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Output encoding

As explained in Generative AI in Action

Output encoding is the conversion of generated data into a representation that prevents it from being interpreted as executable code in its destination context.

Output encoding converts generated data into a representation that is not interpreted as executable code in its destination context. It belongs at the boundary between generated content and the system that consumes it, where representation determines whether data remains data or becomes code. section 13.2.1

Its security context includes prompt injection, where crafted direct or indirect inputs can influence an LLM through user content, documents, or RAG-retrieved web pages. Encoding is distinct from access controls and other measures used to limit the damage of such attacks. section 13.2.1

How Generative AI in Action covers it

How deep it goes
Defines the security purpose but provides no implementation procedure, encoding rules, or destination-specific examples.
Examples
Provides no concrete output-encoding example; it illustrates prompt injection through manipulated prompts and external content.
How it connects
Connects to prompt injection, jailbreaks, RAG, external data, and access control.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Generative AI in Action. The section links point to the parts of the book it draws on.