Unverified50% confidenceBenchmarkExact time
Compared with the old LLM pipeline, GitHub's new secret scanning approach improves offline Precision by 13% and Recall by 19%; online A/B experiments show that median post-push scan execution time drops by 86%, model serving cost drops by more than 98%, and the repository-owner-weighted alert acceptance metric improves by 11.5%.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/19/2026
First Seen
Valid until: 1/17/2027
Sources
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Related Claims
Unverifiedgpt-oss-120b spent about 25 times the cost per correct answer on the distractor counting task, while its accuracy was 7.5 percentage points lower (not significant).65% similarUnverifiedInternal research at GitHub shows that raising the task completion rate from 60% to 80% can reduce total costs by more than 40%.64% similarUnverifiedAfter GitHub Copilot removed line numbers, inference costs dropped by approximately 5% in offline benchmarks, and an online experiment targeting CLI users reduced average daily model inference costs by approximately 3%, with no regression detected in quality or edit failure rates.63% similarUnverifiedIn task B, after reweighting, about 17% of checks would be routed to the managed fallback due to verification failure, actually increasing the load that was originally intended to be reduced.62% similarUnverifiedDuring training, the held-out FID dropped from 33.7 to 27.0, FD-DINOv2 dropped from 570 to 218, and detector-based object accuracy improved from 65% to 90%.62% similar
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