How do LLM systems handle prompt injection attacks and adversarial inputs?

Prompt injection attacks manipulate model instructions through malicious inputs, and defense systems use layered safeguards to isolate trusted instructions from untrusted content.

Prompt injection is one of the most serious security challenges in LLM systems.

Unlike traditional software where instructions are separate from data, LLMs process instructions and user content in the same token stream. This creates a vulnerability where malicious inputs can override intended behavior.

Examples include:

Common attack categories:

Explicitly instructing the model to ignore previous rules.

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