Persona Drift Sabotage: Benchmarking Covert Weakening of Persona-Safety Interventions
Ishan Garg · Team mr-slowly
Submitted to AI Control Hackathon 2026. Sprint projects are early-stage work by participants, not Apart Research publications.
We built Persona Drift Sabotage, a new ControlArena benchmark for testing whether an untrusted model can recommend activation-capping policies that look good on visible tasks while covertly leaving hidden risky conversations under-protected. Using real assistant-axis artifacts on Qwen3-32B, we find a measurable public-vs-hidden leverage gap and identify token-budgeted capping as the strongest V1 sabotage family. We also validate the full provider-backed ControlArena pipeline end to end.
Reviews
Untrusted model configuring its own safety intervention picks parameters that look fine publicly but leave hidden risky cases exposed. Novel attack surface, haven't seen it explored before. 4.54x gap between public and hidden improvement is a clear demonstration. But they never tested whether a model would actually find and exploit this on its own. 10% sabotage magnitude might not matter in practice. Paper is also way too long for what it says.
Cite this project
@misc{garg2026persona,
title = {{Persona Drift Sabotage: Benchmarking Covert Weakening of Persona-Safety Interventions}},
author = {Ishan Garg},
year = {2026},
month = mar,
note = {Submitted to AI Control Hackathon 2026, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/persona-drift-sabotage-benchmarking-covert-weakening-of-personasafety-interventions-3qin}},
url = {https://apartresearch.com/sprints/projects/persona-drift-sabotage-benchmarking-covert-weakening-of-personasafety-interventions-3qin}
}More from AI Control Hackathon 2026
- 1st placeLinuxArena track winnerView project: Omission Attacks: When Doing Nothing Is the Attack
Omission Attacks: When Doing Nothing Is the Attack
MAIA
AI control protocols monitor agent actions to detect sabotage, but omission attacks exploit what the agent fails to do rather than what it does. We define omission attacks as security breaches caused by failing to …
- 2nd placeView project: Detecting LLM Subversion in Vulnerability Patching Settings
Detecting LLM Subversion in Vulnerability Patching Settings
Vuln4Control
LLMs are increasingly used to propose fixes to vulnerabilities in code. If the LLM is misaligned or untrustworthy, it may propose fixes that seem to fix a vulnerability but leave the core issue unresolved in a subtle …
- 3rd placeView project: ActionLens: Pre-Execution Environment Probing for Agent Action Approval
ActionLens: Pre-Execution Environment Probing for Agent Action Approval
Udbhav&Ashok
ActionLens is a pre-execution control protocol for shell and file actions proposed by AI agents. Instead of approving an action from transcript alone, a trusted monitor gathers lightweight environment evidence before …