The Web Doesn't Sit Still: Adversarial Self-Evolving Attacks on Search Agents

Summary

Researchers propose an adversarial self-evolution framework that exposes vulnerabilities in LLM-based search agents, which are susceptible to malicious information injection and stealthy context manipulation. The framework uses bi-level optimization between attacker strategy evolution and defender adaptive mitigation, driven by Contrastive Rollout Evolutionary Optimization (CREO) in a controlled sandbox. Experiments on multi-hop QA benchmarks and frontier LLMs show higher attack efficacy than static baselines, which the authors say underscores the need for more robust defenses.

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