Bibliographic record
Abstract
Process confinement is a key requirement for workloads in the cloud and in other contexts. Existing process confinement mechanisms on Linux, however, are complex and inflexible because they are implemented using a combination of primitive abstractions (e.g., namespaces, cgroups) and complex security mechanisms (e.g., SELinux, AppArmor) that were designed for purposes beyond basic process confinement. We argue that simple, efficient, and flexible confinement can be better implemented today using eBPF, an emerging technology for safely extending the Linux kernel. We present a proof-of-concept confinement application, bpfbox, that uses less than 2000 lines of kernelspace code and allows for confinement at the userspace function, system call, LSM hook, and kernelspace function boundaries---something that no existing process confinement mechanism can do. Further, it does so using a policy language simple enough to use for ad-hoc confinement purposes. This paper presents the motivation, design, implementation, and benchmarks of bpfbox, including a sample web server confinement policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.019 |
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".