Vulnerability Patch Verification for Military Software Systems Through AI-Driven Code-Level Rule Generation
Bibliographic record
Abstract
Patch verification is critical in military systems to ensure that known vulnerabilities are effectively addressed, preventing them from being exploited. Without proper verification, unpatched software could allow adversaries to exploit vulnerabilities, leading to unauthorized access, compromised operations, or even mission failure. In high-stakes environments such as military operations, patch verification is essential for maintaining the security, integrity, and readiness of both software and firmware, particularly in systems that manage sensitive information or control mission-critical equipment. Traditional methods that rely on version strings to verify vulnerability patching are often insufficient. For example, the Heartbleed vulnerability (CVE-2014-0160) affected OpenSSL versions 1.0.1 through 1.0.lf. A system running OpenSSL 1.0.lf might still be flagged as vulnerable, even if a custom patch was applied, in the event that the version string was not updated by the software maintainer fixing the vulnerability. This will lead to false positives in the vulnerability detection process. Conversely, a system may appear secure based on the version string, but if the patch was not correctly implemented, the vulnerability will remain, resulting in false negatives. To address these limitations, this paper presents a new scalable, artificial intelligence-based code-level verification system. By leveraging large language models to generate rules that analyze the actual executable code, this approach verifies whether vulnerabilities have been properly fixed, regardless of version metadata. Additionally, it can pinpoint the exact location of exploitable code as a more accurate and reliable method for detecting and confirming patches. Our experiment, involving 1,466 vulnerable software records with over 4,000 instances, demonstrates that the rule generation system is both accurate and robust.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".