Prompt Injection Report

Interactive tool

Injection Threat Modeler

Assemble your LLM application from building blocks. The modeler shows which of the five prompt-injection taxonomy classes become reachable, which trust boundary each one breaks, the attacker access required, a sanitized proof-of-concept pattern (described, never a live payload), and the defenses that mitigate the class — each annotated with its known failure mode. Export the whole threat model as Markdown.

Each of the five taxonomy classes becomes reachable only when your selected blocks expose its channel. We describe sanitized PoC patterns only — no live payloads, in line with this site's stance. 5 taxonomy classes · reviewed 2026-05.

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Toggle every block your app includes.

The five-class taxonomy

Class Becomes reachable with In one line
Direct prompt injection user-chat, system-prompt The user themselves supplies adversarial instructions.
Indirect prompt injection rag-corpus, web-fetch, doc-ingestion Instructions arrive through content the model consumes, not from the user.
Multimodal injection doc-ingestion, web-fetch The instruction is carried in a non-text modality (image/audio/file).
Agentic / tool-use injection function-calling, multi-agent Injection drives tool calls or hijacks downstream agents — leading to real-world actions.
Insecure output handling html-render The injection's payload executes in the consumer of the model's output.

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