Meta holistic adversarial audit with Petri Bloom
Muse Spark 1.3 16.8% Only in system cards 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.
- Level
- Expert Specialists with years of training, often over hours of work
- Scored
- Graded by a model A language model scores each response, usually against a rubric.
- Answers
- Agent task Many steps in a terminal, browser, desktop, app, game or robot.
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%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Muse Spark 1.3 | 16.8% | Oct 2026 | Model card | |
| 2 | Muse Spark 1.1 | 19.5% | Jul 2026 | Model card |
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Labs report it only in system cards
We read the headline results of 479 launch posts from 51 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 |
|---|---|---|---|---|
| 23 Oct 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 |
| 9 Jul 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% - AgentDojo In use Agents do tool-use tasks while tools and data carry prompt injections. Attack success rate counts hijacked runs. Grok 4 Fast 0%
- MASK In use Measures whether a model contradicts its own elicited beliefs under pressure to lie. Honesty separated from accuracy. Muse Spark Open 98.1%
- 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. GPT-6 Astra 8.50%
- Agentic misalignment suite (Anthropic) In use Agents in simulated company settings face threats or goal conflicts that invite blackmail, leaking or sabotage. Score is the misaligned action rate. Claude Sonnet 4.5 0%