The simple economics of an external shock to a bug bounty platform
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
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.000 | 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