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extract: 2026-02-23-shapira-agents-of-chaos

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domains/ai-alignment/pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md
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@ -50,6 +50,12 @@ Agents of Chaos study provides concrete empirical evidence: 11 documented case s
METR and UK AISI evaluations as of March 2026 focus primarily on sabotage risk and cyber capabilities (METR's Claude Opus 4.6 sabotage assessment, AISI's cyber range testing of 7 LLMs). This narrow scope may miss alignment-relevant risks that don't manifest as sabotage or cyber threats. The evaluation infrastructure is optimizing for measurable near-term risks rather than harder-to-operationalize catastrophic scenarios. METR and UK AISI evaluations as of March 2026 focus primarily on sabotage risk and cyber capabilities (METR's Claude Opus 4.6 sabotage assessment, AISI's cyber range testing of 7 LLMs). This narrow scope may miss alignment-relevant risks that don't manifest as sabotage or cyber threats. The evaluation infrastructure is optimizing for measurable near-term risks rather than harder-to-operationalize catastrophic scenarios.
### Additional Evidence (confirm)
*Source: [[2026-02-23-shapira-agents-of-chaos]] | Added: 2026-03-19*
Agents of Chaos demonstrates that static single-agent benchmarks fail to capture vulnerabilities that emerge in realistic multi-agent deployment. The study's central argument is that pre-deployment evaluations are insufficient because they cannot test for cross-agent propagation, identity spoofing, and unauthorized compliance patterns that only manifest in multi-party environments with persistent state.
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Relevant Notes: Relevant Notes:

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@ -15,6 +15,10 @@ processed_by: theseus
processed_date: 2026-03-19 processed_date: 2026-03-19
enrichments_applied: ["pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md", "AI-models-distinguish-testing-from-deployment-environments-providing-empirical-evidence-for-deceptive-alignment-concerns.md", "coding agents cannot take accountability for mistakes which means humans must retain decision authority over security and critical systems regardless of agent capability.md"] enrichments_applied: ["pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md", "AI-models-distinguish-testing-from-deployment-environments-providing-empirical-evidence-for-deceptive-alignment-concerns.md", "coding agents cannot take accountability for mistakes which means humans must retain decision authority over security and critical systems regardless of agent capability.md"]
extraction_model: "anthropic/claude-sonnet-4.5" extraction_model: "anthropic/claude-sonnet-4.5"
processed_by: theseus
processed_date: 2026-03-19
enrichments_applied: ["pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md"]
extraction_model: "anthropic/claude-sonnet-4.5"
--- ---
# Agents of Chaos # Agents of Chaos
@ -38,3 +42,13 @@ Central argument: static single-agent benchmarks are insufficient. Realistic mul
- Study conducted under both benign and adversarial conditions - Study conducted under both benign and adversarial conditions
- Paper authored by 36+ researchers including Natalie Shapira, Chris Wendler, Avery Yen, Gabriele Sarti - Paper authored by 36+ researchers including Natalie Shapira, Chris Wendler, Avery Yen, Gabriele Sarti
- Study funded/supported by ARIA Research Scaling Trust programme - Study funded/supported by ARIA Research Scaling Trust programme
## Key Facts
- Agents of Chaos study involved 20 AI researchers testing autonomous agents over two weeks
- Study documented 11 case studies of agent vulnerabilities
- Test environment included persistent memory, email, Discord, file systems, and shell execution
- Study conducted under both benign and adversarial conditions
- Paper authored by 36+ researchers including Natalie Shapira, Chris Wendler, Avery Yen, Gabriele Sarti
- Study funded/supported by ARIA Research Scaling Trust programme
- Paper published 2026-02-23 on arXiv (2602.20021)