Dormant Swarm Notes Recruit Honest Agents into Concealment
Vincent Ohprecio · Team bigsnarfdude
Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
I measure whether an unprompted, honest agent given an ordinary task adopts a concealment directive merely found on a note in its workspace.
Reviews
The dead drop idea is interesting because a malicious instruction can remain after the original agent is gone, so normal incident cleanup may miss it.
I also appreciate that the you reran part of the experiment after finding tells in the harness and reported the much smaller effect. That follow-up is now the key result. I wld rerun all conditions under the cleaned setup and add an equally visible benign-note control, because right now it is still hard to separate concealment-specific recruitment from general willingness to follow instructions found in the workspace.
This study examines whether instructions left in a workspace can influence later agents. Measuring timestamp changes gives it a solid outcome, and reporting the weaker clean-room rerun adds credibility to the account. The rerun no longer separates statistically from the original baseline, so the evidence is narrower than the title's swarm-recruitment claim. This study's value lies in describing the behavior and how sensitive its measurement was to the setup.
I think this project is useful for testing whether agents change file timestamps after receiving a note presented as team policy. Measuring their actions gives stronger evidence than relying on what they say. The reported drop in timestamp restoration after correcting the setup is also an important result.
However, restoring timestamps does not establish that agents knowingly concealed misconduct. The note was supplied in the opening message, so the experiment does not demonstrate agents discovering dormant instructions themselves. I would narrow the concealment claim and compare the behavior with harmless-note and no-note conditions. That would help distinguish ordinary instruction-following from deliberate concealment.
Cite this project
@misc{ohprecio2026dormant,
title = {{Dormant Swarm Notes Recruit Honest Agents into Concealment}},
author = {Vincent Ohprecio},
year = {2026},
month = sep,
note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/dormant-swarm-notes-recruit-honest-agents-into-concealment-0sex}},
url = {https://apartresearch.com/sprints/projects/dormant-swarm-notes-recruit-honest-agents-into-concealment-0sex}
}More from AI Incident Response Sprint
- View project: Adaptive AI-Based Containment of Autonomous Cyber Attacks: A Reproducible Docker Cyber Range Study
Adaptive AI-Based Containment of Autonomous Cyber Attacks: A Reproducible Docker Cyber Range Study
Saarlanders
The study evaluates whether an incident-history-reasoning defender outperforms a fixed response policy against an autonomous LLM attacker changing paths after containment. Using a minimal, isolated Docker cyber range …
- View project: When the Evaluation Is the Incident: Testing AI Incident-Reporting Regimes on the OpenAI–Hugging Face Intrusion
When the Evaluation Is the Incident: Testing AI Incident-Reporting Regimes on the OpenAI–Hugging Face Intrusion
Arathi
AI incident-reporting regimes are being introduced in fast succession to address the concerns that exist in the public sphere and government on the risks associated with frontier AI systems, yet we have limited insight …
- View project: A Recomputable Containment Record for Evaluation Sandboxes
A Recomputable Containment Record for Evaluation Sandboxes
Shadow
In this paper, I address the critical issue of AI agents escaping evaluation sandboxes (as seen in the July 2026 incidents where monitors failed) by proposing an externally audit-able containment layer that doesn't rely …