Analyzing Structural Security Posture to Evaluate System Design Decisions
Bibliographic record
Abstract
Software systems are increasing in complexity, with attendant increases in the number of vulnerabilities they contain. Remediating these vulnerabilities, ideally during the early requirements and design phases, has been highly resource-intensive, and is often omitted due to lack of knowledge, time, and/or funds. We propose an approach, applied in these early phases, to address the following issues: 1) to enhance the developer's security knowledge of the system, we introduce the notion of structural security posture, which uses a collection of metrics to assess a system's security based on its structural view, 2) to guide the identification of vulnerabilities, we leverage external security data sources, and 3) to address the issue of resource intensiveness, we offer a tool for evaluating and analyzing a system's structural security posture. We illustrate how our approach facilitates the evaluation of design decisions to improve security using an example.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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; a candidate call from one teacher head, 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".