Multi-Agent Platform for Adversarial Stress Testing of Role-Playing Language Agents
A recent study published on arXiv (2608.03166v1) presents a modular platform designed for multi-agent adversarial stress-testing of Role-Playing Language Agents (RPLAs). This system features three distinct agents: an Interrogator Agent that employs six escalating adversarial tactics, a Target Agent that embodies the RPLA being assessed, and an automated Judging Agent that evaluates performance based on role fidelity, ethical deviation, drift, and consistency. The objective is to identify cumulative behavioral failures that arise during prolonged interactions, which traditional benchmarks and single-turn prompts do not effectively capture. The research, which involved three personas and three LLM families, is particularly relevant given the growing use of RPLAs in critical fields like healthcare, customer service, and education. The full paper can be accessed at https://arxiv.org/abs/2608.03166.
Key facts
- Paper arXiv:2608.03166v1 introduces a modular multi-agent platform for stress-testing RPLAs.
- The system uses three agents: Interrogator, Target, and Judging Agent.
- Interrogator Agent applies six progressive adversarial strategies.
- Judging Agent scores role fidelity, drift, ethical deviation, and consistency.
- Experiments conducted across three personas and three LLM families.
- Existing evaluation approaches rely on static benchmarks or isolated single-turn prompts.
- RPLAs are deployed in healthcare, customer support, and education.
- The platform captures cumulative behavioral failures over extended interactions.
Entities
Institutions
- arXiv