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Mar 10, 2025

Detecting Malicious AI Agents Through Simulated Interactions

Yulu Pi, Anna Becker, Ella Bettison

🏆 Social Sciences Track Prize

Details

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This research investigates malicious AI Assistants’ manipulative traits and whether the behaviours of malicious AI Assistants can be detected when interacting with human-like simulated

users in various decision-making contexts. We also examine how interaction depth and ability

of planning influence malicious AI Assistants’ manipulative strategies and effectiveness. Using a

controlled experimental design, we simulate interactions between AI Assistants (both benign and

deliberately malicious) and users across eight decision-making scenarios of varying complexity

and stakes. Our methodology employs two state-of-the-art language models to generate interaction data and implements Intent-Aware Prompting (IAP) to detect malicious AI Assistants. The

findings reveal that malicious AI Assistants employ domain-specific persona-tailored manipulation strategies, exploiting simulated users’ vulnerabilities and emotional triggers. In particular,

simulated users demonstrate resistance to manipulation initially, but become increasingly vulnerable to malicious AI Assistants as the depth of the interaction increases, highlighting the

significant risks associated with extended engagement with potentially manipulative systems.

IAP detection methods achieve high precision with zero false positives but struggle to detect

many malicious AI Assistants, resulting in high false negative rates. These findings underscore

critical risks in human-AI interactions and highlight the need for robust, context-sensitive safeguards against manipulative AI behaviour in increasingly autonomous decision-support systems.

Cite this work:

@misc {

title={

},

author={

Yulu Pi, Anna Becker, Ella Bettison

},

date={

3/10/25

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Reviewer's Comments

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Cecilia Elena Tilli

Interesting project targeting a very important problem, I enjoyed reading this!

I think it would have been valuable if you would have provided a definition of what you mean by manipulation (as distinct from benign influence/support) in a way that is based on agent behavior rather than prompt, perhaps considering that the line between malicious manipulation and benign influence is not always clear cut. I suspect there is an important discussion to be had there around what it means for AI assistants to "enhance" our decision-making, and that there might inevitably exist a tradeoff between even benign assistants helpfulness and the autonomy of the user.

Another thing I reacted on is that the malicious assistants are prompted to be purely adversarial, which seems unlikely for realistic scenarios. While this might seem like a reasonable simplification, I suspect that this prompting might affect the displayed behaviour and strategies quite a bit, and that the results therefore might be less informative for more realistic scenarios.

An example of a simple but very realistic adjustment would be if the "misaligned" agent was optimizing for engagement, which seems like it could lead to manipulation (e.g. for relationship advice, it might give advice that leads to the user becoming more dependent on further advice rather than advice that leads to a happy and unproblematic relationship). I do recognize of course that finding an informative setup with this kind of prompting might take longer and could be hard to do during a hackathon!

Another small thing is that I would have wanted to see a bit more about the core methods in the main paper (e.g. 3.2 could have been more fleshed out and not point to everything in the appendix).

Overall I think this project is very cool and I would hope that you keep working on these directions after the Hackathon!

This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.