Web agents powered by large language models (LLMs) must process lengthy web
page observations to complete user goals; these pages often exceed tens of
thousands of tokens. This saturates context limits and increases computational
cost processing; moreover, processing full pages exposes agents to security
risks such as prompt injection. Existing pruning strategies either discard
relevant content or retain irrelevant context, leading to suboptimal action
prediction. We introduce FocusAgent, a simple yet effective approach that
leverages a lightweight LLM retriever to extract the most relevant lines from
accessibility tree (AxTree) observations, guided by task goals. By pruning
noisy and irrelevant content, FocusAgent enables efficient reasoning while
reducing vulnerability to injection attacks. Experiments on WorkArena and
WebArena benchmarks show that FocusAgent matches the performance of strong
baselines, while reducing observation size by over 50%. Furthermore, a variant
of FocusAgent significantly reduces the success rate of prompt-injection
attacks, including banner and pop-up attacks, while maintaining task success
performance in attack-free settings. Our results highlight that targeted
LLM-based retrieval is a practical and robust strategy for building web agents
that are efficient, effective, and secure.