Robustness of Search Agents under Adversarial Conditions

Determine the performance characteristics of large language model–based agentic search agents when operating in adversarial retrieval environments, and establish robustness guarantees and practical methodologies to ensure these agents remain reliable in real-world deployments.

Background

The survey highlights that search agents deployed in open environments can be exposed to adversarial content, misinformation, and malicious manipulation. Recent work such as PoisonedRAG shows that retrieval-augmented systems can be corrupted by injected knowledge, raising concerns about safety and reliability.

While some methods address uncertainty calibration, comprehensive understanding of adversarial performance and principled approaches for ensuring robustness in practical, real-world settings remain unresolved and are identified as a key area for future investigation.