# RSB-1000: 1,000-record synthetic benchmark for phishing detection (CC-BY-4.0)

> Source: <https://discuss.huggingface.co/t/rsb-1000-1-000-record-synthetic-benchmark-for-phishing-detection-cc-by-4-0/190933#post_1>
> Published: 2026-10-23 04:33:13+00:00

Hi — I run Reveal Wire ([youtube.com/@RevealWire](http://youtube.com/@RevealWire)), a channel that tests AI claims with real experiments. For an episode on phishing detection I looked for published detection scores for open-weight models and found zero — for any model. So we built RSB-1000: 1,000 fully synthetic records (780 scam / 220 legitimate, including 60 AI-written ‘adversary-in-the-middle’ lures with polished language and lookalike domains), frozen October 2026, SHA256-pinned, CC-BY-4.0. Dataset: [RevealWire/rsb-1000 · Datasets at Hugging Face](https://huggingface.co/datasets/RevealWire/rsb-1000) — code and eval script: [GitHub - RevealWire/rsb-1000: RSB-1000: a frozen 1,000-record synthetic benchmark for scam/phishing detection (780 scam / 220 legit, incl. 60 AiTM cases). Data: CC-BY-4.0, code: MIT. · GitHub](https://github.com/RevealWire/rsb-1000). Tested zero-shot: Qwen 3.8-27B — 98.9% recall, 11.6% FPR; GPT-OSS 20B — 94.0% recall, 23.1% FPR. The AI-written lures were the hardest subset for both models. If you run other models against it, I’d genuinely love to see the numbers.