As large language model (LLM) agents take on more complex tasks with greater autonomy, security has become a major obstacle to their broader deployment. Unlike in attacks that require complex exploitation of operating system vulnerabilities, attackers can easily hijack agents through indirect prompt injection (IPI), leading to privacy leakage, financial loss, and destructive operations. However, existing defenses often make security decisions based on limited static trusted evidence or assess observations in isolation. Such rigid trust assumptions can cause task-essential information to be unnecessarily discarded, resulting in over-defense and degraded agent functionality. To address this problem, we propose ATrust, a defense mechanism that adaptively expands the trust boundary during agent runtime. Starting from the user query as the root trust anchor, ATrust evaluates external observations against the current trust boundary and execution state, progressively admitting task-essential information as new trust anchors. This preserves necessary environmental feedback while excluding irrelevant content, whether benign or malicious, from subsequent decision-making, thereby limiting unnecessary exposure to external environment. We evaluate ATrust on AgentDojo and AgentDyn, where it achieves the strongest overall security--utility trade-off among existing defenses. With GPT-4o as the engine model, ATrust reduces the attack success rate on AgentDyn from 37.80% to 4.37% while enhancing utility by 10.00%, outperforming AuthGraph, the state-of-the-art defense, by 12.33%. These results demonstrate the effectiveness and practicality of ATrust.

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