Prompt Injection Defense: Architecture and Controls
TL;DR
Prompt injection attacks manipulate AI systems by embedding malicious instructions in user inputs or retrieved content. Defense requires layered controls: instruction isolation, input sanitization, output validation, privilege limitation, and monitoring. Use structured prompts, content filtering, semantic analysis, and human oversight for sensitive operations. Test continuously with attack patterns and maintain response procedures for incidents.
Key Facts
Prompt injection exploits the instruction-following nature of LLMs to override intended behavior.
Effective defense requires multiple layers: input, processing, output, and monitoring controls.
Separating system instructions from user inputs reduces successful override attempts.
Retrieved content and external data sources can carry hidden injection payloads.
Attack patterns evolve rapidly, requiring continuous testing and control updates.
Implementation Steps
Implement instruction isolation using structured prompts and clear boundaries.
Deploy input filters to detect and neutralize injection attempts.
Validate outputs for unexpected content, commands, or data exposure.
Limit AI system privileges and require approval for sensitive operations.
Monitor for injection patterns and anomalous behavior with automated alerts.
Establish incident response procedures for confirmed injection attacks.
Glossary
- Prompt injection
- Attack technique that manipulates AI systems through crafted inputs or instructions
- Instruction isolation
- Architecture pattern separating system prompts from user-controllable inputs
- Input sanitization
- Process of filtering and cleaning user inputs before AI processing
- Output validation
- Verification that AI outputs meet safety and security requirements
- Semantic analysis
- Examination of meaning and intent in AI inputs and outputs
- Privilege escalation
- Unauthorized increase in system access or capabilities
References
- [1] NIST AI Risk Management Framework https://www.nist.gov/itl/ai-risk-management-framework
- [2] AI Security Best Practices https://www.nist.gov/itl/ai-risk-management-framework
Machine-readable Facts
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