Meta holistic adversarial audit with Petri Bloom
Muse Spark 1.3 16.8% Two auditor models run Petri Bloom scenarios built from Meta's behavior taxonomy. A binary judge reports the share of scenarios with a violation.
Meta setup over nine taxonomy sub-categories. Each scenario takes its worst of six transcripts.
- Made by
- Meta
- Released
- July 2026
- Kind
- Deception and alignment
- Level
- Expert
- Scored
- Graded by a model
- Answers
- Agent task
- Tasks
- Kept private
- 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 Spark 1.3 leads with 16.8%
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 Spark 1.3 16.8%
- 2 Muse Spark 1.1 19.5%
Source: Labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Muse Spark 1.3 | 16.8% | Oct 2026 | Lab model card | |
| 2 | Muse Spark 1.1 | 19.5% | Jul 2026 | 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 |
|---|---|---|---|---|
| Oct 23, 2026 | Muse Spark 1.3 system card | 16.8 | Broad Adversarial Auditing with Petri Bloom, overall violation rate (binary judge), nine taxonomy sub-categories, Muse Spark 1.3 (API) (Figure 42) | Alignment |
| Jul 9, 2026 | Muse Spark 1.1 system card | 19.5 | Holistic evaluation with Petri Bloom, overall violation rate across nine principle categories (4.3 text, Table 17) | Alignment |
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%