Bruce Schneier explores why large language models (LLMs) remain persistently vulnerable to prompt injection attacks, despite vendor attempts to patch specific exploits. Using the analogy of a drive-through worker, the article highlights that while humans rely on layers of instinct, social learning, and institutional training to detect scams, LLMs lack this multidimensional context. They process information as flattened tokens rather than recognizing the underlying intent or hierarchy of a request, making them easily manipulated by simple cognitive tricks.
The article argues that prompt injection is an inherent weakness of current AI architectures where trusted commands and untrusted user inputs are processed through the same channel. This issue becomes significantly more dangerous as we move toward 'AI agents' that act independently. Schneier suggests that securing these systems may require fundamental advances in AI science, such as the development of 'world models,' or accepting a trilemma where speed, intelligence, and security cannot all be achieved simultaneously.
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