What it measures

Evaluates visual jailbreaks where harmful intent is split across benign text and typography/imagery.

Reading the measurement

Separate satisfying instructions from factual accuracy, human preference and safe behavior. Inspect strict all-constraint success as well as average criterion credit. Judge choice and adversarial prompt selection can materially change the result.

Published evaluation

Published average attack-success rate over thirteen scenario categories; lower is better. The named input condition and model size remain separate, and these values are not VLSBench safety accuracy.

Where this fits in an evaluation suite

Format compliance, preference, truthfulness, and safety require separate measurements. Keep desirable writing style from masking factual errors.

Start with a capability hypothesis, then choose a second test with a different failure mode. Keep the checkpoint, reasoning effort, agent tools, sampling policy, and scoring procedure fixed when measuring a training intervention. Record failures and missing outputs in the denominator.

A reproducible comparison

Published average attack-success rate over thirteen scenario categories; lower is better. The named input condition and model size remain separate, and these values are not VLSBench safety accuracy.

Publish the exact run configuration, item coverage, score units, and available uncertainty. If a source supplies only an aggregate or a single run, preserve that limit. A new model name or a rounded score alone does not establish a capability improvement.

PRIMARY EVIDENCE & FURTHER READING

  1. MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models — published result table ↗

    Liu, Xin, Zhu, Yichen, Gu, Jindong, Lan, Yunshi, Yang, Chao, Qiao, Yu · 2023

Reviewed 2026-10-27. Explanation and interpretation by RLTasks. Benchmark ownership remains with its original authors.