# Prompt Injection, Privacy and Bias — Large Language Models

Source: https://www.geekswithgeeks.com/en/llms/e-security

> Recognise the main security and ethics risks of LLM applications.

## Text can carry commands

**Prompt injection**: instructions hidden in user input or in retrieved content (a web page, an email, a PDF) can override your rules, because the model cannot reliably tell data from instructions. Defences: keep **secrets out of prompts**, give tools the **least privilege**, validate and sanitise model output before it reaches shells, SQL or browsers, require **human approval** for side-effecting actions, and separate trusted instructions from untrusted text. **Privacy**: do not send personal data to providers without a lawful basis and a data-handling agreement; log carefully and redact. **Bias and fairness**: models absorb stereotypes from data, so test across groups and languages and monitor outcomes. Follow the laws and policies that apply to you.

## Assume the model can be tricked

Design so that even a fully hijacked model cannot do serious harm: narrow tools, read-only access where possible, approvals for anything irreversible.

**Quiz:** Which design best limits damage from prompt injection?

- [x] Least-privilege tools plus human approval for risky actions
- [ ] Giving the model admin access
- [ ] Hiding the system prompt only
- [ ] Using a higher temperature

*Answer:* Least-privilege tools plus human approval for risky actions. Limiting what a compromised model can do bounds the harm.