How can I detect rogue robots in a swarm?
Discovering Antagonists in Networks of Systems: Robot Deployment
February 28, 2025
https://arxiv.org/pdf/2502.20125This paper proposes a new method for detecting antagonistic behavior in robot swarms performing a coverage task. It uses a normalizing flow neural network trained on normal swarm behavior to predict the likelihood of a robot's motion given its context (position relative to other robots and the environment). Deviations from expected behavior are flagged as potentially antagonistic. Several antagonist strategies are tested, including subtle and deceptive ones.
Key points for LLM-based multi-agent systems:
- Contextual Anomaly Detection: The paper's focus on context is directly relevant to LLMs, where responses depend heavily on context. This approach could be adapted to detect LLM agents deviating from expected behavior given a conversational context.
- Unsupervised Learning: The use of a normalizing flow trained on normal behavior eliminates the need for labeled anomaly data, a significant challenge in LLM agent development. This approach could be used to detect unexpected LLM behaviors without explicit examples of such behavior.
- Focus on Deceptive Behavior: The paper explicitly addresses detecting subtle, deceptive antagonists. This is crucial for LLM agents, where malicious actors might try to manipulate the system discreetly.
- Data-driven Approach: The data-driven nature of the method aligns well with the current state of LLM development, allowing for adaptation as LLMs evolve and become more complex.
- Potential for Countermeasures: The paper suggests basic countermeasures (like agent exclusion) applicable to LLM-based systems to mitigate the impact of malicious or misbehaving agents.