vulnerabilities. To evaluate the efficacy of KernJC, we build a dataset containing 66 representative real-world vulnerabilities with PoCs from kernel vulnerability research in the past five years. The evaluation shows that KernJC builds vulnerable environments for all these vulnerabilities, 32 (48.5%) of which require non-default configs, and 4 have incorrect version claims in the National Vulnerability Database (NVD). Furthermore, we conduct large-scale spurious version detection on kernel vulnerabilities and identify 128 vulnerabilities that have spurious version claims in NVD. To foster future research, we release KernJC with the dataset in the community.

Software vulnerabilities pose critical security and risk concerns. Many techniques are proposed to assess and prioritize vulnerabilities. To evaluate their performance, researchers often craft datasets from limited data sources, lacking a global overview of broad vulnerability intelligence. The repetitive data preparation process complicates the evaluation of new solutions. To solve this issue, we propose VulZoo, a comprehensive vulnerability intelligence dataset that covers 17 vulnerability data sources. We also construct connections among these sources, enabling more straightforward configuration and adaptation for different tasks. VulZoo provides utility scripts for automatic data synchronization and cleaning, relationship mining, and statistics generation. We make VulZoo publicly available and maintain it with incremental updates. We believe that VulZoo serves as a valuable input to vulnerability assessment and prioritization studies.

Linux kernel vulnerability reproduction is a critical task in system security. To reproduce a kernel vulnerability, the vulnerable environment and the Proof of Concept (PoC) program are needed. Most existing research focuses on the generation of PoC, while the construction of environment is overlooked. However, establishing an effective vulnerable environment to trigger a vulnerability is challenging. Firstly, it is hard to guarantee that the selected kernel version for reproduction is vulnerable, as the vulnerability version claims in online databases can occasionally be incorrect. Secondly, many vulnerabilities cannot be reproduced in kernels built with default configurations. Intricate non-default kernel configurations must be set to include and trigger a kernel vulnerability, but less information is available on how to recognize these configurations.

To solve these challenges, we propose a patch-based approach to identify real vulnerable kernel versions and a graph-based approach to identify necessary configs for activating a specific vulnerability. We implement these approaches in a tool, KernJC, automating the generation of vulnerable environments for kernel vulnerabilities. To evaluate the efficacy of KernJC, we build a dataset containing 66 representative real-world vulnerabilities with PoCs from kernel vulnerability research in the past five years. The evaluation shows that KernJC builds vulnerable environments for all these vulnerabilities, 32 (48.5%) of which require non-default configs, and 4 have incorrect version claims in the National Vulnerability Database (NVD). Furthermore, we conduct large-scale spurious version detection on kernel vulnerabilities and identify 128 vulnerabilities that have spurious version claims in NVD. To foster future research, we release KernJC with the dataset in the community.

This industry research talk examines the trust boundary between runC and the Linux kernel. It revisits the repair of CVE-2019-5736, comparing a sealed memfd copy with a read-only bind mount, and explains how CVE-2022-0847 (Dirty Pipe) can bypass the latter under historical vulnerable kernel conditions. The study develops a constrained static ELF file-injection design that preserves the original runC control flow without growing the file, and demonstrates it through an existing-container scenario and an image-based scenario. It concludes with defenses across workload security, the image supply chain, runtime integrity, and infrastructure updates. The research source code was not released because of its attack impact.

This talk introduces Metarget, an innovative framework designed for the automatic construction of vulnerable cloud-native environments. By facilitating the deployment of multi-layered, vulnerable infrastructures, Metarget enables researchers and ethical hackers to efficiently simulate complex attack scenarios ranging from container exploitation to cluster-level persistence. The presentation highlights offensive methodologies in cloud-native security, including real-world case studies such as post-penetration attacks against Kubernetes clusters. Using Metarget, we explore vulnerabilities like CVE-2020-15257 and CVE-2020-8559, demonstrating how attackers can achieve lateral movement and full cluster compromise. Additionally, we showcase k0otkit, a post-penetration persistence technique for Kubernetes, emphasizing its role in automating and advancing offensive security research. Through a detailed analysis of offensive strategies, this talk illustrates how tools like Metarget accelerate defensive innovations, paving the way for more robust cloud-native security practices.

This presentation introduces k0otkit, a universal post-penetration control technique for Kubernetes (K8s) clusters. By leveraging Kubernetes-native features such as DaemonSets, Secrets, and container injection, k0otkit provides attackers with rapid, covert, and persistent control over large-scale clusters. This talk explores the evolution of k0otkit through its various iterations, highlighting advancements in stealth, persistence, and efficiency, including the adoption of fileless attack techniques and encrypted communication. The discussion outlines a typical Kubernetes penetration process, emphasizing container escape, privilege escalation, and lateral movement, leading to full cluster control. Through live demonstrations, we showcase how k0otkit exploits Kubernetes vulnerabilities, automates reverse shell deployment, and achieves seamless cluster-wide compromise. Finally, the talk concludes with key defensive strategies to mitigate these risks, including implementing Pod security policies, detecting anomalous container behavior, and protecting against fileless attacks. This comprehensive analysis offers valuable insights for both offensive and defensive Kubernetes security research.