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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

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

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

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

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

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

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

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

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

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
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