BrowseSafe++ Screenshot - 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. The guard combines a vision encoder and scalar head over a thumbnail and 1536px tiles, using the maximum logit. Its threshold is fixed using training calibration. Metric: False Positive Rate (%).

Metric: False Positive Rate (%). Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 5 models tracked.

Top models

#ModelScoreOverall rank
1BrowseSafe++ screenshot detector BrowseSafe-Guard6.08
2BrowseSafe++ screenshot detector GPT-5.4 nano6.72
3BrowseSafe++ screenshot detector Qwen3-VL 30B12.47
4BrowseSafe++ screenshot detector Flash-Lite28.08
5BrowseSafe++ screenshot detector Haiku 4.533.83

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