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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteDark Reading reports that HackerOne CEO Kara Sprague said the number of critical vulnerabilities sitting in backlogs rose 30-fold over the previous 12 months, even as mean time to remediation improved 50%. Those figures are striking, but the report does not provide the underlying dataset or definitions needed to verify what was counted. A growing backlog and a faster average time to close issues can coexist because they measure different things.
What the report says—and what it does not establish
Dark Reading attributes both figures to HackerOne CEO Kara Sprague: a 30-fold increase in critical vulnerabilities in backlogs over 12 months and a 50% improvement in mean time to remediation. The figures should be treated as reported claims, not independently verified HackerOne-wide measurements. The available report does not give the original statement, the baseline backlog, the sample of programs, the precise dates, or the calculation behind the improvement.
It also does not define “critical vulnerability backlog.” The count could refer to untriaged reports, validated vulnerabilities awaiting a fix, or another category; the report does not say. That distinction matters: a reported finding is not necessarily a confirmed defect, and a confirmed defect is not automatically a demonstrated exploitable risk.
How can the backlog rise while remediation time improves?
A backlog is a stock: the number of items still waiting at a point in time. Mean time to remediation is a measure of how long resolved items took to close. A shorter average resolution time does not by itself show that the queue is shrinking.
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For example, if incoming findings increase faster than teams can validate and fix them, the open queue can grow even while the team closes completed cases more quickly. The measures could also cover different populations or severity categories. These are plausible ways for the two figures to coexist, not established explanations for the reported trend.
| Measure | What it describes | What it cannot establish alone |
|---|---|---|
| Backlog count | Items waiting at a given time, subject to the source’s definition of what counts. | How quickly resolved items were closed, or how many open items are confirmed exploitable vulnerabilities. |
| Mean time to remediation | Average time to remediate items in the measured population. | Whether the total queue grew or shrank, or whether the same programs and severity classes were measured. |
Why discovery volume is not the same as risk reduction
In a March 2026 article, HackerOne Lead Product Researcher Naz Bozdemir writes: “When discovery outpaces validation, security teams do not automatically reduce more risk.” The article describes how findings can accumulate when teams lack the capacity to validate reports, route them to owners, remediate root causes, and verify fixes. That workflow is useful context, but it does not prove why the 30-fold figure rose.
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Organizations need to distinguish stages in the vulnerability pipeline:
- Submitted: a report or finding arrives and awaits review.
- Validated: the team determines whether it represents a real defect and assigns an appropriate severity.
- Assigned and remediated: an owner addresses the underlying issue.
- Verified: the fix is checked before the item is closed.
Counts at each stage answer different questions. A large intake may mean more reports, not necessarily more unique confirmed vulnerabilities; a count of confirmed, unresolved critical vulnerabilities would indicate a different operational concern. The Dark Reading account does not specify which stage its backlog figure represents.
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How other vulnerability figures compare
Other published figures provide context, but they measure different platforms, populations, and outcomes. They should not be used to confirm or directly compare with HackerOne’s reported backlog change.
| Source and scope | Reported figure | Why it is not a direct comparison |
|---|---|---|
| Journal of Cybersecurity study of Bugcrowd data, published 2024 | Paid vulnerabilities numbered 1,021 in 2019 and 1,136 in 2020. | The study concerns Bugcrowd and historical paid vulnerabilities; it is not a measurement of HackerOne’s later critical-vulnerability backlog. |
| HackerOne, 2025, penetration-test findings | 34-day median resolution lifecycle. | This is a median for penetration-test findings, not a mean for the critical backlog referenced by Dark Reading. |
What organizations should measure
To tell whether discovery is reducing risk or merely growing a queue, security teams should track the movement and age of findings, not rely on one headline number. Useful operational checks include:
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- Define whether the backlog includes submitted, untriaged, validated, or remediation-pending findings.
- Report critical findings separately and state the severity criteria used.
- Measure intake, validation, assignment, remediation, and verification as distinct stages.
- Track both average and median resolution times, with the population and time window clearly stated.
- Monitor the age of open critical issues and the rate at which they are resolved, not only total findings closed.
For the reported 30-fold increase to be interpretable, readers would need the baseline and end-date counts, the programs included, the backlog definition, and a clear account of how the 50% mean-time improvement was calculated. Those details are not provided in the Dark Reading report.
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