How do AI risks differ between traditional ML models and LLM based agents?

Traditional machine learning risk historically centered on largely statistical concerns, including model accuracy, bias in training data or outputs, and data drift over time as real world conditions diverged from what the model was originally trained on. These risks, while significant, generally did not involve the model taking autonomous action in the world. A traditional ML model might make an incorrect prediction, but it typically was not the thing directly executing a consequence based on that prediction.

LLM based agents introduce a genuinely new risk category on top of these older concerns. Natural language itself becomes an attack surface through prompt injection, since these systems interpret and act on text in ways that create manipulation opportunities that simply did not exist for models processing structured numerical data. Autonomous multi step action means an agent can chain together a series of decisions and tool calls that individually might each look reasonable, but that collectively produce an outcome nobody anticipated or intended. Emergent behavior across this kind of multi step process is genuinely difficult to predict just from testing a model's output in isolation, since the risk often only becomes visible once you observe the full chain of actions an agent takes in a realistic scenario.

This shift means security testing has needed to evolve substantially, moving from primarily evaluating model output quality and accuracy toward evaluating agent behavior across entire task chains, which is a fundamentally different and more complex testing problem. This gap between evaluating outputs and evaluating full behavioral chains is the specific space Akto's platform is built to address for agentic systems.