Hy2: A Hybrid Vulnerability Analysis Method
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
Software vulnerabilities remain an ever-present problem. Factors such as software complexity, size, and diversity of vulnerabilities drive the need for automated vulnerability analysis solutions. Past vulnerability analysis methods struggle with nondeterminism and uncertainty introduced by the environment and external dependencies. To address this problem, we present our vulnerability analysis method Hy2, a double hybrid of runtime verification and model checking, and dynamic and static analysis. It approaches the problem of building an abstraction of program behavior with decompilation and uses full-system emulation to handle undecidability and address environmental side effects. We discuss the limitations of past vulnerability analysis methods that motivated Hy2's creation and detail its design and implementation. We present an evaluation of Hy2 on several real-world programs to demonstrate its practicality and effectiveness. We uncovered 18 reported and several unreported vulnerabilities in the programs evaluated and describe limitations and potential improvements to Hy2.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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