Emerging Issues in Responsible Vulnerability Disclosure.
Bibliographic record
Abstract
Security vulnerability in software is the primary reason for security breaches, and an important challenge for IT professionals is how to manage the disclosure of vulnerability information. The IT security community has proposed several disclosure policies, such as full vendor, immediate public and hybrid, and has debated which of these should be adopted by coordinating agencies such as CERT. Our early study (Cavusoglu et al. 2004a) analyzed the optimal disclosure policy that minimizes social loss when vulnerability affects only one software vendor. In this paper, we extend our early work into three directions in order to sled light on current issues in vulnerability disclosure process. (i) When the vulnerability affects multiple vendors, we show that the coordinator's optimal policy cannot ensure that every vendor will release a patch. However, when the optimal policy does elicit a patch from each vendor, we show that the coordinator's grace period in the multiple vendor case falls between the grace periods that it would set individually for the vendors in the single vendor case. (ii) We analyze the impact of an early discovery, which can be encouraged with proper incentive mechanisms, on the release time of the patch, the grace period, and the social welfare. (iii) We also investigate the impact of an early warning system that provides privileged vulnerability information to selected users before the release of a patch for the vulnerability on the social welfare. Finally, we explore the several policy implications of our results and their relationship with current disclosure practices.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".