Individual skill

ai-insecure-output-handling

Exploit apps that trust LLM output — pass model text unsanitized into XSS sinks, SQL, shell, code, or downstream calls. Load when LLM output is rendered as HTML/markdown, executed, or fed to another system. Signals: chatbot output shown with innerHTML/dangerouslySetInnerHTML, "run this code", LLM-generated queries/commands, agent output used in eval/exec.

skills/ai-ml/ai-insecure-output-handling/SKILL.md

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Observed behavior: Network or broad shell
Last checked: Nov 11, 2026

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Repository stars (not skill ratings): 27
Repository last updated: Oct 14, 2026
License: MIT

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# Insecure output handling (LLM → sink)

## When it applies
The app treats LLM output as trusted and passes it into a dangerous sink — rendered as HTML,
executed as code/SQL/shell, or forwarded to another API. The model becomes an injection vector,
especially when its input is attacker-influenced (→ chain with `ai-prompt-injection`).

## Why it works
Developers trust their own model's output, but it's just text — and an attacker can steer it via
prompt injection. If that text lands in `innerHTML`, `eval`, a SQL string, a shell command, or a
system call without sanitization, you get XSS/RCE/SQLi *through* the LLM.

## Method
1. **Find the sink**: where does model output go? HTML render (`innerHTML`, markdown→HTML,