A Flow-Sensitive Refinement Type System for Verifying eBPF Programs
Bibliographic record
Abstract
The Extended Berkeley Packet Filter ( eBPF ) subsystem within an operating system’s kernel enables userspace programs to extend kernel functionality dynamically. Due to the security risks associated with runtime modification of the operating system, eBPF requires all programs to be verified before deploying them within the kernel. Existing approaches to eBPF verification are monolithic, requiring their entire analysis to be done in a secure environment, resulting in the need for extensive trusted codebases. We present a typebased verification approach that automatically infers proof certificates in userspace, thus reducing the size and complexity of the trusted codebase. At the same time, only the proof-checking component needs to be deployed in a secure environment. Moreover, compared to previous techniques, our type system enhances the debuggability of the programs for users through ergonomic type annotations when verification fails. We implemented our type inference algorithm in a tool called VeRefine and evaluated it against an existing eBPF verifier, Prevail . VeRefine outperformed Prevail on most of the industrial benchmarks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".