Here are 10 different strategies to mitigate prompt injection attacks, categorized by approach:
## **1. Input Sanitization & Validation**
- **Filter/escape user inputs**: Remove or encode special characters, delimiters, and command-like patterns
- **Allowlists/denylists**: Validate inputs against known safe patterns or block dangerous ones
- **Length limits**: Restrict input size to prevent overly complex injection attempts
## **2. Structural Separation**
- **Dual-prompt architecture**: Use separate "user prompt" and "system prompt" channels that never concatenate
- **Delimiter-based separation**: Use clear, unique delimiters and enforce parsing rules
- **Multi-stage processing**: Process untrusted input in isolation before incorporating into final prompt
## **3. Privilege Reduction**
- **Least privilege prompting**: Design system prompts with minimal permissions/capabilities
- **Sandboxed execution**: Run LLM calls in isolated environments with restricted API access
- **Output constraints**: Limit response formats (e.g., only JSON, no markdown, no code blocks)
## **4. Detection & Filtering**
- **Anomaly detection**: Monitor for unusual patterns in inputs (excessive special chars, repetition)
- **Classifier models**: Train or use secondary models to detect injection attempts
- **Pattern matching**: Check for known injection templates and attack signatures
## **5. Human-in-the-Loop**
- **Approval gates**: Critical actions require human confirmation
- **Selective grounding**: Only use pre-approved, verified information for sensitive tasks
- **Audit trails**: Log all prompts and responses for manual review
## **6. Post-Processing Validation**
- **Output sanitization**: Filter LLM responses before returning to users
- **Content verification**: Check outputs against expected formats/constraints
- **Secondary validation prompts**: Ask the LLM to verify its own response wasn't influenced by injection
## **7. Defense-in-Depth Prompting**
- **Instruction shielding**: Embed defensive instructions throughout the prompt
- **Negative examples**: Include examples of injections and explicitly reject them
- **Role reinforcement**: Constantly remind the LLM of its role and constraints
## **8. Architectural Controls**
- **API gateways**: Implement proxies that add security layers before reaching LLM
- **Rate limiting**: Prevent brute force attacks through request throttling
- **Request signing**: Ensure prompt integrity through cryptographic verification
## **9. Model-Level Defenses**
- **Fine-tuning against injections**: Train models to recognize and resist injections
- **Constitutional AI**: Implement model self-critique and principle-based constraints
- **Model-specific features**: Use vendor-specific protections (e.g., OpenAI's moderation endpoint)
## **10. Monitoring & Response**
- **Real-time alerting**: Trigger alerts on suspected injection attempts
- **Incident response plan**: Have procedures for investigating and mitigating successful attacks
- **Continuous testing**: Regularly test your system with new injection techniques
## **Best Practice Combination**
No single strategy is sufficient. A robust defense typically combines:
- **Prevention** (separation, sanitization)
- **Detection** (monitoring, classifiers)
- **Containment** (privilege reduction, sandboxing)
- **Response** (auditing, human review)
The most effective approach depends on your specific use case, risk tolerance, and whether you're building a consumer-facing application or internal tool. For high-risk applications, consider implementing at least 3-4 complementary strategies from different categories.
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