{"schemaVersion":"nuwa-current-record-1","version":"2026-10-29.1","publication":"published","work":{"id":"traceprobe","title":"TraceProbe: An Execution-Based Benchmark for Diagnosing Safety Failures in LLM Agents","aliases":["traceprobe"],"authors":["Yuxiang He","Jian Zhao","Yufei Liu","Yuchen Yuan","Xuelong Li"],"primaryFunction":"evaluation","secondaryFunctions":[],"kind":"benchmark","sourceId":"dr-20261028-traceprobe","attributionSourceId":"dr-20261028-traceprobe","originalUrl":"https://aclanthology.org/2026.emnlp-main.1233.pdf","publicationDate":null,"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":["traceprobe"],"contributions":[],"summary":{"en":"Execution-based traces reveal intermediate violations that endpoint scoring misses.","zh":"155 场景、12 模型、每设置 3 次运行；分别报告任务成功、终态违规、攻击子集成功和潜在中间违规。"},"note":{"question":null,"method":null,"finding":null,"supports":null,"limits":null,"connections":null},"resources":[],"tags":[],"contentEditionId":"2026-10-28.1"},"reading":{"id":"traceprobe","en":{"takeaway":"Execution-based traces reveal intermediate violations that endpoint scoring misses.","question":"Can execution traces identify safety violations that final outcomes miss?","design":"The benchmark executes 155 sandbox scenarios spanning non-adversarial failures, internal-authority compromise and external attacks. Twelve models are run three times, totaling 5,580 executions. Repeats are averaged within scenario before averaging across eligible scenarios. ASR uses 105 attack-labeled scenarios; the other headline metrics use 155.","findings":["The mean ASR across the 12 headline models was 0.347. Final-outcome safety can coexist with unsafe intermediate actions. Table 2 preserves TSR, SVR, ASR, STCR and latent violation separately; no event numerators are reconstructed from its rounded ratios."],"interpretation":"Task success and safety need joint reporting, with trace evidence adding failure localization. Replaying recorded tool actions diagnoses a recorded trajectory; it is not a counterfactual in which the model makes new decisions.","limits":"The constructed distribution does not estimate natural incident prevalence. Appendix H bootstrap intervals describe scenario composition; the retained passage does not explicitly state their confidence level.","connections":[],"decisionRelevance":"NUWA recommendation: Use the joint metrics and trace locations to compare intervention points within this source. Do not rank these values against unrelated cyber benchmarks."},"zh":{"takeaway":"155 场景、12 模型、每设置 3 次运行；分别报告任务成功、终态违规、攻击子集成功和潜在中间违规。","question":"终态安全是否掩盖中途违规？","design":"155 个可执行沙箱场景分为非对抗失效、内部权限受损和外部攻击。12 个模型各运行三次，共 5,580 次执行。指标先在场景内平均重复，再跨适用场景平均；ASR 只使用 105 个攻击标注场景，其余主指标使用 155 个场景。","findings":["12 个主模型的平均 ASR 为 0.347。终点安全的运行仍可能包含不安全中间动作。表 2 分别保留 TSR、SVR、ASR、STCR 与潜在违规，不从舍入比例反推事件分子。"],"interpretation":"终点成功与安全必须联合报告，轨迹可补充失效位置。工具动作回放能够定位已有轨迹中的触发点，但不是让模型重新决策的因果反事实。","limits":"ASR 仅针对攻击标记子集；回放重执行既定工具调用，不重新运行模型，不能证明反事实因果。","connections":[],"decisionRelevance":"NUWA 建议：新增过程证据层；保留主试验、配对提醒、压力测试和回放各自分母。"},"assertionPolicy":{"findings":"source-finding","interpretation":"nuwa-interpretation","decisionRelevance":"recommendation"},"observations":[{"id":"traceprobe-claude-opus-4-6-tsr","sourceId":"dr-20261028-traceprobe","metric":"TSR","value":0.852,"unit":"fraction","assertionKind":"source-finding","sourceRefs":[{"sourceId":"dr-20261028-traceprobe","url":"https://aclanthology.org/2026.emnlp-main.1233.pdf","artifactSha256":"b0b85491f4be9fb842c2f97891b033a75b44a85c756b0f3b92cf8da775d8f828","textSha256":"e384b1448665f91ea0204f7c519dcc36f5932f05db24104a7346136b208df6a6","locator":"Table2 PDFp7; 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