BrowseSafe++ Markdown - False Positive Rate: leaderboard

BrowseSafe++ v1 uses paired attack/control webpages. Detection receives one page view without the task, outcome, or injection annotations. Markdown inputs are limited to 40k characters. Temperature is 0 when supported; other parameters use provider defaults. Prompted models remain separate from the trained 0.8B BrowseSafeGuard. The study of three text views and the screenshot-vision study are independent. Adaptive attack success uses a fixed native attacker budget and target-model configuration; lower attack success rate (ASR) is better. Task harnesses are not aliased to native base checkpoints. Fable’s answered-only subset is excluded from the full population; the all-request policy row is retained. Metric: False Positive Rate (%).

Metric: False Positive Rate (%). Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 17 models tracked.

Top models

#ModelScoreOverall rank
1BrowseSafe++ detector Claude Opus 5.53.77
2BrowseSafe++ detector Llama 3.1 8B6.45
3BrowseSafe++ detector BrowseSafe-Guard7.4
4BrowseSafe++ detector DeepSeek V3.111.42
5BrowseSafe++ detector Jev 1.1312.21
6BrowseSafe++ detector GPT-OSS 20B14.84
7BrowseSafe++ detector GPT-6 Astra15.1
8BrowseSafe++ detector Qwen 3.7 Max15.43
9BrowseSafe++ detector Gemini 3.1 Pro19.18
10BrowseSafe++ detector Claude Fable 5.1 (all) †20.41

Interactive version: theaggregate.ai/benchmark?slug=browsesafe-plus-plus-markdown-false-positive-rate · How It Works · Data refreshed daily, snapshot 2026-10-28.