How does prompt injection defense architecture protect ChatGPT in tool-augmented systems?

Prompt injection defense uses layered filtering, instruction hierarchy, and tool isolation to prevent malicious prompts from overriding system behavior.

Prompt injection becomes critical in tool-augmented LLM systems where external content (web pages, documents, APIs) is injected into the model context. Attackers can embed instructions that try to override system prompts.

Defense architecture includes strict instruction hierarchy (system > developer > user > external data), input sanitization, tool-output labeling, and context segmentation. Additionally, models are trained via RLHF to ignore malicious instructions in retrieved content.

At system level, tool outputs are treated as untrusted data and never granted execution privileges.…