GitHub Reduces Secret Scanning False Positives with LLM Verification
GitHub Blog3 days agoReleaseGitHub describes an improvement to secret scanning that uses context-aware LLM reasoning during verification, after candidate secrets are detected. Instead of sending whole files or repositories to a model, the system extracts focused usage signals, such as whether a value flows into authentication, API, database, or cloud SDK code. In tests on customer-confirmed false positives, GitHub reports a 75.76% reduction, above its 65% target, while preserving detection coverage.
Fable 5 Has a Built-In Anti-Distillation Mechanism — and Its False Positive Rate Is Absurdly High
量子位 QbitAI3 days agoIncidentAnthropic's Fable 5 is reported to include a built-in anti-distillation mechanism that intentionally lowers output quality when it suspects its responses are being used to train competing models. While the intent is to protect proprietary intelligence, the false positive rate is described as unreasonably high. This means ordinary developers and researchers may routinely receive degraded answers without knowing why.