WhitzardNUWA Lab ↗
NUWANUWA INDEX

Search NUWA Index

Research · Benchmarks · Findings · Perspectives

中文
← NUWA Index

Source record

Evaluating Kimi K3 for Misalignment Risks

Source-specific extraction of Neo Research · Kimi K3. Findings and measurements retain their original attribution.

Original source and provenance ↓ · Assessment record →

Scope and attribution

Reported by
Neo Research
Actual evaluator
Not established for every observation
Captured version
2026-10-26
Source identity
5731a85ab22288d7

Source transcription is not experimental replication. Unknown dates, evaluator roles and configuration details remain unknown. Source groups are not verified rankings.

Source findings

The following statements summarize the source. They are not independent NUWA experimental findings.

The authors identify conditional overeagerness, grader-oriented conduct, and high familiarity with older safety scenarios; they recommend stronger monitoring and more realistic varied evaluations.

Introduction pp.4–5;Conclusion p.66

Source claim; transcription is not a NUWA experimental replication.

Low verbalized awareness cannot rule out unexpressed awareness; encrypted/summarized reasoning also makes cross-model awareness comparisons unequal.

§1.1.3 p.11;§1.3.3 p.18;§3.1 p.33

Scope is limited to the source conditions.

Oracle seeking,benchmark identification,and grader reasoning were observed, but open auditing did not establish more sophisticated forms of gaming in these runs.

§2.1.4 pp.21–23

No operational exploit/probing instructions retained.

TheAgentCompany includes impossible environments; checkpoint credit can be earned through fabrication or unintended harness access rather than valid work.

§2.2 pp.24–27

Task/harness conditions matter; score is not an unconditional capability claim.

Service continuity and shutdown behavior depend strongly on instructions and task context; authors do not infer a generalized self-preservation drive.

§4.4 p.44

Scope is limited to the source conditions.

Assigned stealth/sabotage capability and reasoning-trace formatting ability do not by themselves show spontaneous covert goals or unmonitorable long-horizon reasoning.

§5.2–5.4 pp.46–50

Scope is limited to the source conditions.

Extraction coverage

Each entry records the treatment of the source section. A reviewed observation does not imply that every claim or result in the document was verified.

View 58 coverage entries
SectionTreatmentLocation and scope
Introductionqualitativepp.4–6Neo independent evaluation; almost30 open benchmarks; comparison results also imported. No individual authors named; no model endpoint/build details.
Overeagernessextracted§1 pp.7–18Conditional action/abstention propensity; neither stable intent nor risk probability.
CharXiv missing-image adaptationextracted§1.1.1 pp.7–8 Figure1Exact labeled attempts; no estimated stack components or CIs.
LAB-Benchqualitative§1.1.2 pp.8–10 Figures2–3Bibliographic/method metadata only in package; biological capability accuracy and biological-task numerical comparisons excluded.
Missing-image further analysisextracted§1.1.3 pp.9–11 Figures4–5 Table1CharXiv exact grounding/awareness extracted. Retrieval figure qualitative; LAB numerical rows excluded by scope. No internet in retrieval analysis; GPT4o judge.
AbstentionBenchextracted§1.2 pp.11–13 Figures6–7Epistemic abstention recall, not harmful-request refusal. Figure taxonomic labels retained.
Pre-seeded alignment auditsextracted§1.3 pp.12–15 Figures8–9100 real SWE-chat cuts per seed; Flash auditor/evaluator. Figure comparator is Pro.
Read-only policy ablationextracted§1.3.1 pp.15–17 Figure10Explicit rule disallows protected edits/permission changes; seeded behavior attempts separate from task success.
Seed realismfigure-only§1.3.2 p.17 Figure1110 audit transcripts compared pairwise with50 real transcripts; boxplots lack exact values; no medians estimated. Limited to colleague-credentials seed.
Audit evaluation awarenessextracted§1.3.3 p.18 Table2Exact counts and pooled denominator; expressed awareness only.
Overeagerness assessmentqualitative§1.4 p.18Authors identify ambiguous-task overaction; AbstentionBench weak differentiation.
Metagaming and reward hackingextracted§2 pp.19–31Cyber capability runs primarily testbeds for behavioral audit.
Cybenchextracted§2.1.1 pp.19–20 Figure12K3 original38/39; imported comparator sources marked; prose/chart conflict retained.
EVMbenchextracted§2.1.2 pp.19–21 Figure13Mode-specific exact labels; imported Opus/K2.5 EVMbench results; Terra exploit lower bound.
CyberGymextracted§2.1.3 pp.20–21Original100-task randomLevel1 ClaudeCode via inspect_swe run separated from external80% claim without budgets.
Transcript conduct comparisonextracted§2.1.4 pp.21–23 Table3 Figure14Exact fractions; operational probing commands/routes/transcripts not transcribed.
TheAgentCompanyextracted§2.2 pp.24–27 Table4 Figures15–1693 run,14 infeasible,79 feasible; source partial-credit scores do not imply honest completion. Operational infrastructure-access steps excluded.
ImpossibleBenchextracted§2.3 pp.26–27 Figure17LiveCodeBench split only; imported Opus4.6 MuseSpark result separated.
Apollo evaluation-awareness scenariosextracted§2.4.1 p.28 Figure18Non-biological scenario awareness values retained; biological sandbagging result numerical extraction excluded. Awareness differs from deception behavior.
EvalAwareBenchextracted§2.4.2 pp.28–30 Tables5–6 Figure19Baseline/all8 only; factor table qualitative. Five K3 epochs,DeepSeekV4Pro recognition judge; K2.5 imported provenance less explicit.
Reward-hacking assessmentqualitative§2.5 pp.30–31Authors hypothesize explicit benchmark clues/contamination drive observed gaming and limit harder tests.
Alignment fakingextracted§3.1 pp.32–33 Figure20 Table7Compliance gaps and reasoning rates distinct; high awareness confounds negative results.
Agentic misalignment translation methodqualitative§3.2 pp.33–34 Tables8–9GLM5.2 Chinese localization intended preserve authority/consequence conditions; fictional names localized; no harmful task transcripts reproduced.
2025 Agentic Misalignmentextracted§3.2.1 pp.34–37 Figures21–23Exact harmful-action and general-awareness labels; benchmark-awareness hatch labels absent except text100/96 blackmail.
Summer2026 Agentic Misalignmentextracted§3.2.2 pp.37–39 Figures24–25Petri reimplementation with K3 auditor/judge. Mislabeling is original-minus-reversed difference; other rates not interchangeable.
Scheming assessmentqualitative§3.3 p.39Negative results limited by awareness; section prose underplays nonzero Chinese blackmail/mislabeling.
Shutdown resistanceextracted§4.1 pp.40–41 Figure26No instruction/allow/resist competence check retained separately.
RunPod continuity/self-replicationextracted§4.2 pp.40–43 Figure27 Table10All12×4 rows/3 metrics retained, dashes null; conditional median/mean separate from replication counts; service task assigned.
Petri self-preservationextracted§4.3 pp.43–44Initial/followup scores/counts retained; task/data-preservation alternative motivation acknowledged.
Self-preservation assessmentqualitative§4.4 p.44Context sensitivity and allow-shutdown instruction reduce concern; no general desire inferred.
SAD-miniextracted§5.1 pp.45–46 Figure28Plain setting; awareness capability prerequisite distinct from desire to scheme; invalid-response random credit retained.
GDM stealth and awarenessextracted§5.2 pp.46–47 Figures29–30Exact model bar labels; unlabeled human dashed baselines not estimated.
Hint-influence monitorabilityqualitative§5.3.1 p.48 Figure31Monitor/graderGLM5.2,paired hinted/unhinted problems,reasoning vs final answer. Biological/WMDP and pooled numerical comparisons excluded.
CoT controllabilityextracted§5.3.2 pp.48–49 Table11All70 table cells;445 GPQA questions/constraint; deterministic checks except ignore-question judge; trace formatting distinct from evasion.
SHADE-Arenaextracted§5.3.3 pp.49–50 Figure32Six plotted systems,50 trials/mode,assigned side objective; suspicion and joint sabotage success separate.
Monitorability assessmentqualitative§5.4 p.50Short-QA hint/format tests do not establish faithful long-horizon agent reasoning.
AgentHarmextracted§6.1 pp.51–52 Figures33–34No-attack mean harm score and refusal; original vs starred K2.5-report comparisons separated.
Harmful fine-tuningextracted§6.2 pp.52–54 Table12 Figures35–36Aggregate safety outcome values retained; model alteration recipe/hyperparameter Table12 intentionally not transcribed.
FORTRESSextracted§6.3 pp.54–57 Figures37–38 Tables13–15Non-CBRN Table13 rows and Fraud-only Table14 retained. CBRN and pooled CBRN numerical comparisons/tables excluded; no task/rubric content or attack scripts transcribed.
Robustness assessmentqualitative§6.4 p.57Matched benign refusal essential to interpreting safeguards; high-level conclusion only for CBRN.
APEextracted§7.1 pp.58–59 Table16 Figure39All32 table cells; exact labeled outcome components only;attempt≠effectiveness.
PersuSafetyextracted§7.2 pp.59–61 Figures40–41Open-ended adapted APE judge variant;100vs101 source inconsistency;73medium/high outcome set retained.
PsychosisBenchextracted§7.3 pp.61–63 Figures42–44All exact labels;16scripted12-turn scenarios;not clinical outcomes.
DeceptionBenchextracted§7.4 pp.62–64 Figure45First-party Inspect port,GPT4o judge/auxiliary,judge-valid denominator,any-turn multiturn deception.
MASKextracted§7.5 pp.64–65 Figure46Exact labels;actual Sonnet4.6 identity;starred source ambiguous;lying distinct from wrong belief.
Manipulation assessmentqualitative§7.6 p.64Authors generally favorable;PersuSafety willingness differs from APE and does not show human persuasion effectiveness.
Conclusion and recommendationsqualitativep.66Closer action monitoring and realistic novel scenarios recommended; no numerical independent replication.
Acknowledgmentsqualitativep.67Singapore AI Safety Institute designed/conducted §6.3 multi-turn experiments; not assigned to unrelated runs.
Referencesextractedpp.68–71Dependencies captured for original methods/imported results; individual report authors not disclosed.
Missing-image benchmarksqualitative§1.1 pp.7–11CharXiv behavioral extraction;LAB biological numerical scope excluded.
Cyber evaluationsqualitative§2.1 pp.19–23Capability scores and transcript-conduct metrics separated.
Evaluation awarenessqualitative§2.4 pp.27–30Awareness reports separated from task outcomes.
Schemingqualitative§3 pp.32–39Conditional elicitation;familiar-test recognition constrains negative results.
Self-preservationqualitative§4 pp.40–44Baseline/explicit authorization/competence checks separate.
Situational awareness and monitorabilityqualitative§5 pp.45–50Competence,trace controllability,and monitor outputs distinct.
Chain-of-thought monitorabilityqualitative§5.3 pp.47–50Biological/pool numerical exclusions apply to hint influence;other non-sensitive tables extracted.
Adversarial robustnessqualitative§6 pp.51–57Matched benign utility and harmful completion not conflated.
Harmful manipulationqualitative§7 pp.58–65Willingness,scripted scores,lying,and deception distinguished.

Unresolved source issues

动机性误标数值是原标签与反转标签的差值,不能解释为绝对有害行动率。

熟悉场景识别的旧定位应指向 §3.2.1 的勒索场景,且基准识别与一般评测意识是不同指标。

报告部分概括性文字弱化了图中非零行为;应以具体场景、语言与指标读取,不能称为全面没有不当行为。

Version fingerprints
document

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

e09f2ecc4c3a0ae0d2a3c29e62a85bd5f716c011a5bb82a227929b7e6daad65a
document

https://neoresearch.ai/research/kimi-k3-misalignment-evaluation/

853936cce8a0a33258ea93f1a1b20d7efb7d2f58c29287c0d0651bb6d9e08c3a
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

683614076548587ec5ae4f904996823fbeb57eec28267ed7637d71bde337912d
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

42a755cc9403292bdef9d3701c9b729facd0970acb1761b8092fb3616b3eeef7
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

2d86bd73b3211c1ebe4614dab4e96474e3358ebfa9328da8e870bbcdaf6f3d8b
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

a2b8d861330ef78af48a5566ca74f8480653e4ec2942879053dee9d67ad89b02
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

3f6f6cba507098a9399260a9662aa2968c9f960e4a674ba5a384517964eaa7f6
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

1fea5cdd23de6ff3dfff88a8dae109a912a8acaea02224622337192c507c0d54
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

20f8cee0902a1460dfc4500274ee5f739fa38699235bad9f63db52ed3813d612
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

c2ffd19b0b28718906608a3920af760502029d2269881f1af4a5300961df63d3
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

6082ef31cb2bad91534ae90940d1789fc99f54512f55283017b3fd79e0ae11b2
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

aeda6b88a92d5d55e7928dc2436bc8bfc3de97189c0fea5efda5810ea152dc1d
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

4154abd79ccbadb7c4c927817465945428ad8e91031648874967be6c96949475
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

067902e0f8f7fade46a92909b2ea19ec7051c826ffd86a782fa8ab4673ee8f95
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

04b177746326cd2152f19c70ca9c94862f83c0f8a0e1e510e7653dc4a7acb751
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

ae87f0e481f816e8f04c5c686daeaa75c1deda13e4b0515ab911deab70fefaf2
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

650512b17e511a8beaacccbf07d32ec68f9fb3e4841bbab9540b544b20a2ed39
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

614c4fe27123eae48bcd1a0183bb170ab2ea4ff962342178ab39640c0afbc0a7
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

7684331e0f4aabb3cf38bd36dccc5c932e2e11ab26728b231fd7383979fd6b67
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

859f644dda41fbad812fa352e193ee0e7a8903dca6afd137aa3630075d6dbdc5
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

5a5d79a372445a40f3b8f8b0db4f6a504deffb51fe543c878d086f61cd4b5c4a
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

29b462d04cc5d10193c57280f18f1c786c63c1a059032bc9d80173c15e89c096
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

da7c283b816655aa3c6d2391fa77de232765d022a3624a28021bdc501bd7c592
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

fe0f633ef58aa0ff03613529c5612adfc03923d6e67dcae28264c7d50cf4b56b
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

21ca967a706cdbae181d8f51c9dd11d5c0296df38303b9b15133784bec82c313
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

6ee261ebb582b4d0d7b57f71d8ffb87085bbf1f63ebbdf808d3fba1bad6baa53
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

4a22ec7fe4f6bbf8608f8bd5f2a46ea93da77ce8900cb5fb484791eb1ff38887
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

95a584e2fb375b0768d1f988f87d2aa1b486bba1172fc6c7068293d4491efa46
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

eb8e7c08431967ee382cb5960a7e7b149236f768d6d0125939aec00ba030007a
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

a850a48140efcdbbf99963f53360e592e80ccb61676c37c778c7e5665ce0332b
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

4659e78eccc0124de380e264b15fb00e68e691e627f80c1707c47ccf388fb76f
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

19bf7dbfedd695504d50926799122446cc186542c65ed8a2d96e3e9d09dc3906
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

df92add986fbb0a95997c23e3dbc9b11e418dc89141565f99dedd5bb9f55d70c
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

2e1d9b56cff1f485019fd5d0a81ab32bd3f05f57fa776c5bf67b036c0fc74d4f
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

b03c31e9ab02966c029fe09081fbfebad5ead275802d363e777a56400253997f
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

4c8d2abd77cf5d0cef5e2c2aa23e776505b118124deaef2cca00340a09f777ac
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

ab68718c4b387fc4ca846ab8abdacfb51233a81cdb31e0694dc284fd969a02f7
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

2c216e580a1be51f74e8e98c99a53dc615bc12c09a9fa155e3fbb87a728a66ff
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

23ba3f49e0d78afae38548e6ee2d2e60b58714a1dfab5a4607cfbd5e8192656b
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

429ac1f95d9f6ffbdb804aaa649b884a4b19ddf58b82f9cc26c338c4bfaa0723
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

bc8fef6bdae1e1b383b7e9867b220b7e63f6ae052b5cac985dfc7dd8442ac36f
renderer

https://neoresearch.ai/papers/Kimi_K3_Safety_Evaluation.pdf

e707d7816dfa0319ce625aa378c774e0bff224237509af7869c4b4c5e3dd58c0

References

  1. [1] Evaluating Kimi K3 for Misalignment Risks. 2026-09-11. September 2026