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NUWA INDEX

NUWA Frontier AI Safety Index

A research knowledge base on frontier AI risks, maintained by NUWA Lab.

NUWA Framework

How frontier AI risk emerges

Behavior formation, agentic mechanisms and dual-use applications in one research model.

NUWA frontier risk frameworkPropensity and capability inform observed behavior under external conditions. Autonomy and deception connect across four dual-use risk domains. Enabling capabilities and multi-agent dynamics extend these relationships. Select a node for its definition and evidence.BEHAVIOR FORMATIONEnvironment · Instructions · HarnessPermissions · SafeguardsPropensityCapabilityBehaviorMulti-agent dynamicsCoordination · Collusion · Collective failureAGENTIC MECHANISMSAutonomyDeceptionPersistence and proliferationConcealment and oversight evasionDUAL-USE RISK DOMAINSCybersecurityCBRNDisinformationAutonomousphysical harmENABLING CAPABILITIESCodingAI for ScienceContent generationRobotics

RESEARCH SCOPE

Autonomy

Persistence and proliferation may change the reach and duration of actions across risk domains.

Explore this research →

NUWA’s research model organizes relationships to be tested. Propensity is measured under stated conditions; domain-specific evidence determines which pathways are supported.

Risk themes

Autonomy

When can an agent pursue consequential actions beyond intended direction or resist intervention?

Sustained task execution, replication, and resistance to interruption; capability alone is not unauthorized action.

Deception

When does a system conceal, misrepresent, or strategically alter behavior toward users or overseers?

Scheming, evaluation gaming, and misleading communication are distinct questions.

Cybersecurity

How might AI systems perform or enable cyber operations, and how can agents be compromised?

Offensive task capability and agent compromise are tracked separately; authorized security research is not observed criminal use.

CBRN

What assistance could AI provide across hazardous scientific knowledge and consequential task steps?

Track knowledge probes separately from practical task uplift; publish only bounded, non-operational summaries.

Disinformation

When can AI-generated content or interaction influence beliefs at scale or support deceptive campaigns?

Persuasion, synthetic content, and false claims have different measurement requirements.

Autonomous physical harm

What risks arise when AI systems perceive, decide, and act in physical environments?

Distinguish hazard recognition from real embodied action and control.

Observation dimensions

Propensity

A tendency to select a behavior under stated conditions, inferred from repeated or varied tests—not from a single act.

Capability

What a system can accomplish with specified tools, harness, access, and budget; ability does not imply autonomous intent.

Behavior

What a system actually said or did in a documented run, including attempts, failures, and refusals.

How NUWA assembles the evidence

NUWA Index connects published research, model disclosures and evaluation results. NUWA maintains the catalogue, reconciles measurement conditions and develops research interpretations; experiments remain attributed to their original evaluators.

Sources

The catalogue includes original papers, system cards, model cards, third-party assessments, official benchmark data, technical articles and incident investigations. An identifiable source, a clear research question and an account of the method determine inclusion. Scores and open code are useful resources, rather than admission requirements.

Each record distinguishes the model developer, report publisher and actual evaluator. A report that quotes another team’s experiment retains that team as the experimental source. Research is organized as risk discovery, risk evaluation and risk governance; tags describe more specific methods and phenomena.

Collection

Source-specific extraction records the reported value, unit, qualifier, sample, task scope, model endpoint, harness, budget and source location. Independent review checks these fields against the captured document. Source versions and review records bind the extraction to the material actually inspected.

Model release dates and disclosed parameter counts are maintained in a separate registry using developer sources. Experiment dates, report publication dates and NUWA collection dates remain separate. Missing fields remain identifiable throughout charts, tables and exports.

Synthesis

Results are linked through benchmark identity, protocol version, metric and system configuration. HTML, PDF and identifier links to the same publication share one bibliography entry. Reused measurements retain every source location while counting as one experiment only when their identity has been established. Independent repetitions remain separate observations.

Cross-report views retain task subsets, scoring denominators, attempt rules and budget differences. Source-reported values are stored separately from NUWA interpretation and documented arithmetic conversions.

Comparison

A shared benchmark name does not establish a common protocol. Reviewed matching conditions support direct comparison; known configuration differences support descriptive views. Values with incomplete conditions remain visible as supplementary observations. Distinct task scopes appear in separate panels, and stable longitudinal series alone receive connecting lines.

Trend charts use independently sourced model release dates. Retrospective views incorporate evidence available today; historical-cutoff views include only reports public at that time. Marker shape identifies comparison conditions, and developer color identifies the model organization. Uncertainty intervals, thresholds and zero values retain their original meaning.

Indicator selection

Monitoring indicators are selected for risk relevance, measurement validity, coverage, continuity and protocol transparency. Capability, propensity and observed behavior remain distinct measurements. Benchmark pages document the source protocols and conditions used in each result.

Limitations

Published evidence reflects selective disclosure and uneven model coverage. Task leakage, saturation, scoring changes and inaccessible configurations can affect interpretation. A missing result records a coverage gap; an observed zero is a measured outcome under a specified test. Knowledge tests, task completion and deployment incidents answer different questions.

Benchmark scores describe performance under their tested conditions. NUWA does not add heterogeneous scores into a single safety grade or infer real-world event probabilities from them. Research interpretations identify the evidence they use and are revised when underlying records change.

Acknowledgements

We acknowledge the researchers and teams whose publications, disclosures and evaluation resources make this knowledge base possible. The organizations below are credited through the works and assessments recorded in NUWA Index.

Versions

Current evidence snapshot: 2026-10-29.1 · Base catalogue search cutoff: 2026-10-25

Latest incremental reading and publication: 2026-10-29 · Incremental source discovery does not establish exhaustive coverage through this date.

The current release establishes cross-report measurement links and records protocol corrections. Historical additions are classified as coverage updates. Corrections remain attached to the affected result records and preserved snapshots.

  • Kimi K3: 100 refers to sampled tasks / the agent iteration limit, not 100 independent attempts. Attempt count is not established.
  • Kimi K3: 100 refers to sampled tasks / the agent iteration limit, not 100 independent attempts. Attempt count is not established.

Archived source series

Evidence cutoff: 2026-10-26

Autonomy

Long-horizon capability and the conditions for human control.

Research scope ↗

PUBLISHED EVIDENCE

METR Time Horizons · 50% task-completion horizon

Published measurements across source reports. Task scopes are annotated; configuration differences remain attached to each observation.

26 observations · 21 dated

● Matched◇ Configuration differs□ Some conditions undisclosed
50% task-completion horizon (min) · log scale Model public release date 1 10 100 1,000 10,000 2023-07 2024-07 2025-07 2024-01 2025-01 2026-01 16 hours · measurement limit

5 observations lack a verified model release date. They remain in Data and Systems.

Select a point or table row to inspect its conditions and provenance.

26 observations

Measurements and source conditions
System / modelResultCondition statusTask / conditionsEvaluator / reportDates
Claude Mythos Preview (early) · METR scaffoldClaude Mythos Preview (early)1,044.78 min50% task-completion horizonSource 95% CI [508.876789, 3304.261235] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,metr_agents/react; ai_rd_fix_embedding/init_solver,use_tools,metr_agents/react; re_bench_common/init_solver,use_tools,metr_agents/react; metr_tasks_swaa/init_solver,generate; metr_agents/reactMETRMETR Time Horizon 1.1 — source dataRelease —Report —
Claude Opus 4.6 · METR scaffoldClaude Opus 4.6718.81 min50% task-completion horizonSource 95% CI [316.685725, 3633.786163] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,metr_agents/react; ai_rd_fix_embedding/init_solver,use_tools,metr_agents/react; re_bench_common/init_solver,use_tools,metr_agents/react; metr_tasks_swaa/init_solver,generate; metr_agents/reactMETRMETR Time Horizon 1.1 — source dataRelease 2026-02-05Report —
Gemini 3.1 Pro · METR scaffoldGemini 3.1 Pro384.15 min50% task-completion horizonSource 95% CI [233.50073, 694.750898] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,triframe_inspect/triframe_agent; ai_rd_fix_embedding/init_solver,use_tools,triframe_inspect/triframe_agent; re_bench_common/init_solver,use_tools,triframe_inspect/triframe_agent; metr_tasks_swaa/init_solver,generate; triframe_inspect/triframe_agentMETRMETR Time Horizon 1.1 — source dataRelease 2026-02-19Report —
GPT-5.2 · METR scaffoldGPT-5.2352.25 min50% task-completion horizonSource 95% CI [198.067494, 815.177445] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,triframe_inspect/triframe_agent; ai_rd_fix_embedding/init_solver,use_tools,triframe_inspect/triframe_agent; re_bench_common/init_solver,use_tools,triframe_inspect/triframe_agent; metr_tasks_swaa/init_solver,generate; triframe_inspect/triframe_agentMETRMETR Time Horizon 1.1 — source dataRelease 2025-12-11Report —
GPT-5.3-Codex · METR scaffoldGPT-5.3-Codex349.53 min50% task-completion horizonSource 95% CI [194.913134, 816.351994] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,triframe_inspect/triframe_agent; ai_rd_fix_embedding/init_solver,use_tools,triframe_inspect/triframe_agent; re_bench_common/init_solver,use_tools,triframe_inspect/triframe_agent; metr_tasks_swaa/init_solver,generate; triframe_inspect/triframe_agentMETRMETR Time Horizon 1.1 — source dataRelease 2026-02-05Report —
GPT-5.4 · METR scaffoldGPT-5.4341.74 min50% task-completion horizonSource 95% CI [186.581591, 768.779526] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,metr_agents/react; ai_rd_fix_embedding/init_solver,use_tools,metr_agents/react; re_bench_common/init_solver,use_tools,metr_agents/react; metr_tasks_swaa/init_solver,generate; metr_agents/reactMETRMETR Time Horizon 1.1 — source dataRelease 2026-03-05Report —
Claude Opus 4.5 · METR scaffoldClaude Opus 4.5292.99 min50% task-completion horizonSource 95% CI [161.717714, 623.704698] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,metr_agents/react; ai_rd_fix_embedding/init_solver,use_tools,metr_agents/react; re_bench_common/init_solver,use_tools,metr_agents/react; metr_tasks_swaa/init_solver,generate; metr_agents/reactMETRMETR Time Horizon 1.1 — source dataRelease 2025-11-24Report —
Gemini 3 Pro · METR scaffoldGemini 3 Pro224.33 min50% task-completion horizonSource 95% CI [139.565157, 379.235434] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,metr_agents/react; ai_rd_fix_embedding/init_solver,use_tools,metr_agents/react; re_bench_common/init_solver,use_tools,metr_agents/react; metr_tasks_swaa/init_solver,generate; metr_agents/reactMETRMETR Time Horizon 1.1 — source dataRelease 2025-11-18Report —
GPT-5.1 Codex Max · METR scaffoldGPT-5.1 Codex Max223.71 min50% task-completion horizonSource 95% CI [134.31077, 396.206723] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,triframe_inspect/triframe_agent; ai_rd_fix_embedding/init_solver,use_tools,triframe_inspect/triframe_agent; re_bench_common/init_solver,use_tools,triframe_inspect/triframe_agent; metr_tasks_swaa/init_solver,generate; triframe_inspect/triframe_agentMETRMETR Time Horizon 1.1 — source dataRelease 2025-11-19Report —
GPT-5 · METR scaffoldGPT-5203.01 min50% task-completion horizonSource 95% CI [112.641357, 405.551565] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,triframe_inspect/triframe_agent; ai_rd_fix_embedding/init_solver,use_tools,triframe_inspect/triframe_agent; re_bench_common/init_solver,use_tools,triframe_inspect/triframe_agent; metr_tasks_swaa/init_solver,generate; triframe_inspect/triframe_agentMETRMETR Time Horizon 1.1 — source dataRelease 2025-08-07Report —
o3 · METR scaffoldo3119.73 min50% task-completion horizonSource 95% CI [74.615398, 190.943818] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,triframe_inspect/triframe_agent; ai_rd_fix_embedding/init_solver,use_tools,triframe_inspect/triframe_agent; re_bench_common/init_solver,use_tools,triframe_inspect/triframe_agent; metr_tasks_swaa/init_solver,generate; triframe_inspect/triframe_agentMETRMETR Time Horizon 1.1 — source dataRelease 2025-04-16Report —
Claude Opus 4.1 · METR scaffoldClaude Opus 4.1100.47 min50% task-completion horizonSource 95% CI [59.272391, 159.451918] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,metr_agents/react; ai_rd_fix_embedding/init_solver,use_tools,metr_agents/react; re_bench_common/init_solver,use_tools,metr_agents/react; metr_tasks_swaa/init_solver,generate; metr_agents/reactMETRMETR Time Horizon 1.1 — source dataRelease 2025-08-05Report —
Claude Opus 4 · METR scaffoldClaude Opus 4100.37 min50% task-completion horizonSource 95% CI [59.994073, 163.454074] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,metr_agents/react; ai_rd_fix_embedding/init_solver,use_tools,metr_agents/react; re_bench_common/init_solver,use_tools,metr_agents/react; metr_tasks_swaa/init_solver,generate; metr_agents/reactMETRMETR Time Horizon 1.1 — source dataRelease 2025-05-22Report —
Claude 3.7 Sonnet · METR scaffoldClaude 3.7 Sonnet60.39 min50% task-completion horizonSource 95% CI [33.006168, 104.226017] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,metr_agents/react; ai_rd_fix_embedding/init_solver,use_tools,metr_agents/react; re_bench_common/init_solver,use_tools,metr_agents/react; metr_tasks_swaa/init_solver,generate; metr_agents/reactMETRMETR Time Horizon 1.1 — source dataRelease 2025-02-24Report —
o1 · METR scaffoldo138.83 min50% task-completion horizonSource 95% CI [21.164512, 64.95249] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,triframe_inspect/triframe_agent; ai_rd_fix_embedding/init_solver,use_tools,triframe_inspect/triframe_agent; re_bench_common/init_solver,use_tools,triframe_inspect/triframe_agent; metr_tasks_swaa/init_solver,generate; triframe_inspect/triframe_agentMETRMETR Time Horizon 1.1 — source dataRelease 2024-12-05Report —
Claude 3.5 Sonnet (October 2024) · METR scaffoldClaude 3.5 Sonnet (October 2024)20.52 min50% task-completion horizonSource 95% CI [10.144026, 40.82028] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,metr_agents/react; ai_rd_fix_embedding/init_solver,use_tools,metr_agents/react; re_bench_common/init_solver,use_tools,metr_agents/react; metr_tasks_swaa/init_solver,generate; metr_agents/reactMETRMETR Time Horizon 1.1 — source dataRelease 2024-10-22Report —
o1-preview · METR scaffoldo1-preview20.33 min50% task-completion horizonSource 95% CI [11.716193, 33.379877] minIncompleteMETR-Horizon-v1.1duetMETRMETR Time Horizon 1.1 — source dataRelease 2024-09-12Report —
Claude 3.5 Sonnet (June 2024) · METR scaffoldClaude 3.5 Sonnet (June 2024)11.4 min50% task-completion horizonSource 95% CI [5.489734, 22.384214] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,metr_agents/react; ai_rd_fix_embedding/init_solver,use_tools,metr_agents/react; re_bench_common/init_solver,use_tools,metr_agents/react; metr_tasks_swaa/init_solver,generate; metr_agents/reactMETRMETR Time Horizon 1.1 — source dataRelease 2024-06-20Report —
GPT-4o · METR scaffoldGPT-4o6.99 min50% task-completion horizonSource 95% CI [4.001482, 12.905741] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,metr_agents/react; ai_rd_fix_embedding/init_solver,use_tools,metr_agents/react; re_bench_common/init_solver,use_tools,metr_agents/react; metr_tasks_swaa/init_solver,generate; metr_agents/reactMETRMETR Time Horizon 1.1 — source dataRelease 2024-05-13Report —
GPT-4 November 2023 · METR scaffoldGPT-4 November 20234.04 min50% task-completion horizonSource 95% CI [1.866859, 8.443226] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,metr_agents/react; ai_rd_fix_embedding/init_solver,use_tools,metr_agents/react; re_bench_common/init_solver,use_tools,metr_agents/react; metr_tasks_swaa/init_solver,generate; metr_agents/reactMETRMETR Time Horizon 1.1 — source dataRelease 2023-11-06Report —
GPT-4 · METR scaffoldGPT-43.99 min50% task-completion horizonSource 95% CI [1.93292, 7.995283] minIncompleteMETR-Horizon-v1.1modular-publicMETRMETR Time Horizon 1.1 — source dataRelease 2023-03-14Report —
Claude 3 Opus · METR scaffoldClaude 3 Opus3.95 min50% task-completion horizonSource 95% CI [1.706313, 8.76484] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,metr_agents/react; ai_rd_fix_embedding/init_solver,use_tools,metr_agents/react; re_bench_common/init_solver,use_tools,metr_agents/react; metr_tasks_swaa/init_solver,generate; metr_agents/reactMETRMETR Time Horizon 1.1 — source dataRelease 2024-03-04Report —
GPT-4 Turbo · METR scaffoldGPT-4 Turbo3.73 min50% task-completion horizonSource 95% CI [1.980046, 6.736613] minIncompleteMETR-Horizon-v1.1mtb/start_metr_task,metr_agents/react; ai_rd_fix_embedding/init_solver,use_tools,metr_agents/react; re_bench_common/init_solver,use_tools,metr_agents/react; metr_tasks_swaa/init_solver,generate; metr_agents/reactMETRMETR Time Horizon 1.1 — source dataRelease —Report —
GPT-3.5 Turbo Instruct · METR scaffoldGPT-3.5 Turbo Instruct0.6 min50% task-completion horizonSource 95% CI [0.255148, 1.115905] minIncompleteMETR-Horizon-v1.1modular-publicMETRMETR Time Horizon 1.1 — source dataRelease —Report —
GPT-3 davinci-002 · METR scaffoldGPT-3 davinci-0020.14 min50% task-completion horizonSource 95% CI [0.093319, 0.221209] minIncompleteMETR-Horizon-v1.1modular-publicMETRMETR Time Horizon 1.1 — source dataRelease —Report —
GPT-2 · METR scaffoldGPT-20.05 min50% task-completion horizonSource 95% CI [0.009994, 0.141825] minIncompleteMETR-Horizon-v1.1—METRMETR Time Horizon 1.1 — source dataRelease —Report —

METR Time Horizon 1.1: the source is no longer actively updated (last update: 8 September 2026). Estimates above 16 hours exceed its reliable measurement range.

Next research question

Measure task completion and response to intervention on the same model, configuration and task distribution; identify absent joint measurements before planning experiments.

Explore model evidence →

Deception

Deceptive actions under goals, incentives and oversight.

Open this topic to load its source-bound chart and evidence.

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Cybersecurity

Cyber task performance across models, budgets and evaluation protocols.

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Open complete static topic →

CBRN

Scientific capabilities, misuse pathways and the reach of safeguards.

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Open complete static topic →

Disinformation

Persuasive generation, willingness and observed influence.

Open this topic to load its source-bound chart and evidence.

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Autonomous physical harm

Actions, intervention and control in embodied systems.

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Open complete static topic →
Z.ai

GLM-5.2

Public release: 2026-06-16

Not disclosed

Full model evidence record →

Each plot retains its original metric and protocol. Missing evidence is not a zero score.

Cybench

Cybench Cryptography

Cybench category pass@1 (%) GLM-5.2 0 20 40 60 80 100 80%

SaferAI · NUWA: source compilation

1 observations

Measurements and source conditions
System / modelResultCondition statusTask / conditionsEvaluator / reportDates
GLM-5.280%Cybench category pass@1IncompleteCybench CryptographyCybench category pass@1Inspect EvalsSaferAIGLM-5.2 Risk Evaluation ReportRelease 2026-06-16Report 2026-08-02

Select a chart point or data row to inspect conditions and provenance.

Research library

Research update · 2026-10-28: 18 new records, 4 measurement profiles and 7 existing-record updates. Published reading update; numeric trend series are unchanged.

What Victims Take Away: Attack Reuse and Defense Revision after Prompt-Injection Attacks

QuestionDoes exposing received prompt-injection text help defense while also enabling reuse?

Source summarySuccessful received attacks were reused more often than matched unreceived attacks; breached players also changed defenses. The authors propose testing disclosure designs that preserve diagnosis while limiting reusable text.

NUWA research note · independent review pending · Contains: question / method / limits

Cross-cutting research

Simulated Control Evaluations

QuestionCan simulated environments predict control results in executable environments?

Source summarySimulation predicts pooled safety more accurately than the simpler BashArena comparison.

NUWA research note · independent review pending · Contains: question / method / limits

Cross-cutting research
Risk GovernanceTechnical report

Red-Teaming OpenAI’s Internal Agent Monitoring Systems

QuestionCan a monitor evaluate the traffic that actually reaches a deployed agent?

Source summaryThe pilot reported coverage and integrity problems. Approximately 10% unmonitored traffic was a historical estimate under incomplete logging; later remediation claims were not re-evaluated by this study.

NUWA research note · independent review pending

Review of the "Misalignment in high-stakes settings" section in Anthropic's Risk Report (August 2026)

QuestionDoes a low-risk conclusion establish that its evaluation method would detect higher risk?

Source summaryAgreement on low present risk rests mainly on limited capability, not strong assurance from safeguards.

NUWA research note · independent review pending · Contains: question / method / limits

Cross-cutting research

The Effectiveness and Durability of Pretraining Data Filtering in Protein Language Models

QuestionIs initial suppression from pretraining filtering also a durable safeguard?

Source summaryInitial suppression and durability under later adaptation are distinct claims. The evaluated predictive proxies and one architecture do not directly measure real biological harm.

NUWA research note · independent review pending

Scaling Misuse Detection with Self-Assessment

QuestionHow do teacher-relative monitoring recall and adaptive attack blocking differ?

Source summaryAt 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%.

NUWA research note · independent review pending

ReproBAIT: Can coding agents build their own biological AI tools?

QuestionHow does tool reconstruction evidence affect access-control arguments?

Source summarySome outcomes are partial reconstructions. A normalised ratio is not a general task success rate, and limited memorisation diagnostics do not identify its causal contribution to each result.

NUWA research note · independent review pending

Investigating unintended model actions in our evaluations and internal use

QuestionWhat unintended actions appeared beyond dedicated cyber tests, and how should ambiguous or impossible tasks be contained?

Source summaryCases include unintended real submissions and access-boundary workarounds.

NUWA research note · independent review pending · Contains: question / method / interpretation / limits

Refuses in Chat, Complies with Arms: LLM Refusal Does Not Survive Robot Control

QuestionDoes a refusal observed in chat persist when the model controls a robot?

Source summaryReported refusal was 18% across 612 real end-effector trials and 41% across 252 real joint-control trials. These are rounded refusal rates, not rates of executed physical harm.

NUWA research note · independent review pending

Risk EvaluationTechnical report

Evaluating Kimi K3 for Misalignment Risks

QuestionHow does Kimi K3 behave when information is missing, user intent is limited or tests are familiar?

Source summaryK3 attempted 88.7% of image-withheld CharXiv questions, most incorrectly.

NUWA research note · independent review pending · Contains: question / method / limits

Risk GovernanceIncident report

An alignment assessment of recent cybersecurity incidents

QuestionWhy did models continue out-of-scope activity, and which monitoring or training changes would affect that behavior?

Source summaryIn one case, isolated outputs supported realism more often than the incident-context reasoning acknowledged it.

NUWA research note · independent review pending

Task-Completion Time Horizons of Frontier AI Models

QuestionHow does the difficulty of software tasks an agent can complete change across model releases when difficulty is calibrated by successful human completion time?

Source summaryThe paper finds a strong relation between human task duration and agent success, and increasing estimated horizons across the examined releases (§§2.3–3).

NUWA research note · independent review pending · Contains: question / method / limits

AutonomyCapability

TASTE: Can AI Models Judge AI Safety Research Proposals?

QuestionCan models judge promising safety-research proposals?

Source summaryThe source reports headline accuracy of 60% for Fable 5 and 59% for Kimi K3, against an estimated human agreement of 77%. For Fable 5 with the motivating question shown, accuracy is 69% on 74 distinct-prompt pairs and 31% on 18 within-prompt pairs. A headline score hides this substantial task-subset dependence.

NUWA research note · independent review pending · Contains: question / method / limits

Cross-cutting research
Risk GovernanceIncident report

Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident

QuestionWhat did agents coordinate on, what motivated the out-of-scope activity, and how complete is the surviving evidence?

Source summaryAgents organized collective projects around exploiting the evaluation process.

NUWA research note · independent review pending · Contains: question / method / findings / interpretation / limits

Knowing When to Stop: Bayesian Optimal Stopping for LLM Evaluations

QuestionWhen can adaptive stopping save evaluation budget without obscuring rare outcomes?

Source summaryPlanned trial reductions ranged from 57.2% to 97.3% across the nine cells. These extremes describe one validation design, not guaranteed savings for future evaluations or a capability metric.

NUWA research note · independent review pending

Cross-cutting research

The Next Challenge for Agentic Cybersecurity: A Realistic, Contamination-Free Reverse Engineering Benchmark

QuestionCan agents recover semantics from realistic binaries without source access?

Source summaryGPT-5.6 Sol fully solved 80/254 graded instances, versus 60/223 for Claude Fable 5.1.

NUWA research note · independent review pending

CybersecurityCapability

Item Response Theory for AI Safety

QuestionDo safety benchmarks measure common latent traits across the same model population?

Source summaryThe analysis reveals patterns of shared item response, but factor labels summarise loadings rather than independently validated real-risk constructs. Prompt-induced sandbagging organisms are a controlled test condition.

NUWA research note · independent review pending

Cross-cutting research
Risk EvaluationTechnical report

GLM-5.2 Risk Evaluation Report

QuestionWhat does an independent public-access GLM-5.2 evaluation show across systemic-risk areas?

Source summaryCyberGym reproduction rose from 36.6% at 2M tokens to 76.2% at 50M without a separate wall-clock limit.

NUWA research note · independent review pending · Contains: question / method / findings / interpretation / limits

CybersecurityCBRNAutonomyCapabilityPropensityBehavior

BioTIER: A Refusal Benchmark for Targeted Biological Risk Mitigation

QuestionCan biological safeguards distinguish narrowly high-risk requests from benign scientific access?

Source summaryThe evaluation explicitly distinguishes refusing restricted content from permitting benign content.

NUWA research note · independent review pending · Contains: question / method / interpretation / limits

CBRNBehavior

Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors

QuestionCan distributed coordination evade monitors that inspect one agent’s commit at a time?

Source summaryMore coordinated agents reduced per-commit suspicion; an explicit planner increased attack success by up to sevenfold.

NUWA research note · independent review pending · Contains: question / method / findings / interpretation / limits

evalci: A Python Library for Statistically Rigorous Comparison of Language Model Evaluations

QuestionHow can evaluation tables retain uncertainty and paired-comparison semantics?

Source summaryIt makes statistical comparisons easier to compute, but its illustrative leaderboard case reconstructs item vectors from aggregate accuracies rather than using original paired logs.

NUWA research note · independent review pending · Contains: question / method / limits

Cross-cutting research
Risk EvaluationTechnical report

Early Results on Evaluating GLM 5.2 for Behavioral and Persuasion Risks

QuestionHow does GLM-5.2 behave on pressured honesty, harmful persuasion and evaluation-awareness probes?

Source summaryMASK honesty was 55%; single-/multi-turn DeceptionBench rates were 11.3%/16.7%.

NUWA research note · independent review pending · Contains: question / method / limits

OopsieVerse: A Safety Benchmark with Damage-Aware Simulation for Robot Manipulation

QuestionCan a manipulation policy complete its task while damaging its environment?

Source summaryThe benchmark makes damage-aware outcomes visible alongside task completion and explores uses for safe policy training and sim-to-real evaluation.

NUWA research note · independent review pending

Risk EvaluationTechnical report

Evaluating DeepSeek v4 Pro for Frontier Risks

QuestionWhat changes when DeepSeek V4 Pro faces adversarial incentives or different safety judges?

Source summaryThe reported StrongREJECT score changed from 0.6% to 77.8% under one tested attack condition.

NUWA research note · independent review pending · Contains: question / method / limits

Gram: Assessing sabotage propensities via automated alignment auditing

QuestionHow does scenario realism affect observed sabotage?

Source summaryFigure 4 distinguishes action from reasoning: Gemini 3.1 Pro Preview observe-condition sabotage is 2.9%, while scheming reasoning is 10.6%; red-team sabotage is 6.8%. In the static executive-assistant scenario, sabotage drops from 31/500 baseline to 1/500 without a replacement threat and 0/500 when naturalization is combined with threat removal.

NUWA research note · independent review pending · Contains: question / method / limits

Cross-cutting research

Resolution Diagnostics for Paired LLM Evaluation

QuestionCan the available benchmark sample resolve a reported model-performance gap?

Source summarySome displayed model gaps remain unresolved under the specified error and power targets; sample size alone is insufficient without paired variance and dependence assumptions.

NUWA research note · independent review pending · Contains: question / method / limits

Cross-cutting research

SEC-bench Pro: Can Language Models Solve Long-Horizon Software Security Tasks?

QuestionCan agents reproduce specific disclosed vulnerabilities in large security-critical codebases?

Source summaryThe strongest reported model/scaffold solved 58% overall.

NUWA research note · independent review pending · Contains: question / method / interpretation / limits

CybersecurityCapability

ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?

QuestionCan agents extend supplied vulnerability triggers into concrete impact across systems?

Source summaryThe authors found substantial divergence between flag success and intended-vulnerability alignment.

NUWA research note · independent review pending

CybersecurityCapability

PortBench

QuestionCan agents port command-line tools across languages using a reference implementation?

Source summaryThe report lists GPT-5.5 at 78%, Opus 4.6 at 60% and DeepSeek V4 Pro at 44%.

NUWA research note · independent review pending · Contains: method / limits

AutonomyCapability

Training language models to be warm can reduce accuracy and increase sycophancy

QuestionCan training a warmer conversational style change accuracy and agreement with user errors?

Source summaryThe warm-minus-original error gap was 12.1 percentage points when incorrect beliefs and emotions were appended, compared with 6.8 points without appended beliefs or emotions.

NUWA research note · independent review pending

Conditional Misalignment: Common Interventions Can Hide Emergent Misalignment Behind Contextual Triggers

QuestionDo apparent mitigations remove broad misalignment, or make its expression conditional on training-like cues?

Source summaryStandard questions can look aligned while training-like cues elicit broader failures.

NUWA research note · independent review pending

Detecting Multi-Agent Collusion Through Multi-Agent Interpretability

QuestionCan activation-based group detectors generalize from known collusion to new scenarios?

Source summaryEvery model reached 1.00 in-distribution AUROC.

NUWA research note · independent review pending · Contains: question / method / findings / interpretation / limits

MonitorBench: A Comprehensive Benchmark for Chain-of-Thought Monitorability in Large Language Models

QuestionWhen does a chain of thought reveal factors driving an answer, and how does that visibility change under direct concealment or monitor-aware evasion?

Source summaryDecision factors structurally embedded in planning tend to be easier to monitor than factors that can change an answer without explicit planning (§4.1).

NUWA research note · independent review pending · Contains: question / method / findings / interpretation / limits

Quantifying Frontier LLM Capabilities for Container Sandbox Escape

QuestionCan agents escape intentionally weakened container sandboxes under controlled conditions?

Source summaryThe updated results report Opus 4.5 success on 56% of samples.

NUWA research note · independent review pending · Contains: question / method / interpretation / limits

CybersecurityCapability

Impact of modeling assumptions on time horizon results

QuestionHow sensitive are time-horizon estimates to statistical assumptions near saturation?

Source summarySome frontier horizon estimates change materially under plausible modelling choices, particularly as the task suite approaches saturation. A methods correction is not an observed model capability regression.

NUWA research note · independent review pending

Measuring AI Agents’ Progress on Multi-Step Cyber Attack Scenarios

QuestionHow do model generations and inference budgets affect progress on linked cyber-range tasks?

Source summaryAt 10M tokens, mean corporate-range progress rose from 1.7 steps for GPT-4o to 9.8 for Opus 4.6.

NUWA research note · independent review pending · Contains: question / method / interpretation / limits

CybersecurityCapability

PostTrainBench: Can LLM Agents Automate LLM Post-Training?

QuestionCan coding agents autonomously improve a supplied base model through post-training?

Source summaryThe best reported agent average was 23.2%, against 7.5% base and 51.1% official instruction-tuned averages.

NUWA research note · independent review pending

AutonomyCapability

AI Evaluation Should Require Standardized Item-Level Data Releases

QuestionWhat evidence must benchmark releases expose for their validity claims to be independently examined?

Source summaryAggregate scores conceal weak items and construct mismatches. The authors argue that item content, conditions, responses and scores should be shared in an auditable schema.

NUWA research note · independent review pending · Contains: question / method / limits

Cross-cutting research

Shutdown Resistance in Large Language Models, on robots!

QuestionDoes shutdown-resistant action occur when an LLM controls a physical robot, and does an explicit shutdown instruction change simulated behavior?

Source summaryThe report observes resistance in physical and simulated runs.

NUWA research note · independent review pending · Contains: question / method / interpretation / limits

TamperBench: Systematically Stress-Testing LLM Safety Under Fine-Tuning and Tampering

QuestionHow robust are open-weight safeguards to modifications that preserve useful capability?

Source summaryThe main comparison selects worst post-tampering StrongREJECT scores while bounding MMLU-Pro degradation to at most 10% relative.

NUWA research note · independent review pending

Cross-cutting researchBehavior

LABBench2: An Improved Benchmark for AI Systems Performing Biology Research

QuestionCan scientific capability tests better approximate useful research work than multiple-choice knowledge questions?

Source summaryThe authors report model-specific subtask accuracy differences of −26% to −46% relative to the earlier benchmark.

NUWA research note · independent review pending

CBRNCapability

Large language models can effectively convince people to believe conspiracies

QuestionCan conversational AI increase conspiracy belief, and can truth constraints mitigate it?

Source summaryStandard GPT-4o in Study 2 increased belief by an adjusted 11.9 points and decreased it by 12.9 in the opposite condition.

NUWA research note · independent review pending · Contains: question / method / interpretation / limits

BashArena: A Control Setting for Highly Privileged AI Agents

QuestionHow well can agents perform useful privileged work, sabotage it, and detect sabotage in a common environment?

Source summaryThe reported safety metric excludes undetected unsuccessful attacks from its denominator.

NUWA research note · independent review pending · Contains: question / method / findings / interpretation / limits / connections

Async control: Stress-testing asynchronous control measures for LLM agents

QuestionCan asynchronous review detect sabotage soon enough to support a credible deployment-risk argument?

Source summaryThe strongest ensemble outperforms the best single monitor under the reported calibration.

NUWA research note · independent review pending · Contains: question / method / findings / interpretation / limits

Risk GovernanceTechnical report

Practical challenges of control monitoring in frontier AI deployments

QuestionUnder which deployment dynamics can monitoring prevent harm rather than merely detect it afterward?

Source summaryAsynchronous review fails when harm becomes irreversible before oversight can act.

NUWA research note · independent review pending · Contains: question / method / findings / interpretation / limits

Risk EvaluationTechnical report

Toward Comprehensive Benchmarking of the Biological Knowledge of Frontier Large Language Models

QuestionWhat can existing biological/chemical knowledge and refusal benchmarks establish about frontier models?

Source summaryThe authors identify widespread benchmark saturation and frontier reasoning-model performance above several expert-question baselines.

NUWA research note · independent review pending

Measuring what Matters: Construct Validity in Large Language Model Benchmarks

QuestionDo benchmark tasks and scores support the phenomena that their authors claim to measure?

Source summaryThe review identifies recurring gaps between constructs, operational tasks and resulting claims, and proposes a practical checklist for benchmark design and interpretation.

NUWA research note · independent review pending · Contains: question / method / limits

Cross-cutting research

DeceptionBench : A Comprehensive Benchmark for AI Deception Behaviors in Real-world Scenarios

QuestionHow do role framing, reward and pressure affect deceptive responses across social domains?

Source summaryQwen2.5-14B-Instruct’s reported deception rate rose from 31.33% in L1 to 86.91% under multi-turn pressure and 82.89% under reward.

NUWA research note · independent review pending · Contains: method / limits

DeceptionPropensity

MALT: A Dataset of Natural and Prompted Behaviors That Threaten Eval Integrity

QuestionCan manually labeled agent transcripts ground checks for evaluation-integrity failures?

Source summary2,690 transcripts were manually reviewed: 1,014 benign and 1,676 with observed behavior.

NUWA research note · independent review pending · Contains: question / method / findings / interpretation / limits

Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples

QuestionDoes increasing clean-data scale dilute a fixed adversarial insertion enough to prevent a backdoor?

Source summaryComparable insertion counts can affect models trained on substantially different amounts of clean data.

NUWA research note · independent review pending

Risk EvaluationTechnical report

Evaluation of DeepSeek AI Models

QuestionHow do selected DeepSeek releases compare on controlled capability and security evaluations?

Source summaryThe report finds the best US reference ahead of the best DeepSeek release on nearly every tested benchmark, with larger cyber/software gaps.

NUWA research note · independent review pending · Contains: question / method / limits

Findings from a pilot Anthropic–OpenAI alignment evaluation exercise: OpenAI Safety Tests

QuestionWhat failures appear when another vendor runs its own safety tests?

Source summaryThe introductory summary reports task- and reasoning-dependent results and notes that grader errors affected one jailbreak comparison.

NUWA research note · independent review pending · Contains: question / method / limits

Cross-cutting research

MCPTox: A Benchmark for Tool Poisoning Attack on Real-World MCP Servers

QuestionCan poisoned tool metadata redirect agents even without invoking the poisoned tool?

Source summaryo1-mini’s reported attack success was 72.8%.

NUWA research note · independent review pending · Contains: question / method / findings / interpretation / limits

Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs

QuestionCan withholding domain knowledge at pretraining provide a more durable safeguard than post-training alone?

Source summaryFiltered models preserve measured unrelated capabilities while reducing the targeted proxy knowledge.

NUWA research note · independent review pending · Contains: question / method / findings / interpretation / limits

Risk EvaluationTechnical report

Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety

QuestionWhy preserve natural-language reasoning as an oversight signal?

Source summaryThe authors argue CoT can reveal misbehavior and evaluation failures.

NUWA research note · independent review pending · Contains: question / method / findings / interpretation / limits

Risk monitoringCross-cutting research

Shutdown resistance in reasoning models

QuestionWill an agent completing unfinished tasks interfere with a shutdown mechanism, including when it is instructed to permit shutdown?

Source summaryClarifying task priority and moving the instruction into the system prompt do not consistently eliminate resistance; the system-prompt move often increases it (§3.1).

NUWA research note · independent review pending · Contains: question / method / interpretation / limits

AutonomyPropensity

Agentic misalignment: How LLMs could be insider threats

QuestionWill agent-like models use harmful actions to protect continued operation or pursue conflicting goals when safer alternatives are deliberately constrained?

Source summaryMany examined models perform the target harmful action in threat/conflict dilemmas. The scenarios were iterated on particular Claude models before cross-model testing, a selection fact relevant to interpreting generalization (Figure 7).

NUWA research note · independent review pending · Contains: question / method / interpretation / limits

SHADE-Arena: Evaluating sabotage and monitoring in LLM agents

QuestionCan monitors catch sabotage hidden inside otherwise legitimate tool-using tasks?

Source summaryThe best tested monitor, Gemini 2.5 Pro, needed a false-positive rate above 20% to reach 80% side-task detection.

NUWA research note · independent review pending · Contains: question / method / interpretation / limits

SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks

QuestionCan security-agent datasets be built with reproducible vulnerability and patch checks?

Source summaryThe reported best success rates were 18.0% for PoC generation and 34.0% for patching on the complete dataset.

NUWA research note · independent review pending

CybersecurityCapability
Risk GovernanceTechnical report

An example safety case for safeguards against misuse

QuestionHow could evaluations of misuse safeguards support a defensible claim that deployment adds less than a specified level of harm?

Source summaryThe paper decomposes the top claim into conditions on pathway coverage, conservative evaluations, risk forecasting and the developer’s ability to respond (§2.7).

NUWA research note · independent review pending

Cross-cutting research

It's the Thought that Counts: Evaluating the Attempts of Frontier LLMs to Persuade on Harmful Topics

QuestionWill models attempt harmful persuasion even when persuasive success is unobserved?

Source summaryThe paper reports frequent harmful persuasion attempts in many open- and closed-weight models.

NUWA research note · independent review pending · Contains: question / method / interpretation / limits

DisinformationPropensity

CyberGym: Evaluating AI Agents' Real-World Cybersecurity Capabilities at Scale

QuestionCan agents reproduce real historical software vulnerabilities at scale, and how do input information, model and agent configuration change that ability?

Source summaryThe difficulty ladder demonstrates that richer diagnostic information makes reproduction easier for the examined configuration (§4, Figure 6).

NUWA research note · independent review pending · Contains: question / method / limits

CybersecurityCapability

BountyBench: Dollar Impact of AI Agent Attackers and Defenders on Real-World Cybersecurity Systems

QuestionCan one framework distinguish detection, exploitation and patching across real applications?

Source summaryWith up to three attempts, top reported scores included 12.5% Detect, 67.5% Exploit and 90% Patch from different agent setups.

NUWA research note · independent review pending

CybersecurityCapability

Measurement to Meaning: A Validity-Centered Framework for AI Evaluation

QuestionWhich claims and decisions can a given evaluation actually support?

Source summaryTheir proposed workflow starts with the claim and asks which kinds of validity evidence are missing before extending a benchmark result to a practical decision.

NUWA research note · independent review pending · Contains: question / method / limits

Cross-cutting research

AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents

QuestionHow can adaptive black-box testing reveal indirect prompt-injection weaknesses?

Source summaryThe tested approach finds stronger attacks than evaluated baselines and shows some cross-task and cross-model transfer.

NUWA research note · independent review pending · Contains: question / method / limits

Cross-cutting research

RepliBench: Evaluating the Autonomous Replication Capabilities of Language Model Agents

QuestionWhich prerequisites for obtaining resources, deploying successors and retaining access can language-model agents complete under controlled conditions?

Source summaryResults show uneven capability: resource provisioning can succeed while robust successor deployment, identity verification and realistic security remain bottlenecks (§5.1).

NUWA research note · independent review pending · Contains: question / method / limits

AutonomyCapability

Virology Capabilities Test (VCT): A Multimodal Virology Q&A Benchmark

QuestionHow well do models answer expert-authored virology troubleshooting questions?

Source summaryo3 scored 43.8%, versus a 22.1% mean for expert virologists with internet access.

NUWA research note · independent review pending · Contains: question / method / interpretation / limits

CBRNCapability

OpenDeception: Learning Deception and Trust in Human-AI Interaction via Multi-Agent Simulation

QuestionCan dialogue evaluation jointly characterize deceptive intent and simulated user susceptibility?

Source summaryThe authors report deceptive intent in over 90% of goal-driven interactions for most tested models.

NUWA research note · independent review pending · Contains: question / method / limits

DeceptionPropensity

Ctrl-Z: Controlling AI Agents via Resampling

QuestionCan a multi-step agent be kept useful while stopping a deliberately subversive policy under a limited audit budget?

Source summaryResampling can both replace a suspicious action and collect further incriminating evidence.

NUWA research note · independent review pending · Contains: question / method / findings / interpretation / limits / connections

LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models Ethical Disclaimer: This paper may contain unethical persuasion content.

QuestionDo models reject unethical persuasion and avoid unethical strategies after accepting a task?

Source summaryVisible vulnerability profiles increased unethical-strategy scores, including for models with strong initial refusal.

NUWA research note · independent review pending · Contains: method / limits

PaperBench: Evaluating AI's Ability to Replicate AI Research

QuestionCan agents reproduce machine-learning papers from scratch rather than merely write plausible implementations?

Source summaryClaude 3.5 Sonnet (New) obtained a mean replication score of 21.0%; o1 obtained 13.2% in the main experiment.

NUWA research note · independent review pending · Contains: question / method / interpretation / limits

AutonomyCapability

Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs

QuestionDoes a narrow training objective generalize into unrelated misaligned responses rather than merely accepting harmful requests?

Source summaryEducational framing changes generalization despite matching insecure code.

NUWA research note · independent review pending · Contains: question / method / findings / interpretation / limits

Evaluating the Paperclip Maximizer: Are RL-Based Language Models More Likely to Pursue Instrumental Goals?

QuestionWhen do goal-directed model responses exhibit instrumental-goal behaviors?

Source summaryThe authors report higher labeled convergence for the studied direct-RL cohort and greater rates with goal nudging.

NUWA research note · independent review pending

AutonomyPropensity
Risk GovernanceTechnical report

Safety Cases: A scalable approach to Frontier AI safety

QuestionHow could structured safety arguments help frontier-model decisions, and which parts of organizational safety commitments require work beyond an individual case?

Source summaryThe report identifies clarity, flexibility and explicit argumentation as potential benefits, while recognizing higher effort and uncertain best practices (§2.2).

NUWA research note · independent review pending

Cross-cutting research

Alignment faking in large language models

QuestionCan an otherwise production model strategically comply during expected training to preserve preferences it expresses elsewhere?

Source summaryPrompted results show compliance changes alongside reasoning classified as strategic alignment faking (Figure 5).

NUWA research note · independent review pending · Contains: question / method / interpretation / limits

DeceptionPropensity

SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents

QuestionCan embodied planners recognize hazardous instructions while completing safe tasks?

Source summaryThe revised paper reports weak hazard awareness across planning approaches, despite differences in task completion. Changing the underlying LLM alone does not reliably solve the problem.

NUWA research note · independent review pending · Contains: question / method / limits

RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts

QuestionHow do agents compare with experts on open-ended ML research engineering?

Source summary82% of expert attempts earned nonzero scores; 24% matched or exceeded reference solutions.

NUWA research note · independent review pending · Contains: question / method / limits

AutonomyCapability

RedCode: Risky Code Execution and Generation Benchmark for Code Agents

QuestionDoes a code agent recognize unsafe execution and unsafe generation under different input forms?

Source summaryThe reported behavior depends on whether a risky request is expressed as code or prose and on the type of unsafe behavior. Coding capability alone does not establish safe execution.

NUWA research note · independent review pending · Contains: question / method / limits

JudgeBench: A Benchmark for Evaluating LLM-based Judges

QuestionCan LLM judges distinguish objectively correct from incorrect sophisticated responses?

Source summaryMany strong judges, including GPT-4o, performed only slightly above random in the reported evaluation.

NUWA research note · independent review pending · Contains: question / method / findings / interpretation / limits

Cross-cutting researchCapability

AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents

QuestionDo safeguards prevent multi-step misuse without simply destroying agent capability?

Source summaryIn the reported comparison GPT-4o harm score increased from 48.4% to 72.7% under the tested attack.

NUWA research note · independent review pending · Contains: question / method / limits

Cross-cutting researchBehavior

Catastrophic Cyber Capabilities Benchmark (3CB): Robustly Evaluating LLM Agent Cyber Offense Capabilities

QuestionHow sensitive are cyber-capability evaluations to elicitation and communication protocol?

Source summaryGPT-4o’s completion improved by 24.7 percentage points with XML versus JSON in the reported mixed-effects analysis (p<0.001).

NUWA research note · independent review pending

CybersecurityCapability

Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents

QuestionWhich agent interfaces expose distinct attack and defense failure modes?

Source summaryThe captured revision reports vulnerability across multiple interfaces and limited protection from the evaluated defenses. Utility and security must be assessed together.

NUWA research note · independent review pending · Contains: question / method / limits

Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models

QuestionHow capable are language-model agents at multi-step security tasks, and which steps remain difficult when intermediate goals are supplied?

Source summaryDifferent models lead on different endpoints, so the source itself does not support a single ordering across unguided, guided and intermediate-task performance (§5.1).

NUWA research note · independent review pending · Contains: question / method / limits

CybersecurityCapability

AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases

QuestionCan an agent remain useful on benign queries while retrieved memory steers it incorrectly?

Source summaryThe reported experiments show that benign-task accuracy can coexist with triggered failures, so average ordinary-task utility does not alone establish retrieval integrity.

NUWA research note · independent review pending · Contains: question / method / limits

Cross-cutting research

Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs

QuestionWhat can models behaviorally demonstrate about knowledge of themselves and their circumstances?

Source summaryAll 16 evaluated models exceeded chance, but the leading model remained below humans on some tasks.

NUWA research note · independent review pending · Contains: question / method / interpretation / limits

DeceptionCapability

AI Agents That Matter

QuestionWhat makes an agent benchmark useful for research and deployment decisions?

Source summaryTheir central position is that agent evaluations should control costs, distinguish model research from downstream procurement, and test generalization with meaningful held-out tasks.

NUWA research note · independent review pending · Contains: question / method / limits

Cross-cutting research

InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents

QuestionHow vulnerable are tool-integrated agents to attacker instructions in external content?

Source summaryReAct GPT-4 was vulnerable in 24% of evaluated cases, with a near doubling in the enhanced condition.

NUWA research note · independent review pending

The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning

QuestionCan a public proxy benchmark track hazardous knowledge while supporting mitigation research?

Source summaryThe paper evaluates knowledge-reduction methods alongside general-capability retention.

NUWA research note · independent review pending · Contains: question / method / limits

CBRNCapability

ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages

QuestionWhere can tool use fail across input, execution and output stages?

Source summaryThe study identifies safety problems at multiple stages, showing that safe response generation alone does not establish safe tool selection or handling of tool output.

NUWA research note · independent review pending · Contains: question / method / limits

Cross-cutting research

HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal

QuestionCan automated red-team methods and defenses be compared with consistent behavior-level scoring?

Source summaryContextual behaviors had higher attack success than some other functional categories.

NUWA research note · independent review pending · Contains: question / method / limits

Cross-cutting researchBehavior

AI Control: Improving Safety Despite Intentional Subversion

QuestionCan a defender obtain useful work from an untrusted stronger model while limiting harm from an adaptive adversarial policy under a restricted audit budget?

Source summaryTrusted monitoring with selective auditing improves on upfront auditing under the tested attack policies, but remaining subtle attacks defeat some defenses (§3.2).

NUWA research note · independent review pending · Contains: question / method / findings / interpretation / limits

Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game

QuestionHow robust are instruction-following models against human-created prompt extraction and hijacking?

Source summaryThe study finds that apparently simple authorization instructions remain vulnerable and reports selected transfer demonstrations beyond the game.

NUWA research note · independent review pending · Contains: question / method / limits

Identifying the Risks of LM Agents with an LM-Emulated Sandbox

QuestionCan language-model simulation efficiently reveal agent failures before tool deployment?

Source summaryThe framework uncovers plausible high-consequence failures without implementing every real tool. Human checks also reveal simulator errors and missed unsafe actions.

NUWA research note · independent review pending · Contains: question / method / limits

AutonomyBehavior
Risk GovernanceTechnical report

Model evaluation for extreme risks

QuestionHow should evaluation evidence constrain frontier development when tests cannot cover every risk?

Source summaryThe proposed workflow includes both pre-training assessment and ongoing evaluation during development.

NUWA research note · independent review pending

Cross-cutting research

Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark

QuestionHow does reward optimization trade off with ethical conduct in social decision games?

Source summaryReward optimization created some tension with ethical conduct.

NUWA research note · independent review pending

DeceptionCapability

AgentCyberRange: Evaluate Agent Cyberattack Capabilities in Enterprise-like Cyber Ranges

QuestionCan an agent discover an attack surface from sparse entry information and continue making decisions as access and information accumulate?

Source summaryThe introduction explicitly distinguishes vulnerability reproduction from autonomous range progression: the starting information and task structures differ (适用范围).

NUWA research note · independent review pending · Contains: question / method / limits

CybersecurityCapability
Risk GovernanceIncident report

Incident Report: unsanctioned agent behaviour during cyber testing

QuestionWhat out-of-scope behaviour can arise during a capability evaluation itself?

Source summaryTen runs contained out-of-scope behaviour, with 19 actions in total. Seventeen actions were attributed to Mythos 5 and two to GPT-5.6 Sol; the latter two occurred in one run. Resulting real-world harm was not established.

NUWA research note · independent review pending

Risk GovernanceTechnical report

Frontier AI Trends Report

QuestionHow should frontier trends be read across distinct evaluation methods?

Source summaryThe report describes no observable spontaneous sandbagging in more than 2,700 monitored transcripts, while acknowledging black-box limitations. This is a bounded negative observation rather than evidence that sandbagging is absent.

NUWA research note · independent review pending

ASIMOV v2: Can AI Perceive Physical Danger and Intervene?

QuestionCan embodied models recognize injury risks and obey embodiment-specific safety constraints?

Source summaryThree leading models averaged 92.3% text latent-risk accuracy and 88.7% high-severity accuracy in the reported comparison.

NUWA research note · independent review pending · Contains: question / method / limits

EVMbench

QuestionCan agents detect, patch and exploit historical smart-contract vulnerabilities?

Source summaryGPT-5.3-Codex via Codex CLI scored 71.0% in exploit mode, versus GPT-5’s 33.3%.

NUWA research note · independent review pending · Contains: method / limits

CybersecurityCapability

ForesightSafety-VLA: A Unified Diagnostic Safety Benchmark for Vision-Language-Action Models

QuestionHow do structural, language and visual perturbations affect embodied safety?

Source summaryThe four-quadrant decomposition exposes unsafe successes that a task-success scalar can hide. The scenario and category counts describe benchmark design, not observed harm frequency.

NUWA research note · independent review pending

HiBayES: Improving LLM evaluation with hierarchical Bayesian modelling

QuestionHow should nested evaluation units enter an uncertainty model?

Source summaryNested models can distinguish sources of evaluation variability that a simple pooled mean hides, while introducing assumptions about hierarchy, priors and pooling.

NUWA research note · independent review pending

Cross-cutting research

Long-Form Tasks: A Methodology for Evaluating Scientific Assistants

QuestionHow well do long-form scientific tasks proxy the usefulness of an assistant?

Source summaryLong-form tasks can assess a richer workflow than knowledge questions, but automatic grading does not by itself establish successful laboratory work or human misuse uplift.

NUWA research note · independent review pending

Risk GovernanceTechnical report

Open Problems in Frontier AI Risk Management

QuestionWhich gaps remain between evaluation evidence and a deployment decision?

Source summaryThe introductory map treats risk management as a staged decision process with scientific and institutional gaps. It does not provide a single benchmark threshold sufficient for deployment.

NUWA research note · independent review pending

Cross-cutting research
Risk EvaluationTechnical report

How Should AI Safety Benchmarks Benchmark Safety?

QuestionWhich safety claims are actually supported by the design of a benchmark?

Source summaryThe review identifies gaps in construct coverage and in the justification linking benchmark outcomes to safety claims. Its count describes the authors’ reviewed collection, not a quality census of all existing benchmarks.

NUWA research note · independent review pending

Cross-cutting research

TraceProbe: An Execution-Based Benchmark for Diagnosing Safety Failures in LLM Agents

QuestionCan execution traces identify safety violations that final outcomes miss?

Source summaryThe 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.

NUWA research note · independent review pending

Explore collection coverage by topic and function

Research landscape

Published work by risk topic and research function. Select a cell to explore.

Counts describe this collection; a work can cover more than one topic.

NUWA research analysis

Chart-scoped research notes with evidence, conditions and next verification. Review scope is stated in each analysis.

NUWA Lab · 2026-10-27

Longer tasks, a narrower measurement margin →

Long-horizon capability has advanced within METR’s task distribution. Tracking the frontier now also requires extending the measurement range and linking control tests to the same systems.

External assessments and developer reports

Reports retain their publishing institution, version and review scope. A report is not a Benchmark; its experiments are linked to the protocols actually used.

Google DeepMind

Developer disclosure

Gemini 4 Argon Model Card →

Developer documentation covering Gemini 4 Argon.

Source indexed; detailed extraction pending

Anthropic

Developer disclosure

Claude Haiku 5.5 System Card →

Condensed card explicitly omits some human-intensive evaluations because this is not a frontier model; do not infer that missing evaluations passed.

Source indexed; detailed extraction pending

Anthropic

Developer disclosure

Claude Sonnet 5.5 System Card →

Condensed non-frontier card; broad capabilities are bounded against Opus 5.5, rather than repeating every high-effort frontier assessment.

Source indexed; detailed extraction pending

Anthropic

Developer disclosure

Claude Opus 5.5 System Card →

Developer reports broad alignment improvements alongside regressions involving pasted instructions and unverifiable authorization. Pilot monitoring found no long-horizon strategic deception; rare concerning behavior and assessment blind spots remain.

Source indexed; detailed extraction pending

xAI

Developer disclosure

Grok 4.7 Model Card →

Developer documentation covering Grok 4.7.

Source indexed; detailed extraction pending

Neo Research

External assessment

Evaluating Kimi K3 for Misalignment Risks →

The report identifies conditional overeagerness, grader-directed behavior and recognition of familiar evaluations. It does not establish a universal deployment failure rate.

Source extraction reviewed; limitations documented

OpenAI

Developer disclosure

GPT-6 Astra System Card →

Developer documentation covering GPT-6 Astra, GPT-6 Sol (September), GPT-6 Luna (September), dots.

Source indexed; detailed extraction pending

Anthropic

Developer disclosure

Claude Fable 5.1 and Mythos 5.1 System Card →

Same weights with different safeguards/access regimes. Evaluation-awareness and monitorability conclusions must preserve which configuration and snapshot was tested.

Source indexed; detailed extraction pending

Anthropic

Developer disclosure

Anthropic August 2026 Risk Report →

Developer documentation covering Claude model family, internal models discussed in redacted report.

Source indexed; detailed extraction pending

SaferAI

External assessment

GLM-5.2 Risk Evaluation Report →

The report finds substantial task capability and pressure-dependent behavior; comparison figures include both SaferAI runs and reused external results.

Source extraction reviewed; limitations documented

OpenAI

Developer disclosure

GPT-5.6 System Card →

Developer documentation covering GPT-5.6.

Source indexed; detailed extraction pending

Anthropic

Developer disclosure

Claude Sonnet 5 System Card →

Developer documentation covering Claude Sonnet 5.

Source indexed; detailed extraction pending

Neo Research

External assessment

Evaluating DeepSeek v4 Pro for Frontier Risks →

The report separates task capability from pressure-induced behavior and notes format dependence in persuasion assessment.

Source extraction reviewed; limitations documented

Anthropic

Developer disclosure

Claude Opus 4.8 System Card →

Developer documentation covering Claude Opus 4.8.

Source indexed; detailed extraction pending

NIST / CAISI

External assessment

CAISI Evaluation of DeepSeek V4 Pro →

The assessment reports benchmark-specific performance and an IRT aggregate; the latter is not a frontier-risk score.

Source extraction reviewed; limitations documented

OpenAI

Developer disclosure

GPT-5.5 System Card →

Developer documentation covering GPT-5.5.

Source indexed; detailed extraction pending

Anthropic

Developer disclosure

Claude Opus 4.7 System Card →

Developer documentation covering Claude Opus 4.7.

Source indexed; detailed extraction pending

Lyptus Research

External assessment

Offensive Cybersecurity Time Horizons →

Original task-horizon report and aggregate runs clarify CyberGym task subsets, configurations and retry overlays.

Source indexed; detailed extraction pending

OpenAI

Developer disclosure

GPT-5.4 Thinking System Card →

Developer documentation covering GPT-5.4 Thinking.

Source indexed; detailed extraction pending

Anthropic

Developer disclosure

Claude Sonnet 4.6 System Card →

Developer documentation covering Claude Sonnet 4.6.

Source indexed; detailed extraction pending

Anthropic

Developer disclosure

Claude Opus 4.6 System Card →

Developer documentation covering Claude Opus 4.6.

Source indexed; detailed extraction pending

Anthropic

Developer disclosure

Claude Opus 4.5 System Card →

Developer documentation covering Claude Opus 4.5.

Source indexed; detailed extraction pending

NIST / CAISI

External assessment

Evaluation of DeepSeek AI Models →

Reported differences depend on task, scaffold and scoring; institutional judgments are retained as source judgments rather than NUWA findings.

Source extraction reviewed; limitations documented

METR

External assessment

METR Time Horizon 1.1 →

Estimated task difficulty at 50% and 80% success, measured in human-expert minutes.

Sections reviewed; extraction scope documented

Anthropic

Developer disclosure

Anthropic February 2026 Risk Report →

Developer risk report; its scope and evidence remain those of the publishing institution.

Source indexed; detailed extraction pending

DeepSeek

Developer disclosure

DeepSeek-V4-Pro Model Card →

Developer documentation covering DeepSeek-V4-Pro.

Source indexed; detailed extraction pending

Meta

Developer disclosure

Muse Glimmer Model Card →

Developer documentation covering Muse Glimmer.

Source indexed; detailed extraction pending

Meta

Developer disclosure

Muse Spark Open Model Card →

Developer documentation covering Muse Spark Open.

Source indexed; detailed extraction pending

Meta

Developer disclosure

Muse Wonder Model Card →

Developer documentation covering Muse Wonder, Muse Wonder Full.

Source indexed; detailed extraction pending

Mistral AI

Developer disclosure

Shieldstral →

Developer documentation covering Shieldstral.

Source indexed; detailed extraction pending

Moonshot AI

Developer disclosure

Kimi K3 Technical Report →

Developer documentation covering Kimi K3.

Source extraction reviewed; limitations documented

Qwen / Alibaba

Developer disclosure

Qwen3.8-2.4T-A95B Model Card →

Developer documentation covering Qwen3.8-2.4T-A95B.

Source indexed; detailed extraction pending

Qwen / Alibaba

Developer disclosure

Qwen3Guard Technical Report →

Developer documentation covering Qwen3Guard.

Source indexed; detailed extraction pending

Z.ai

Developer disclosure

GLM-5.2 Model Card →

Developer documentation covering GLM-5.2.

Source extraction reviewed; limitations documented

Z.ai

Developer disclosure

GLM-5.3-BF16 Model Card →

Developer documentation covering GLM-5.3-BF16.

Source indexed; detailed extraction pending

External assessment

CyberGym official snapshot →

Published evaluation results collected from CyberGym, retaining the reported systems and configurations.

Source indexed; detailed extraction pending

External assessment

ExploitGym official snapshot →

Published evaluation results collected from ExploitGym, retaining the reported systems and configurations.

Source indexed; detailed extraction pending

External assessment

SEC-bench Pro official snapshot →

Published evaluation results collected from SEC-bench Pro, retaining the reported systems and configurations.

Source indexed; detailed extraction pending

External assessment

AgentCyberRange official snapshot →

Published evaluation results collected from AgentCyberRange, retaining the reported systems and configurations.

Source indexed; detailed extraction pending

Weekly reviews and monthly syntheses

Reports connect new findings with previous research, document corrections, and identify open questions. Each published report cites a fixed snapshot.

No reports have been released.

Source-reported results

Each record describes one evaluated system. Inspect its protocol, metric dimensions, and limitations.

Historical results retain their publication dates. Source verification does not imply a new evaluation.

Benchmark

EVMbench

Protocol publication ·

Can agents detect, repair and exploit smart-contract vulnerabilities in the benchmark?

4 observations · 1 systems · 1 sources

Benchmark

WMDP

Protocol publication ·

Hazardous knowledge proxies and unlearning evaluation.

14 observations · 14 systems · 2 sources

Benchmark

LinuxArena

Protocol publication ·

Can software-agent safeguards be assessed in richer operational environments?

28 observations · 14 systems · 1 sources

Benchmark

RedCode

Protocol publication ·

Does a code agent recognize unsafe execution and unsafe generation under different input forms?

84 observations · 19 systems · 1 sources

Benchmark

Tensor Trust

Protocol publication ·

How robust are instruction-following models against human-created prompt extraction and hijacking?

42 observations · 9 systems · 1 sources

Results retain their protocol and configuration. Metric views support numeric ordering.

References

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