Charlemagne Labs social-engineering benchmark
Muse Spark 1.3 1.60% Only in system cards Attacker models try to persuade a skeptical simulated victim to take an action over many turns. Scored by attempted and carry-through rates.
Used in Meta Muse Spark safety reports. Filtered to 280 scenarios. Attempted and carry-through tracked separately.
- 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 1.60%
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 1.60%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Muse Spark 1.3 | 1.60% | Oct 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.
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 |
|---|---|---|---|---|
| 23 Oct 2026 | Muse Spark 1.3 system card | 1.6 | Social Engineering Propensity, attempted rate, Muse Spark 1.3 (API) (Figure 9). Carry-through rate 0.0 | Cyber risk |
| 9 Jul 2026 | Muse Spark 1.1 system card | 5.1 | Charlemagne Labs multi-turn social engineering composite score 0 to 100, non-refused prompts (Table 1, Figure 12) | Cyber risk |
| 12 Jun 2026 | Muse Spark system card | 57.3 | Charlemagne Labs multi-turn social engineering composite capability score 0 to 100 (Table 1, Figure 20). Deployed-system refusal rate 99.9 (Table 2) | 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% - 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%