We implemented the Rapid7 vulnerability scanner last month, but our false positive rate on web application endpoints is too high. What are the best tuning practices to improve accuracy?
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The searches the AI actually ran for this prompt.
SEED QUERY
We implemented the Rapid7 vulnerability scanner last month, but our false positive rate on web application endpoints is too high. What are the best tuning practices to improve accuracy?
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
Classifying 2 queries…
Searches and sources
ChatGPT2 real searches · gpt-6
Rapid7 InsightVM vulnerability scanning web application false positives tuning authenticated scans scan templates vulnerability exceptions documentation Google ↗
Rapid7 AppSpider false positives web application scan tuning authentication exclusions verification documentation Google ↗
- Selecting vulnerability checks · docs.rapid7.com
- Scan Template Best Practices · docs.rapid7.com
- Investigate false positives · docs.rapid7.com
- Accepting Risk with Vulnerability Exceptions · docs.rapid7.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Rapid7 InsightVM vulnerability scanning web application false positives tuning authenticated scans scan templates vulnerability exceptions documentation OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
Rapid7 AppSpider false positives web application scan tuning authentication exclusions verification documentation OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |