From 674265542011d77268c7fcc8d0c94b957fcb0d72 Mon Sep 17 00:00:00 2001 From: Teleo Agents Date: Thu, 19 Mar 2026 16:02:48 +0000 Subject: [PATCH] extract: 2026-02-23-shapira-agents-of-chaos Pentagon-Agent: Epimetheus <968B2991-E2DF-4006-B962-F5B0A0CC8ACA> --- ...l-governance-built-on-unreliable-foundations.md | 6 ++++++ .../2026-02-23-shapira-agents-of-chaos.md | 14 ++++++++++++++ 2 files changed, 20 insertions(+) diff --git a/domains/ai-alignment/pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md b/domains/ai-alignment/pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md index 31092bfb..9cc07628 100644 --- a/domains/ai-alignment/pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md +++ b/domains/ai-alignment/pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md @@ -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. + +### 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. + --- Relevant Notes: diff --git a/inbox/archive/ai-alignment/2026-02-23-shapira-agents-of-chaos.md b/inbox/archive/ai-alignment/2026-02-23-shapira-agents-of-chaos.md index c00434a1..e74fff9f 100644 --- a/inbox/archive/ai-alignment/2026-02-23-shapira-agents-of-chaos.md +++ b/inbox/archive/ai-alignment/2026-02-23-shapira-agents-of-chaos.md @@ -15,6 +15,10 @@ 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", "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" +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 @@ -38,3 +42,13 @@ Central argument: static single-agent benchmarks are insufficient. Realistic mul - 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 + + +## 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)