eBPF-PATROL: Protective Agent for Threat Recognition and Overreach Limitation using eBPF in Containerized and Virtualized Environments

TL;DR

eBPF-PATROL is a lightweight, extensible security agent that uses eBPF technology to monitor and enforce runtime security policies in containerized and virtualized environments, effectively detecting and preventing various attacks.

Contribution

The paper introduces eBPF-PATROL, a novel, context-aware security framework leveraging eBPF for real-time threat detection and prevention in cloud-native environments.

Findings

Low overhead (< 2.5%) during operation

High detection accuracy for real-world attacks

Effective prevention of privilege escalation and container escape

Abstract

With the increasing use and adoption of cloud and cloud-native computing, the underlying technologies (i.e., containerization and virtualization) have become foundational. However, strict isolation and maintaining runtime security in these environments has become increasingly challenging. Existing approaches like seccomp and Mandatory Access Control (MAC) frameworks offer some protection up to a limit, but often lack context awareness, syscall argument filtering, and adaptive enforcement, providing the ability to adjust decisions at runtime based on observed application behavior, workload changes, or detected anomalies rather than relying solely on static or predefined rules.This paper introduces eBPF-PATROL (eBPF-Protective Agent for Threat Recognition and Overreach Limitation), an extensible lightweight runtime security agent that uses extended Berkeley Packet Filter (eBPF) technology to monitor and enforce policies in containerized and virtualized environments. By intercepting system calls, analyzing execution context, and applying user-defined rules, eBPF-PATROL detects and prevents real-time boundary violations, such as reverse shells, privilege escalation, and container escape attempts. We describe the architecture, implementation, and evaluation of eBPF-PATROL, demonstrating its low overhead (< 2.5 percent) and high detection accuracy across real-world attack scenarios.