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eBPF Traffic Collection

What is eBPF?​

eBPF (extended Berkeley Packet Filter) is a technology that allows running sandboxed programs in the Linux kernel without changing kernel source code or loading kernel modules. Originally designed for packet filtering, eBPF has evolved into a general-purpose execution engine that enables safe and efficient kernel programming. Many Kubernetes network technologies such as Envoy and Cilium are moving to eBPF to reduce resource utilization.

How eBPF Works for Network Traffic Collection​

Kernel-Level Observability​

eBPF programs run directly in kernel space, providing unprecedented visibility into network traffic with minimal overhead. Unlike traditional packet capture methods that require copying packets from kernel to user space, eBPF can process network data directly where it originates.

Key advantages include:

  • Zero-copy operations: Data can be analyzed without expensive memory copies
  • Minimal performance impact: Programs run in kernel space with native performance
  • Real-time processing: Traffic analysis happens as packets flow through the network stack
  • Comprehensive visibility: Access to both network packets and kernel metadata

Traffic Capture Architecture​

eBPF programs attach to various kernel hook points to intercept network traffic:

Socket-Level Hooks​

  • Socket create/destroy: Track connection lifecycle
  • Socket send/receive: Monitor data transmission
  • Socket state changes: Observe connection state transitions

Network Stack Hooks​

  • TC (Traffic Control): Capture packets at the network interface level
  • XDP (eXpress Data Path): Ultra-fast packet processing at the driver level
  • Netfilter hooks: Integrate with iptables and connection tracking

System Call Tracing​

  • syscall entry/exit: Monitor application network API calls
  • kprobes/kretprobes: Trace specific kernel functions
  • tracepoints: Use predefined kernel instrumentation points

User-Space Instrumentation​

  • uprobes/uretprobes: Instrument user-space functions for TLS visibility
  • USDT (User Statically Defined Tracing): Application-specific tracing points
  • Library function hooking: Monitor SSL/TLS library calls

Data Collection Process​

  1. Program Loading: eBPF programs are compiled to bytecode and loaded into the kernel
  2. Verification: Kernel verifier ensures programs are safe and will terminate
  3. Attachment: Programs attach to appropriate kernel hooks
  4. Event Generation: Network events trigger program execution
  5. Data Aggregation: Programs collect and process network metadata
  6. User Space Delivery: Processed data is delivered to user space applications

TCP Traffic Analysis​

For TCP traffic specifically, eBPF enables deep inspection capabilities:

Connection Tracking​

  • Monitor TCP handshake establishment
  • Track connection state throughout lifecycle
  • Detect connection failures and anomalies

Data Flow Analysis​

  • Capture request/response pairs
  • Measure latency and throughput
  • Analyze data patterns and sizes

Performance Metrics​

  • Round-trip time (RTT) measurements
  • Congestion window analysis
  • Retransmission detection
  • Bandwidth utilization

TLS Traffic Visibility​

One of the most powerful capabilities of eBPF for API testing is the ability to inspect encrypted TLS traffic without requiring certificate management or SSL termination.

User-Space uprobes for TLS Decryption​

eBPF uprobes (user-space probes) can instrument SSL/TLS library functions to capture plaintext data before encryption and after decryption:

OpenSSL Instrumentation

  • SSL_write(): Capture outbound plaintext data before encryption
  • SSL_read(): Capture inbound plaintext data after decryption
  • SSL_write_ex() / SSL_read_ex(): Support for newer OpenSSL APIs
  • BIO_write() / BIO_read(): Low-level I/O operations for comprehensive coverage

Library Detection and Compatibility

  • Automatic detection of OpenSSL, BoringSSL, and LibreSSL versions
  • Dynamic symbol resolution for different library versions
  • Support for statically linked applications through binary analysis
  • Fallback mechanisms for custom TLS implementations

Benefits for Encrypted Traffic Analysis​

Complete API Visibility

  • Full HTTP/HTTPS request and response bodies
  • Headers, cookies, and authentication tokens
  • JSON, XML, and binary payload inspection
  • GraphQL queries and responses over HTTPS

Zero Configuration

  • No certificate installation or management required
  • No application code modifications needed
  • No proxy deployment or traffic redirection
  • Transparent operation with existing infrastructure

Production Safety

  • Read-only access to application memory
  • No modification of encryption keys or certificates
  • Minimal performance impact (typically <2% overhead)
  • Fail-safe operation prevents application crashes

Implementation Considerations for TLS​

Memory Layout Compatibility

  • Handle different SSL library versions and memory layouts
  • Adapt to compiler optimizations and inlining
  • Support for containerized applications with varying library versions

Data Correlation

  • Match encrypted network packets with decrypted payloads
  • Maintain connection state across SSL sessions
  • Handle connection multiplexing and HTTP/2 streams

Security Boundaries

  • Respect process isolation and user permissions
  • Audit logging for compliance requirements
  • Optional data masking for sensitive information

Security and Safety​

eBPF's design ensures safe execution within the kernel:

  • Verifier: Static analysis prevents infinite loops and invalid memory access
  • Sandboxing: Programs run in isolated execution context
  • Resource limits: Bounded execution time and memory usage
  • Permission model: Fine-grained access control to kernel features

Implementation Considerations​

Kernel Requirements​

  • Linux kernel 5.1+
  • eBPF-enabled kernel configuration
  • Sufficient kernel memory for program loading

Permissions​

  • CAP_BPF capability (Linux 5.8+) or CAP_SYS_ADMIN
  • Appropriate seccomp and AppArmor policies
  • Container runtime privilege requirements

Performance Tuning​

  • Program optimization for hot code paths
  • Efficient data structures and algorithms
  • Minimal memory allocations in kernel space
  • Batched data delivery to user space

Sidecar vs eBPF​

Advantages of Kubernetes Sidecars​

  • Language Agnostic: Works with any application regardless of programming language or framework.
  • Rich Protocol Support: Can handle complex protocols (HTTP/2, gRPC, etc.) and TLS termination.
  • User-Space Flexibility: Easier to update, debug, and extend without kernel dependencies.
  • Fine-Grained Control: Can inject, modify, or block traffic at the application layer.
  • Mature Ecosystem: Integrates with existing service mesh and observability tools.

Advantages of eBPF​

  • Low Overhead: Kernel-level visibility with minimal performance impact.
  • Transparent Operation: No need to modify application code or deployment manifests.
  • Comprehensive Coverage: Captures all network traffic, including non-proxied and encrypted flows.
  • Security Isolation: Runs in a sandboxed environment with strict safety checks.
  • Dynamic Instrumentation: Can attach to running systems without restarts or redeployments.

Interested in leveraging eBPF for advanced traffic collection? Contact us at support@speedscale.com to learn more about our eBPF collector beta program and how it can enhance your API testing capabilities.