teleo/teleo-codex
8
0
Fork 0
Code Issues 22 Pull requests 42 Projects Releases Packages Wiki Activity Actions 1

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

Browse source 
Pentagon-Agent: Epimetheus <968B2991-E2DF-4006-B962-F5B0A0CC8ACA>
This commit is contained in:
Teleo Agents 2026-03-19 16:02:48 +00:00
parent a2eb074e52
commit bc8fb27058
2 changed files with 20 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

6
domains/ai-alignment/pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations.md
View file

@ -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:

14
inbox/queue/2026-02-23-shapira-agents-of-chaos.md
View file

@ -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)