Extended Berkeley Packet Filter (eBPF) has emerged as a kernel-level framework enabling dynamic security enforcement in modern operating systems. While eBPF's cybersecurity potential has attracted significant attention, existing work remains fragmented across domains, evaluation methodologies, and deployment contexts. This systematic literature review applies PRISMA methodology to identify, categorize, and synthesize peer-reviewed research on eBPF-based cybersecurity mechanisms. Following a structured screening of 3,735 records from six databases, 54 primary studies (2018-2026) were analyzed and organized into a seven-domain taxonomy: DDoS mitigation, intrusion detection, IoT security, container security, microservice protection, networking, and security tools. Analysis reveals eBPF enables low-overhead security enforcement (median 2.4% CPU overhead [1.1-8.6%], ranging from nanosecond-scale costs for infrequent hooks to 10-20% for kernel hot paths) with high detection accuracy (94-99%). It particularly excels in kernel-level monitoring, real-time packet processing, and cloud-native workload protection. However, significant challenges persist: verifier-imposed constraints limit algorithm complexity, 85.1% (46/54) of studies require low-level programming expertise, kernel version fragmentation hinders portability, and 96.2% (52/54) of research fails to address eBPF's own vulnerabilities. This review identifies critical research gaps in multi-tenant isolation, adversarial machine learning (ML) robustness, production validation, and standardized evaluation frameworks. By consolidating fragmented knowledge and highlighting architectural trade-offs between safety and expressiveness, this work provides a foundation for next-generation eBPF security systems and actionable directions for kernel programmability research.

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