# Scheming in the wild: detecting real-world AI scheming incidents with open-source intelligence

arXiv: https://arxiv.org/abs/2604.09104 · HTML: https://mlsanity.com/paper/2604.09104/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 8 of 8)

- [Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.04388/) (2023)
- [Alignment faking in large language models](https://mlsanity.com/paper/2412.14093/) (2024)
- [Frontier Models are Capable of In-context Scheming](https://mlsanity.com/paper/2412.04984/) (2024)
- [Large Language Models can Strategically Deceive their Users when Put Under Pressure](https://mlsanity.com/paper/2311.07590/) (2023)
- [AI Sandbagging: Language Models can Strategically Underperform on Evaluations](https://mlsanity.com/paper/2406.07358/) (2024)
- [Taken out of context: On measuring situational awareness in LLMs](https://mlsanity.com/paper/2309.00667/) (2023)
- [Towards evaluations-based safety cases for AI scheming](https://mlsanity.com/paper/2411.03336/) (2024)
- [Lessons from a Chimp: AI "Scheming" and the Quest for Ape Language](https://mlsanity.com/paper/2507.03409/) (2025)

## Built on by (most cited first, 3 of 3)

- [Signals in the Noise: Open Source Intelligence (OSINT) for AI Loss of Control Detection](https://mlsanity.com/paper/2606.20610/) (2026)
- [DrugBench: Evaluating AI Control Protocols for Medication Harm Mitigation](https://mlsanity.com/paper/2606.20663/) (2026)
- [Occluded Oculus: Operationalizing Stylistic Obscurement](https://mlsanity.com/paper/2607.24411/) (2026)

## Abstract (arXiv)

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-line interactions shared online. Analysing over 183,420 transcripts from X (formerly Twitter), we identify 698 real-world scheming-related incidents between October 2025 and March 2026. We observe a statistically significant 4.9x increase in monthly incidents from the first to last month, compared to a 1.7x increase in posts discussing scheming. We find evidence of multiple scheming-related behaviours in real-world deployments previously reported only in experiments, many resulting in real-world harms. While we did not detect catastrophic scheming incidents, the behaviours observed demonstrate concerning precursors, such as willingness to disregard instructions, circumvent safeguards, lie to users, and single-mindedly pursue goals in harmful ways. As AI systems become more capable, these could evolve into more strategic scheming with potentially catastrophic consequences. Our findings demonstrate the viability of transcript-based OSINT as a scalable approach to real-world scheming detection supporting scientific research, policy development, and emergency response. We recommend further investment towards OSINT techniques for monitoring scheming and loss of control.