Bibliographic record
Abstract
The number of real-time embedded devices is increasing, especially in critical places such as industrial and medical devices. These devices are the target of many security attacks; therefore, their security must be ensured, and existing vulnerabilities must be fixed immediately. Typical update approaches require rebooting or halting the devices for an unpredictable time, and are hence not applicable for real-time embedded devices such as medical devices, which must run continuously without rebooting. Hotpatching, which patches the code without rebooting the device, has been used in this context. However, existing hotpatching methods require manual effort from programmers that is error-prone and time-consuming. Further, little attention has been paid to these techniques for real-time embedded devices. This paper proposes AutoPatch, the first automatic hotpatching approach for real-time embedded devices. AutoPatch automatically analyzes the official patch to extract its semantics using predicate abstraction, and generates a semantically equivalent patch called hotpatch. Our initial results show that AutoPatch can automatically generate hotpatches correctly based on the official patches (i.e., real-world CVEs) using program analysis. We also validate that the generated hotpatch can fix the vulnerabilities without rebooting or halting the devices.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".