DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents

Understand

Large Language Models (LLMs) are increasingly central to agentic systems due to their strong reasoning and planning capabilities.

• By interacting with external environments through predefined tools, these agents can carry out complex user tasks.
• Nonetheless, this interaction also introduces the risk of prompt injection attacks, where malicious inputs from external sources can mislead the agent's behavior, potentially resulting in economic loss, privacy leakage, or system compromise.
• System-level defenses have recently shown promise by enforcing static or predefined policies, but they still face two key challenges: the ability to dynamically update security rules and the need for memory stream isolation.