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TOPIC · ENTITY #1142

secret scanning

Related event timeline, sources and context from the news index.

EVENT TIMELINE

3

MODELS · 1 SOURCE · GitHub AI & ML

GitHub deploys ModernBERT-based context-aware classifier to reduce secret scanning noise

GitHub replaced an LLM-heavy secret-scanning pipeline with a two-stage system: fast regex candidate generation followed by a fine-tuned ModernBERT classifier that evaluates each candidate plus surrounding context. The new pipeline yields an 86% faster median post-push scan execution time, over 98% lower serving cost versus the previous LLM-based approach, and an 11.5% improvement in user acceptance by reducing false positives.

7.0

COMPANIES · 1 SOURCE · GitHub AI & ML

GitHub adds fine‑tuned classifier to extend push protection for unstructured secrets

GitHub reports that AI agents now appear in roughly one in three pull requests and says secret exposures are doubling yearly; to scale prevention it introduced a fine‑tuned classifier (built with Microsoft Applied Sciences) that evaluates candidate secrets in under two milliseconds to extend push protection to unstructured secrets. GitHub also published operational metrics: a public secret appears about once every two seconds, push protection prevents roughly 30% of newly detected secrets before they enter repo history, screened pushes grew 2.84× while pushes carrying credentials grew 2.59× between Q2 2024 and Q2 2026, and mean manual revocation time remains around 40 days.

7.0

MODELS · 1 SOURCE · GitHub AI & ML

GitHub: How to evaluate LLMs before production

GitHub's blog published a post titled “How to evaluate LLMs before production” that describes lessons the team learned while evaluating large language models for a real-world secret-scanning system. The post is presented as practical guidance for assessing LLM behavior and suitability prior to deployment.

6.0