defend-llm-prompt-injection

Secure LLM agents against prompt injection attacks by enforcing strict trust boundaries with input/output guardrails, PII redaction, and random per-request delimiters to isolate untrusted data from system instructions.

Prevent data exfiltration from RAG content by implementing multi-layered defense mechanisms including injection classifiers, schema validation, and strict data containment to stop hidden instructions from triggering unauthorized tool actions.

Implement input guardrails for untrusted user input by applying input sanitization protocols, injection classifiers, and schema validation to ensure malicious users cannot hijack your agent or leak system prompts.

Yes, this approach maintains access to sensitive tools while processing untrusted documents by enforcing strict separation between trusted system instructions and untrusted data content using random per-request delimiters.

Test regression against an attack corpus for LLM cybersecurity by evaluating your hardened agent architecture against known prompt injection, jailbreak, and data exfiltration patterns to verify defense effectiveness.

Hidden instructions in uploaded files force malicious actions because untrusted data content lacks strict isolation from system instructions, allowing input to hijack your LLM agent without proper trust boundaries.