SIEVE: Defending Autonomous LLM Agents Against Indirect Prompt Injection
As LLM agents transition from text generation to autonomous tool execution, they face heightened risks from Indirect Prompt Injection (IPI), where malicious external data manipulates agent reasoning to execute unauthorized actions. Current defenses are either too rigid (rule-based) or computationally expensive (constant semantic auditing). Researchers from Emory University have developed SIEVE, a hybrid defense framework that utilizes an "Intent Graph" for deterministic verification of tool transitions and argument sources. By escalating only ambiguous or non-deterministic actions to a high-level Semantic Adjudication Module, SIEVE significantly reduces Attack Success Rate (ASR) across AgentLure and AgentDojo benchmarks while maintaining high operational utility and minimizing token overhead compared to state-of-the-art baselines like DRIFT and ARGUS.