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
A basic requirement of a secure computer system is that it be up to date with regard to software security patches. Unfortunately, Infrastructure as a Service (IaaS) clouds make this difficult. They leverage virtualization, which provides functionality that causes traditional security patch update systems to fail. In addition, the diversity of operating systems and the distributed nature of administration in the cloud compound the problem of identifying unpatched machines. In this work, we propose P2, a hypervisor-based patch audit solution. P2 audits VMs and detects the execution of unpatched binary and non-binary files in an accurate, continuous and OSagnostic manner. Two key innovations make P2 possible. First, P2 uses efficient information flow tracking to identify the use of unpatched non-binary files in a vulnerable way.We performed a patch survey and discover that 64% of files modified by security updates do not contain binary code, making the audit of non-binary files crucial. Second, P2 implements a novel algorithm that identifies binaries in mid-execution to allow handling of VMs resumed from a checkpoint or migrated into the cloud. We have implemented a prototype of P2 and and our experiments show that it accurately reports the execution of unpatched code while imposing performance overhead of 4%.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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