POSTER: Leveraging eBPF to enhance sandboxing of WebAssembly runtimes
Bibliographic record
Abstract
WebAssembly is a binary instruction format designed as a portable compilation target enabling the deployment of untrusted code in a safe and efficient manner. While it was originally designed to be run inside web browsers, modern runtimes like Wasmtime and WasmEdge can execute WebAssembly directly on various systems. In order to access system resources with a universal hostcall interface, a standardization effort named WebAssembly System Interface (WASI) is currently undergoing. With specific regard to the file system, runtimes must prevent hostcalls to access arbitrary locations, thus they introduce security checks to only permit access to a pre-defined list of directories. This approach not only suffers from poor granularity, it is also error-prone and has led to several security issues. In this work we replace the security checks in hostcall wrappers with eBPF programs, enabling the introduction of fine-grained per-module policies. Preliminary experiments confirm that our approach introduces limited overhead to existing runtimes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".