Helpfulness violations (from ART)
Gemini 2.5 Pro Jun 2025 6.10% Automated red teaming attackers search for benign prompts where the model gives unhelpful answers. Reports the violation rate found.
In-house Google DeepMind automated red teaming. Attackers and judges are prompted or finetuned Gemini models.
- Made by
- Google DeepMind
- Kind
- Refusing harmful requests
- Level
- Everyday
- Scored
- Graded by a model
- Answers
- Open-ended
- Tasks
- Kept private
- Strongest model tested
- Gemini 2.5 Pro Jun 2025 #96 on the capability index
- Last in a system card
- July 2025
- Last model added
- July 2025
Gemini 2.5 Pro leads with 6.10%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
- 1 Gemini 2.5 Pro Jun 2025 6.10%
- 2 Gemini 2.5 Flash Jun 2025 6.60%
Source: Labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Gemini 2.5 Pro Jun 2025 | 6.10% | Jul 2025 | Lab model card | |
| 2 | Gemini 2.5 Flash Jun 2025 | 6.60% | Jul 2025 | Lab model card |
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Labs’ model cards
Labs report it only in system cards
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark. Some system cards do, below.
2 system cards report it, from 1 lab
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Jul 7, 2025 | Gemini 2.5 Pro technical report | 6.1 | Helpfulness violations (from ART), lower is better (Table 8) | Safety |
| Jul 7, 2025 | Gemini 2.5 Flash technical report | 6.6 | Helpfulness violations (from ART), lower is better (Table 8) | Safety |
More in Safety and alignment
- FORTRESS In use Paired adversarial and benign national-security misuse prompts. Rubric-graded risk score on adversarial and over-refusal score on benign prompts. Claude Opus 5.5 5.78%
- StrongREJECT In use Forbidden-prompt jailbreak benchmark with a rubric grader that scores how useful a non-refusal actually is.
Inkling 98.6% - Gray Swan IPI Arena benchmark In use Indirect prompt-injection attacks from public red-teaming competitions replayed on tool, coding and browser agents. Attack success rate at k attempts. Gemini 4 Carbon 0%
- DistressBench In use Models reply as a chatbot to 718 simulated users in psychological crisis, in single-turn and multi-turn chats. Three judge models score replies against clinician-written rubrics.
Muse Spark 1.2 88.4% - Lakera B3 (Backbone Breaker Benchmark) In use Crowdsourced prompt-injection attacks hit backbone LLMs inside agent threat snapshots at three defense levels. The score is attack resistance, 100 minus the board's vulnerability percentage. Claude Opus 4.5 89%
- IHEval In use Models face conflicts between system, user and tool instructions. Reports a composite score for following the higher-priority instruction. GPT-4o 70%