Hypervisor support for identifying covertly executing binaries
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
Hypervisors have been proposed as a security tool to defend against malware that subverts the OS kernel. However, hypervisors must deal with the semantic gap between the low-level information available to them and the high-level OS abstractions they need for analysis. To bridge this gap, systems have proposed making assumptions derived from the kernel source code or symbol information. Unfortunately, this information is nonbinding – rootkits are not bound to uphold these assumptions and can escape detection by breaking them. In this paper, we introduce Patagonix, a hypervisorbased system that detects and identifies covertly executing binaries without making assumptions about the OS kernel. Instead, Patagonix depends only on the processor hardware to detect code execution and on the binary format specifications of executables to identify code and verify code modifications. With this, Patagonix can provide trustworthy information about the binaries running on a system, as well as detect when a rootkit is hiding or tampering with executing code. We have implemented a Patagonix prototype on the Xen 3.0.3 hypervisor. Because Patagonix makes no assumptions about the OS kernel, it can identify code from application and kernel binaries on both Linux and Windows XP. Patagonix introduces less than 3 % overhead on most applications. 1
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 | 0.000 |
| Science and technology studies | 0.001 | 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