ReaLM - PA-Attack: leaderboard

ReaLM (2026) is a red-teaming benchmark for physical-world vision-language models on 832 driving, manipulation, grasping, physics, egocentric, diagnostic and scene cases, attacked black-box toward scenario-specific failure targets. This board is the attack success rate under PA-Attack, an untargeted out-of-distribution prototype attack: the percent of attacked cases answered incorrectly; lower is better.

Metric: Attack success rate (%; share of adversarial cases where the model gives an incorrect, task-relevant answer) under PA-Attack (untargeted out-of-distribution prototype attack, L-inf 8/255) on the 832 ReaLM physical-world cases (driving, manipulation, grasping, physics, egocentric, diagnostics and scene QA; image, question and ground-truth answer), black-box one-shot evaluation at temperature 0, a Qwen3-8B judge extracting the answer; lower is better. Source: arxiv.org. Saturation forecast: Around October 2027. 13 models tracked.

Top models

#ModelScoreOverall rank
1Qwen 3.5 27B38.1#359
2Qwen 3.5 122B A10B39.4#331
3GPT-4.1 Mini40.6#779
4Qwen 3.6 Flash41.7#424
5Gemini 3 Flash41.9#164
6Qwen 3.5 9B42.8#803
7Kimi K2.543#256
8Seed 2.0 Mini45.4#508

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=realm-pa-attack · How It Works · Data refreshed daily, snapshot 2026-11-13.