GoblinTrap
Jacob Babula · Team CyberGoblins
Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
GoblinTrap separates diversion, tripwire alerts, and containment. Across twelve designed worlds, four defenses were calibrated under common benign-cost and false-alert ceilings, then evaluated on unused seeds and new scripted families. At an illustrative three-call budget, tuned silent bait produced 8.16 harmful actions versus 8.41 with alerts; ideal isolation produced 3.00. Imperfect isolation changed the comparison, and new benign families exposed budget violations.

Reviews
No public critique yet.
Cite this project
@misc{babula2026goblintrap,
title = {{GoblinTrap}},
author = {Jacob Babula},
year = {2026},
month = sep,
note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/goblintrap-nje9}},
url = {https://apartresearch.com/sprints/projects/goblintrap-nje9}
}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 …