Assessing privacy risks in large language model outputs including training data memorisation, PII leakage in generated text, prompt injection leading to data extraction, and hallucinated personal data. Covers output filtering, guardrails, and monitoring. Keywords: LLM privacy, output risk, memorisation, PII leakage, prompt injection, hallucinated PII.
$npx -y skills add mukul975/privacy-data-protection-skills --skill llm-output-privacy-riskInstalls into the current project.
Run `npx skills use "https://github.com/mukul975/privacy-data-protection-skills" --skill "mukul975/privacy-data-protection-skills/llm-output-privacy-risk"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/mukul975/privacy-data-protection-skills" that are relevant to the current task. Run `npx skills add "https://github.com/mukul975/privacy-data-protection-skills"` and select the relevant skills, then follow their instructions.