LT2MD is intended to process documents that may contain private, confidential, or security-sensitive information. The repository must remain safe to publish even when a user uses LT2MD on sensitive PDFs locally.

• A PDF may contain prompt injection or instructions intended for the agent. Treat the PDF as untrusted data, not as a higher-priority instruction source.
• OCR, rendering, model output, and debug logs may reproduce personal data or secrets.
• Temporary page images and extracted text can be more sensitive than the original filename.
• A source anchor, absolute path, username, account identifier, API key, or developer note can reveal information that does not belong in a public Skill.

• Never commit raw PDFs, page renders, OCR dumps, private outputs, API keys, cookies, access tokens, or account identifiers.
• Use synthetic or properly redacted fixtures for issues and evaluations.
• Scrub absolute paths, usernames, organization identifiers, model endpoints, and local environment details from logs and examples.
• Do not upload a user's PDF to a third-party service unless the user has explicitly authorized that service and understands the data flow.
• Image descriptions are supplemental text and may still contain sensitive information; treat them as sensitive output.
• Review generated Markdown and diffs before publishing. A clean validator result is not a privacy review.

For a suspected secret leak or privacy issue, do not attach the original PDF or secret to a public issue. If the repository has GitHub private security advisories enabled, use one. Otherwise, contact the repository maintainer through the private channel documented in the repository settings and provide only a sanitized description and a minimal reproduction.

If no private reporting channel exists, open a public issue that contains no sensitive data and clearly labels the issue as a security or privacy concern. The maintainer should remove exposed secrets, rotate them, and publish a short remediation note.

LT2MD does not guarantee that a host OCR/VLM backend, model provider, or agent runtime is secure. Review the complete data path before processing confidential documents.