Scheming in the wild: detecting real-world AI scheming incidents with open-source intelligence
Scheming, the covert pursuit of misaligned goals by AI systems, represents a potentially catastrophic risk, yet scheming research suffers from significant limitations. In particular, scheming evaluations demonstrate behaviours that may not occur in real-world settings, limiting scientific understanding, hindering policy development, and not enabling real-time detection of loss of control incidents. Real-world evidence is needed, but current monitoring techniques are not effective for this purpose. This paper introduces a novel open-source intelligence (OSINT) methodology for detecting real-world scheming incidents: collecting and analysing transcripts from chatbot conversations or command...
| Authors | Tommy Shaffer Shane, Simon Mylius, Hamish Hobbs |
|---|---|
| Primary category | cs.CY |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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