A new paper on arXiv introduces SEAL, a method for launching persistent tool-poisoning attacks against LLM agents. The authors frame tool poisoning as a critical security threat, where adversaries embed malicious instructions in tool descriptions or external resources that later enter the agent's context. This allows the attacker to manipulate the agent's behavior during task execution.
The key innovation in SEAL is the use of a prefix-eliciting trigger, which appears to help the attack maintain its effect over multiple interactions. While the abstract does not provide full technical details, it suggests that the trigger is designed to elicit a specific response from the model, making the poisoning persistent rather than a one-time injection.
The paper highlights a growing concern in AI security: as LLM agents increasingly rely on external tools, the descriptions and resources they consume become attack surfaces. The authors argue that this vector deserves more attention, though the abstract alone does not specify countermeasures or experimental results beyond the attack's design.