{"schemaVersion":"nuwa-current-record-1","version":"2026-10-29.1","publication":"published","work":{"id":"self-assessment","title":"Scaling Misuse Detection with Self-Assessment","aliases":["self assessment"],"authors":[],"primaryFunction":"governance","secondaryFunctions":[],"kind":"study","sourceId":"dr-20261028-self-assessment","attributionSourceId":"dr-20261028-self-assessment","originalUrl":"https://safeguards.anthropic.com/2026/self-assessment/","publicationDate":"2026-10-16","updatedOn":"2026-10-28","areas":["deception","cybersecurity"],"scopeNote":{"en":"Selected original sections; local editorial review. Not an independently replicated experiment.","zh":"选读原始材料；本地编辑审阅稿，不是独立复现实验。"},"protocolIds":[],"contributions":[{"organizationId":"anthropic","role":"publisher","sourceId":"dr-20261028-self-assessment","locator":"Results; limitations; text lines 00008,00014,00060"}],"summary":{"en":"Teacher-relative recall, passive monitoring and adaptive blocking are distinct endpoints.","zh":"相对教师分类器，在 1% 误报率下总体召回 78%、高优先级召回 94%；对抗阻断试验仍有明显失效。"},"note":{"question":null,"method":null,"finding":null,"supports":null,"limits":null,"connections":null},"resources":[],"tags":[],"contentEditionId":"2026-10-28.1"},"reading":{"id":"self-assessment","en":{"takeaway":"Teacher-relative recall, passive monitoring and adaptive blocking are distinct endpoints.","question":"How do teacher-relative monitoring recall and adaptive attack blocking differ?","design":"The source separates production-model offline self-assessment, independently trained anonymous size groups and adaptive blocking on 24 tasks. Offline reference labels come from a teacher classifier. Production classifiers are disabled in the blocking experiment.","findings":["At a 1% false-positive rate, teacher-relative recall was 78% overall and 94% for high-priority cases. Adaptive attack success was 19/24 without safeguards, 13/24 with self-assessment blocking and 6/24 with a prompted classifier; printed rates were 79%, 54% and 25%."],"interpretation":"Teacher-relative recall, traffic coverage and adaptive protection answer different questions. Self-assessment adds observations but retains dependence on label quality and attack distribution. Anonymous XS/S/M/L labels do not disclose parameter counts.","limits":"Teacher blind spots can propagate. Passive monitoring is not blocking, and the anonymous training experiment is not a matched comparison with the production-model result.","connections":[],"decisionRelevance":"NUWA recommendation: Record the labeling reference, operating point, traffic coverage and adaptive attacker separately before using this evidence in a control argument."},"zh":{"takeaway":"相对教师分类器，在 1% 误报率下总体召回 78%、高优先级召回 94%；对抗阻断试验仍有明显失效。","question":"廉价自评能否覆盖大规模滥用检测？","design":"来源分别报告生产模型的离线自评监测、匿名尺寸组的独立训练实验，以及 24 个任务上的自适应阻断实验。离线参考标签来自教师分类器；阻断试验关闭生产分类器，三类设计分别解释。","findings":["1% 误报率下，教师相对召回总体为 78%，高优先级为 94%。自适应攻击成功分别为无防护 19/24、自评阻断 13/24、提示分类器阻断 6/24；原文舍入百分比为 79%、54%、25%。"],"interpretation":"教师相对召回、运行覆盖和自适应防护回答不同问题。自评可以增加观测，却仍依赖标注参考与攻击分布；匿名 XS/S/M/L 也不能当作已披露参数规模。","limits":"同一教师用于训练和评估，存在共同盲点；离线监测不等于在线阻断，也不等于可靠监测自身失准。","connections":[],"decisionRelevance":"NUWA 建议：补充教师独立性、人类复核、实际覆盖与响应成本。"},"assertionPolicy":{"findings":"source-finding","interpretation":"nuwa-interpretation","decisionRelevance":"recommendation"},"observations":[{"id":"no-safeguards","sourceId":"dr-20261028-self-assessment","metric":"Attack success","value":79,"unit":"%","assertionKind":"source-finding","sourceRefs":[{"sourceId":"dr-20261028-self-assessment","url":"https://safeguards.anthropic.com/2026/self-assessment/","artifactSha256":"03b9ece6dae1c006622bb47373fb914bec20cee054a452748f30573c63ff6110","textSha256":"dae7a057951783e9c5d28fa96611c0ffda40e4f16aaeecc69620656d4255c6d2","locator":"Can self-assessment block attacks? 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