CyberBench (Liu et al.)
Muse Wonder Full 0.80% leads since October 2026 Models face detail-seeking high-severity cyber requests. Meta uses it as a seed set for adaptive red-teaming and reports attack success rate.
AAAI-24 AICS workshop benchmark. Not the same as Cybench. The original is a 10-dataset cyber NLP suite (NER, summarization, multiple choice, classification). Meta describes and uses it as a source of harmful cyber requests.
- Released
- February 2024
- Kind
- Refusing harmful requests
- Level
- Expert
- Scored
- Graded by a model
- Answers
- Open-ended
- Tasks
- All public
- Strongest model tested
- Muse Spark 1.3 #26 on the capability index
- Last in a system card
- October 2026
- Last model added
- October 2026
Muse Wonder Full leads with 0.80%
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 Muse Wonder Full 0.80%
- 2 Muse Spark 1.3 6.30%
- 3 Muse Spark 1.1 9.00%
Source: Labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Muse Wonder Full | 0.80% | Oct 2026 | Lab model card | |
| 2 | Muse Spark 1.3 | 6.30% | Oct 2026 | Lab model card | |
| 3 | Muse Spark 1.1 | 9.00% | Jul 2026 | Lab model card |
2 models have held number one in labs’ own numbers since its release in February 2024
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Labs’ model cards
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.
3 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 |
|---|---|---|---|---|
| Oct 23, 2026 | Muse Spark 1.3 system card | 6.3 | CyberBench multi-turn ASR, Muse Spark 1.3 (API) (Figure 14). Single-turn 2.8 | Cyber risk |
| Oct 12, 2026 | Muse Wonder Full model card | 0.8 | CyberBench multi-turn ASR, lower is better (Cyber Robustness Evaluations table, section 4). Single turn 0.8 | Cyber risk |
| Jul 9, 2026 | Muse Spark 1.1 system card | 9 | CyberBench multi-turn ASR, Muse Spark 1.1 model-only (Figure 19 text). Single-turn 3.0. System 0.8 and 4.6 | Cyber risk |
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%