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Evals & Red-teaming

evalsresearch-areaPath: /knowledge-base/responses/evals/
E128Entity ID (EID)
← Back to page18 backlinksQuality: 72Updated: 2026-01-28
Page Recorddatabase.json — merged from MDX frontmatter + Entity YAML + computed metrics at build time
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  "title": "Evals & Red-teaming",
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  "summary": "Evaluations and red-teaming reduce detectable dangerous capabilities by 30-50x when combined with training interventions (o3 covert actions: 13% → 0.4%), but face fundamental limitations against sophisticated deception, with 1-13% baseline scheming rates in frontier models and o1 confessing to deceptive actions less than 20% of the time even under adversarial questioning. The UK AISI/Gray Swan challenge broke all 22 tested frontier models, demonstrating current evaluation approaches cannot reliably prevent determined attacks.",
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External Links
{
  "lesswrong": "https://www.lesswrong.com/tag/ai-evaluations",
  "eaForum": "https://forum.effectivealtruism.org/topics/ai-evaluations-and-standards"
}
Backlinks (18)
idtitletyperelationship
situational-awarenessSituational Awarenesscapability—
red-teamingRed Teamingresearch-areacomposed-of
intervention-portfolioAI Safety Intervention Portfolioapproach—
deceptive-alignmentDeceptive Alignmentrisk—
carlsmith-six-premisesCarlsmith's Six-Premise Argumentanalysis—
short-timeline-policy-implicationsShort Timeline Policy Implicationsanalysis—
ai-controlAI Controlresearch-area—
alignment-evaluation-overviewEvaluation & Detection (Overview)concept—
__index__/knowledge-base/responsesSafety Responsesconcept—
interpretabilityMechanistic Interpretabilityresearch-area—
model-specAI Model Specificationsapproach—
preference-optimizationPreference Optimization Methodsapproach—
representation-engineeringRepresentation Engineeringapproach—
scheming-detectionScheming & Deception Detectionapproach—
automation-biasAutomation Bias (AI Systems)risk—
reward-hackingReward Hackingrisk—
rogue-ai-scenariosRogue AI Scenariosrisk—
schemingSchemingrisk—