How do you manage prompt injection and data leakage risks with LLMs?

Prompt Injection Mitigation

• Input Sanitization:

  All user inputs are sanitized using strict allowlists and escaped to remove special characters or tokens that could manipulate prompt context.

• Prompt Templates:

  We use well-structured prompt templates with static instruction blocks and clearly bounded user input areas. This ensures user content cannot override system behavior.

• System/User Segregation:

  Clear separation of system-level instructions from user-level content using delimiters or structured JSON formats to reduce the injection surface.

• Content Filtering:

  We scan both inputs and outputs using keyword-based and AI-assisted filters to block or redact sensitive terms, jailbreak attempts, or malicious payloads.

Data Leakage Protection

• Minimal Context Principle:

  Only relevant, non-sensitive data is passed to LLMs. PII and business-sensitive inputs are explicitly excluded.

• Access Control:

  Access to prompt construction logic, logs, and model responses is restricted and audited, especially in multi-tenant or enterprise setups.

• API Key Security:

  LLM API keys are stored in encrypted secret managers (e.g. Vault, AWS Secrets Manager) and rotated regularly.

• Output Monitoring:

  Responses from the LLM are post-processed and filtered before delivery to end users to catch hallucinations or confidential data leaks.

  • Rule-based & keyword filters catch profanity, sensitive terms, and format issues.

  • AI moderation tools (e.g. OpenAI Moderation) scan for toxicity or unsafe content.

• Hosted vs On-Prem Decisioning:

  For high-risk data contexts, we prefer on-prem or private-hosted models, avoiding external LLM APIs entirely.