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BGPFuzz: Automated Configuration Fuzzing of the Border Gateway Protocol

Chenlu Zhang, Amirmohammad Pasdar, Van-Thuan Pham

TL;DR

The paper tackles the problem of misconfigurations in BGP causing outages by introducing BGPFuzz, a structure-aware and stateful fuzzing framework. It combines grammar-based mutation, runtime state feedback, and multi-oracle anomaly detection to automatically generate and validate BGP configurations in a virtualized environment, avoiding disruption of real networks. The approach yields improved bug discovery and input validity over random fuzzing, demonstrating its potential to uncover realistic misconfiguration-induced faults like max-prefix violations and sub-prefix hijacks. This work lays the groundwork for scalable, automated validation of BGP configurations with practical impact for operators and researchers alike.

Abstract

Telecommunications networks rely on configurations to define routing behavior, especially in the Border Gateway Protocol (BGP), where misconfigurations can lead to severe outages and security breaches, as demonstrated by the 2021 Facebook outage. Unlike existing approaches that rely on synthesis or verification, our work offers a cost-effective method for identifying misconfigurations resulting from BGP's inherent complexity or vendor-specific implementations. We present BGPFuzz, a structure-aware and stateful fuzzing framework that systematically mutates BGP configurations and evaluates their effects in virtualized network. Without requiring predefined correctness properties as in static analysis, BGPFuzz detects anomalies through runtime oracles that capture practical symptoms such as session resets, blackholing, and traffic redirection. Our experiments show that BGPFuzz can reliably reproduce and detect known failures, including max-prefix violations and sub-prefix hijacks.

BGPFuzz: Automated Configuration Fuzzing of the Border Gateway Protocol

TL;DR

The paper tackles the problem of misconfigurations in BGP causing outages by introducing BGPFuzz, a structure-aware and stateful fuzzing framework. It combines grammar-based mutation, runtime state feedback, and multi-oracle anomaly detection to automatically generate and validate BGP configurations in a virtualized environment, avoiding disruption of real networks. The approach yields improved bug discovery and input validity over random fuzzing, demonstrating its potential to uncover realistic misconfiguration-induced faults like max-prefix violations and sub-prefix hijacks. This work lays the groundwork for scalable, automated validation of BGP configurations with practical impact for operators and researchers alike.

Abstract

Telecommunications networks rely on configurations to define routing behavior, especially in the Border Gateway Protocol (BGP), where misconfigurations can lead to severe outages and security breaches, as demonstrated by the 2021 Facebook outage. Unlike existing approaches that rely on synthesis or verification, our work offers a cost-effective method for identifying misconfigurations resulting from BGP's inherent complexity or vendor-specific implementations. We present BGPFuzz, a structure-aware and stateful fuzzing framework that systematically mutates BGP configurations and evaluates their effects in virtualized network. Without requiring predefined correctness properties as in static analysis, BGPFuzz detects anomalies through runtime oracles that capture practical symptoms such as session resets, blackholing, and traffic redirection. Our experiments show that BGPFuzz can reliably reproduce and detect known failures, including max-prefix violations and sub-prefix hijacks.

Paper Structure

This paper contains 10 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Approach
  4. Framework Overview
  5. Input Quality Improvement
  6. Structure-Aware Mutation Strategy
  7. State-Aware Input Generation
  8. Bug Detector Design
  9. Preliminary Evaluation
  10. Future Work

Figures (3)

  • Figure 1: An example telecommunications network illustrating connections between different Autonomous Systems (ASes).
  • Figure 2: The workflow of our approach to identify BGP vulnerabilities in a given network topology.
  • Figure 3: BGPFuzz Runtime State Machine. States: $S_0$ = Normal Run, $S_1$ = Intermediate (No Error), $S_2$ = Error Detected. Transitions are labeled $E_j$.