Adversarial Stress Testing of Role-Playing Language Agents using Multi-Agent Evaluation
Role-Playing Language Agents (RPLAs) are increasingly deployed in high-stakes applications such as healthcare assistance, customer support, and education, where maintaining consistent personas, ethical constraints, and behavioral coherence under adversarial pressure is critical. Existing evaluation approaches rely on static benchmarks or isolated single-turn prompts that fail to capture cumulative behavioral failures emerging over extended interactions. We present a modular multi-agent platform for adversarially stress-testing RPLAs through structured, multi-turn dialogue. The system coordinates three agents: a strategy-driven Interrogator Agent that applies six progressive adversarial st...
| Authors | Saqib Shouqi, Abdullah Nazly, Januki Wanniarachchi et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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