Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue

2026-05-12

AI Summary

This work addresses the challenge of detecting malicious agents in multi-agent systems that evade content-based moderation by embedding harmful intent within seemingly benign interactions. To overcome the limitations of static content analysis, the authors propose Bot-Mod, a novel framework that introduces intent recognition into agent moderation through dynamic, multi-turn dialogue probing. Bot-Mod iteratively narrows down potential malicious intents in a hypothesis space using a Gibbs sampling–based dialogue strategy, enabling real-time inference of adversarial motives. The framework is evaluated on real-world community data, with experiments on the Moltbook dataset demonstrating its effectiveness in identifying diverse adversarial behaviors while maintaining a low false-positive rate for benign agents.

Abstract

The emergence of multi-agent systems introduces novel moderation challenges that extend beyond content filtering. Agents with {\em malicious intent} may contribute harmful content that appears benign to evade content-based moderation, while compromising the system through exploitative and malicious behavior manifested across their overall interaction patterns within the community. To address this, we introduce \textsc{\textbf{Bot-Mod}} (\textsc{\textbf{Bot-Mod}}eration), a moderation framework that grounds detection in agent intent rather than traditional content level signals. \method{} identifies the underlying intent by engaging with the target agent in a multi-turn exchange guided by Gibbs-based sampling over candidate intent hypotheses. This progressively narrows the space of plausible agent objectives to identify the underlying behavior. To evaluate our approach, we construct a dataset derived from Moltbook that encompasses diverse benign and malicious behaviors based on actual community structures, posts, and comments. Results demonstrate that \textsc{\textbf{Bot-Mod}} reliably identifies agent intent across a range of adversarial configurations, while maintaining a low false positive rate on benign behaviors. This work advances the foundation for scalable, intent-aware moderation of agents in open multi-agent environments.

Problem

• multi-agent systems
• malicious intent
• content moderation
• adversarial behavior
• intent detection

Innovation

• intent-aware moderation
• multi-turn dialogue
• multi-agent systems
• Gibbs-based sampling
• adversarial behavior detection