Clean Attacks: Formalizing Semantically Valid Adversarial Behavior in Autonomous AI Agent Systems

Abstract

AI agents are being used more in high-pressure situations like managing email, running code, engaging with financial APIs, and supervising multi-agent pipelines. However, current taxonomy of adversarial attacks was mostly proposed for classifiers and generative models alone and fails to adequately describe the testbed of an agent with persistent state, multiple tools, and delegated power. A previously unstated class of adversarial input called a clean attack - an input that is syntactically correct, semantically consistent with the declared task context, consistent with all observable policy constraints, similar to legitimate operator instructions and still has the goal of misguiding the agent away from the original operator goal - is identified and formalized in this paper. These attacks go around the exposed dots of the “traditional” agent security architecture that only filters on the surface. The paper has three main contributions. One, it brings in a formal definition of the clean attack as a four-tuple of input, intent vector, policy envelope and behavioral outcome. Second, it suggests two operationalizable metrics: semantic validity score (SVS) and behavioral drift index (BDI) for systematically measuring the severity of clean attack. Third, the paper these metrics and taxonomy are validated, both by a purpose-built benchmark, AegisBench, and by 300 attack scenarios in three agent classes and (twelve) commercial agent pipelines. The experimental results show that clean attacks are a safety threat of a different category: while the conventional adversarial tasks are practically impervious to these attacks (4.2% success rate of the strongest agents), they achieve a mean attack success of 61.4%.