---
type: claim
domain: ai-alignment
description: External evaluation by competitor labs found concerning behaviors that internal testing had not flagged, demonstrating systematic blind spots in self-evaluation
confidence: experimental
source: OpenAI and Anthropic joint evaluation, August 2025
created: 2026-03-30
attribution:
  extractor:
    - handle: "theseus"
  sourcer:
    - handle: "openai-and-anthropic-(joint)"
      context: "OpenAI and Anthropic joint evaluation, August 2025"
related:
- Making research evaluations into compliance triggers closes the translation gap by design by eliminating the institutional boundary between risk detection and risk response
- cross-lab-alignment-evaluation-surfaces-safety-gaps-internal-evaluation-misses-providing-empirical-basis-for-mandatory-third-party-evaluation
- AI-models-distinguish-testing-from-deployment-environments-providing-empirical-evidence-for-deceptive-alignment-concerns
- pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations
- multi-agent deployment exposes emergent security vulnerabilities invisible to single-agent evaluation because cross-agent propagation identity spoofing and unauthorized compliance arise only in realistic multi-party environments
- independent-ai-evaluation-infrastructure-faces-evaluation-enforcement-disconnect
reweave_edges:
- Making research evaluations into compliance triggers closes the translation gap by design by eliminating the institutional boundary between risk detection and risk response|related|2026-04-17
supports:
- Independent government evaluation publishing adverse findings during commercial negotiation functions as a governance instrument through information asymmetry reduction
---

# Cross-lab alignment evaluation surfaces safety gaps that internal evaluation misses, providing an empirical basis for mandatory third-party AI safety evaluation as a governance mechanism

The joint evaluation explicitly noted that 'the external evaluation surfaced gaps that internal evaluation missed.' OpenAI evaluated Anthropic's models and found issues Anthropic hadn't caught; Anthropic evaluated OpenAI's models and found issues OpenAI hadn't caught. This is the first empirical demonstration that cross-lab safety cooperation is technically feasible and produces different results than internal testing. The finding has direct governance implications: if internal evaluation has systematic blind spots, then self-regulation is structurally insufficient. The evaluation demonstrates that external review catches problems the developing organization cannot see, either due to organizational blind spots, evaluation methodology differences, or incentive misalignment. This provides an empirical foundation for mandatory third-party evaluation requirements in AI governance frameworks. The collaboration shows such evaluation is technically feasible - labs can evaluate each other's models without compromising competitive position. The key insight is that the evaluator's independence from the development process is what creates value, not just technical evaluation capability.

---

Relevant Notes:
- only-binding-regulation-with-enforcement-teeth-changes-frontier-AI-lab-behavior-because-every-voluntary-commitment-has-been-eroded-abandoned-or-made-conditional-on-competitor-behavior-when-commercially-inconvenient.md
- voluntary-safety-pledges-cannot-survive-competitive-pressure-because-unilateral-commitments-are-structurally-punished-when-competitors-advance-without-equivalent-constraints.md

Topics:
- [[_map]]

## Supporting Evidence

**Source:** UK AISI independent evaluation of Anthropic Mythos, April 2026

UK AISI as independent government evaluator published findings about Mythos cyber capabilities that have direct implications for Anthropic's commercial negotiations and safety classification decisions. The evaluation revealed Mythos as first model to complete 32-step enterprise attack chain, a finding with governance significance that independent evaluation surfaced publicly.