The Clean Attack Problem: When Nothing Looks Wrong, but Everything Is Compromised
AI agents are being used more in high-pressure situations such as managing email, running code, interacting with financial APIs, and supervising multi-agent pipelines. However, the 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 - a syntactically correct input, 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 exploit the exposed dots in the “traditional” agent security architecture, which only filters at the surface. Reference research paper on experiment published in: https://ijsrm.net/index.php/ijsrm/article/view/6755
Toward a New Security Paradigm for AI-Native Systems: Most future...
Copyright of this story solely belongs to hackernoon.com. To see the full text click HERE
