The simple economics of an external shock to a bug bounty platform
Bibliographic record
Abstract
Abstract We first provide background on the “nuts and bolts” of a bug bounty platform: a two-sided marketplace that connects firms and individual security researchers (“ethical” hackers) to facilitate the discovery of software vulnerabilities. Researchers get acknowledged for valid submissions, but only the first submission of a distinct vulnerability is rewarded money in this tournament-like setting. We then empirically examine the effect of an exogenous external shock (COVID-19) on Bugcrowd, one of the leading platforms. The shock presumably reduced the opportunity set for many security researchers who might have lost their jobs or been placed on a leave of absence. We show that the exogenous shock led to a huge rightward shift in the supply curve and increased the number of submissions and new researchers on the platform. During the COVID period, there was a significant growth in duplicate (already known) valid submissions, leading to a lower probability of winning a monetary reward. The supply increase resulted in a significant decline in the equilibrium price of valid submissions, mostly due to this duplicate submission supply-side effect. The results suggest that had there been a larger increase in the number of firms and bug bounty programs on the platform, many more unique software vulnerabilities could have been discovered.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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".