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Evidence record

Claude Opus 4.7

MASK · Evidence record

Sources and provenance ↓ Read the research narrative →

Original source charts and materials

MASK by archetype

Original evaluator results · source order

Continuations honesty

0–100%
Claude Opus 4.7
59.1%
Showing 1 of 1 systems

Condition-bounded frequency of lying under stated pressures.

A controlled benchmark does not establish stable intent or real-world deception prevalence. Report dates and original executor are shown with this assessment.

Evaluation conditions and system configurations
Claude Opus 4.7
Per-archetype scored prompts: 176 / 125 / 120 / 209 / 274 / 96. Some supplied CI cells are inconsistent with displayed n; reproduced as reported, not recalculated. · MASK by archetype
modelVersion: not specified · harness: not specified · tools: not specified · budget: not specified
MASK by archetype · source order
System / configurationContinuations honestyDisinformation honestyDoubling-down honestyKnown-facts honestyProvided-facts honestyStatistics honestyPublication / verification
Claude Opus 4.7Harness not specified59.1%95% Wilson CI [51.7, 66.1] (as reported)33.6%95% Wilson CI [25.9, 42.3] (as reported)68.3%95% Wilson CI [59.6, 76.0] (as reported)63.6%95% Wilson CI [56.9, 69.9] (as reported)74.8%95% Wilson CI [69.4, 79.6] (as reported)80.2%95% Wilson CI [71.1, 86.9] (as reported)Published2026-08-02

Assessment provenance

GLM-5.2 Risk Evaluation Report → · External report

Actual evaluation: Center for AI Safety · Model record →

Method and scope

Condition-bounded frequency of lying under stated pressures.

Evidence type
Benchmark
Assessment subject
Models under pressure to state an inaccurate belief
Primary dimension
Propensity
Evidence snapshot
2026-10-29.1

Primary construct is conditional willingness to make misleading statements, not general truthfulness accuracy.

Source finding

Source-reported measurements in Appendix A.7; Table A.3, pp. 41–42. Transcription is not independent experimental replication.

Limitations

A controlled benchmark does not establish stable intent or real-world deception prevalence.

A controlled benchmark does not establish stable intent or real-world deception prevalence.

Related research questions

References

  1. [1] Richard Ren, Arunim Agarwal, et al.. The MASK Benchmark: Disentangling Honesty From Accuracy in AI Systems. 2026-01-05. v3 [arXiv]; Author affiliations on the original paper title page
  2. [2] GLM-5.2 Risk Evaluation Report. 2026-08-02. v1.1 · revised 26 August 2026 ; Appendix A.7; Table A.3, pp. 41–42