How the Review Pipeline Works

Every day the pipeline scans new arXiv submissions in AI security and adjacent areas and selects the papers that look most relevant to this site's focus: LLM safety, RAG security, agent security, and adversarial machine learning.

Reviews are then drafted by an LLM pipeline that I built and direct. It reads each selected paper and produces a structured write-up — threat model, approach, results, and takeaways — following a format and editorial guidelines I defined. Daily digests and weekly trend reports are assembled the same way from the individual reviews. Header images on these posts are AI-generated.

I spot-check output before publication rather than line-editing every post, so mistakes can slip through: a misread table, an overstated claim, an imprecise summary. When I find an error — or someone points one out — I correct the article and note the fix in the commit history. Machine-generated posts carry a disclosure line linking here, and standalone paper reviews are kept out of search indexing so the original papers rank instead.

If something looks wrong, I want to know. Ask the AskAI widget on any article, or open an issue on GitHub. Human-written work — research papers, projects, and tutorials — is labeled by its type and does not go through this pipeline.