Improving error messages for eBPF programs

Abstract

Context: eBPF is an emerging technology in cloud computing, allowing user-defined programs to run in kernel space for observability, networking, and security. To ensure system integrity, the kernel relies on the eBPF verifier, a static analyzer that rejects potentially unsafe code. However, the verifier’s error messages are notoriously difficult to understand, generally referencing low-level bytecode rather than the original C source and making debugging a difficult and time-consuming task. Objective: The goal of this work is to improve the eBPF verifier error messages and make debugging easier by mapping verification errors back to the original C source code and by providing more understandable feedback to developers. Methods: This paper presents Pretty Verifier, a tool designed to improve the eBPF verifier error messages. By analyzing the verifier log and the compiler debug information, the tool maps verification errors back to the specific lines of C code, providing human-readable explanations and actionable fix suggestions. To rigorously validate the tool despite the scarcity of faulty eBPF datasets, we developed a fuzzing framework based on the BRF semantic fuzzer, capable of generating a balanced dataset of broken programs. Results: Experimental results on over 400 test cases demonstrate that the tool successfully localizes errors in 84% of cases and provides precise, context-aware explanations. Conclusion: Pretty Verifier significantly improves the developer’s experience and facilitates the resolution of critical security issues by improving the readability of eBPF verifier messages and strengthening their connection to the original C source code.