Splitting interfaces: making trust between applications and operating systems configurable
Bibliographic record
Abstract
In current commodity systems, applications have no way of limiting their trust in the underlying operating system (OS), leaving them at the complete mercy of an attacker who gains control over the OS. In this work, we describe the design and implementation of Proxos, a system that allows applications to configure their trust in the OS by partitioning the system call interface into trusted and untrusted components. System call routing rules that indicate which system calls are to be handled by the untrusted commodity OS, and which are to be handled by a trusted private OS, are specified by the application developer. We find that rather than defining a new system call interface, routing system calls of an existing interface allows applications currently targeted towards commodity operating systems to isolate their most sensitive components from the commodity OS with only minor source code modifications. We have built a prototype of our system on top of the Xen Virtual Machine Monitor with Linux as the commodity OS. In practice, we find that the system call routing rules are short and simple---on the order of 10's of lines of code. In addition, applications in Proxos incur only modest performance overhead, with most of the cost resulting from inter-VM context switches.
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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.018 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".