Jinwoo Kim

This paper introduces BeaCon, a novel tool for the automated generation of adjustable container security policies. Unlike prior approaches, BeaCon leverages dynamic analysis to simulate realistic environments, uncovering container execution paths that may remain hidden during the profiling phase. To address the challenge of exploring vast profiling spaces, we employ efficient heuristics to reveal additional system events with minimal effort. In addition, BeaCon incorporates a security and functionality scoring mechanism to prioritize system calls and capabilities based on their impact on the host OS kernel's security and the functionality of containerized applications. By integrating these scores, BeaCon achieves a customized balance between security and functionality, enabling cloud providers to enforce security measures while maintaining tenant availability. We implemented a prototype of BeaCon using eBPF kernel technology and conducted extensive evaluations. Results from the top 15 containers, which revealed significant improvements, demonstrate that BeaCon identifies an average of 16.5% additional syscalls by applying diverse environments. Furthermore, we evaluated its effectiveness in mitigating risks associated with 45 known vulnerabilities (e.g., CVEs), showcasing its potential to significantly enhance container security. Additionally, we performed proof-of-concept demonstrations for two well-known security vulnerabilities, showing that BeaCon successfully reduces attack surface by blocking these exploits.

Modern enterprise networks comprise diverse and heterogeneous systems that support a wide range of services, making it challenging for administrators to track and analyze sophisticated attacks such as advanced persistent threats (APTs), which often exploit multiple vectors. To address this challenge, we introduce the concept of network-level security provenance, which enables the systematic establishment of causal relationships across hosts at the network level, facilitating the accurate identification of the root causes of security incidents. Building on this concept, we present SecTracer as a framework for a network-wide provenance analysis. SecTracer offers three main contributions: (i) comprehensive and efficient forensic data collection in enterprise networks via software-defined networking (SDN), (ii) reconstruction of attack histories through provenance graphs to provide a clear and interpretable view of intrusions, and (iii) proactive attack prediction using probabilistic models. We evaluated the effectiveness and efficiency of SecTracer through a real-world APT simulation, demonstrating its capability to enhance threat mitigation while introducing less than 1% network throughput overhead and negligible latency impact.

Serverless computing has rapidly emerged as a prominent cloud paradigm, enabling developers to focus solely on application logic without the burden of managing servers or underlying infrastructure. Public serverless repositories have become key to accelerating the development of serverless applications. However, their growing popularity makes them attractive targets for adversaries. Despite this, the security posture of these repositories remains largely unexplored, exposing developers and organizations to potential risks. In this paper, we present the first comprehensive analysis of the security landscape of serverless components hosted in public repositories. We analyse 2,758 serverless components from five widely used public repositories popular among developers and enterprises, and 125,936 Infrastructure as Code (IaC) templates across three widely used IaC frameworks. Our analysis reveals systemic vulnerabilities including outdated software packages, misuse of sensitive parameters, exploitable deployment configurations, susceptibility to typo-squatting attacks and opportunities to embed malicious behaviour within compressed serverless components. Finally, we provide practical recommendations to mitigate these threats.

Credential stuffing attacks have caused significant harm to online users who frequently reuse passwords across multiple websites. While prior research has attempted to detect users with reused passwords or identify malicious login attempts, existing methods often compromise usability by restricting password creation or website access, and their reliance on complex account-sharing mechanisms hinders real-world deployment. To address these limitations, we propose PassREfinder-FL, a novel framework that predicts credential stuffing risks across websites. We introduce the concept of password reuse relations -- defined as the likelihood of users reusing passwords between websites -- and represent them as edges in a website graph. Using graph neural networks (GNNs), we perform a link prediction task to assess credential reuse risk between sites. Our approach scales to a large number of arbitrary websites by incorporating public website information and linking newly observed websites as nodes in the graph. To preserve user privacy, we extend PassREfinder-FL with a federated learning (FL) approach that eliminates the need to share user sensitive information across administrators. Evaluation on a real-world dataset of 360 million breached accounts from 22,378 websites shows that PassREfinder-FL achieves an F1-score of 0.9153 in the FL setting. We further validate that our FL-based GNN achieves a 4-11% performance improvement over other state-of-the-art GNN models through an ablation study. Finally, we demonstrate that the predicted results can be used to quantify password reuse likelihood as actionable risk scores.

In modern containerized cloud environments, the adoption of RDMA (Remote Direct Memory Access) has expanded to reduce CPU overhead and enable high-performance data exchange. Achieving this requires strong performance isolation to ensure that one container's RDMA workload does not degrade the performance of others, thereby maintaining critical security assurances. However, existing isolation techniques are difficult to apply effectively due to the complexity of microarchitectural resource management within RDMA NICs (RNICs). This paper experimentally analyzes two types of resource exhaustion attacks on NVIDIA BlueField-3: (i) state saturation attacks and (ii) pipeline saturation attacks. Our results show that state saturation attacks can cause up to a 93.9% loss in bandwidth, a 1,117x increase in latency, and a 115% rise in cache misses for victim containers, while pipeline saturation attacks lead to severe link-level congestion and significant amplification, where small verb requests result in disproportionately high resource consumption. To mitigate these threats and restore predictable security assurances, we propose HT-Verbs, a threshold-driven framework based on real-time per-container RDMA verb telemetry and adaptive resource classification that partitions RNIC resources into hot, warm, and cold tiers and throttles abusive workloads without requiring hardware modifications.