Application-level isolation and recovery with solitude
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When computer systems are compromised by an attack, it is difficult to determine the precise extent of the damage caused by the attack because the state changes made by an attacker and those made by regular users can be closely intertwined. This problem occurs due to implicit sharing in operating systems, and it can be especially severe for persistent state. In particular, the file system provides a single namespace that when compromised can have cascading effects on the entire system, making intrusion analysis and recovery a time-consuming and error-prone process. In this paper, we present Solitude, an application-level isolation and recovery system that is designed to both limit the effects of attacks and simplify the post-intrusion recovery process. Solitude uses a copy-on-write filesystem to provide a transparent, restricted privilege isolation environment for running untrusted applications, and it uses an explicit file sharing mechanism across the isolation environments that limits attack propagation without compromising functionality. Solitude provides two modes of recovery. If a sandboxed application proves to be untrustworthy, a course-grained recovery method allows easily removing the footprint of the software. However, if a user mistakenly moves malicious files to the trusted environment via explicit file sharing, then Solitude uses data dependency tracking to allow fine-grained recovery.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it