AI Security & Red Teaming — Prompt Injection and Jailbreak Defense Strategies

What is AI Security?

AI security is the discipline of protecting AI systems from malicious attacks and designing systems resilient to such attacks. It provides security at both model and pipeline levels.

Core Attack Types

Prompt Injection

Malicious instruction injection into natural language prompts. Attempts to override the model's original instructions.

Jailbreak

Attempts to bypass model safety boundaries. Known jailbreak techniques:

• DAN (Do Anyting Now): Persona assignment that liberates the model
• Role-play: Assigning dangerous roles
• Encoding bypass: Encoding with Base64, ROT13
• Multi-step: Gradually bypassing boundaries over time
• Multi-language: Attacking in low-resource languages

Data Poisoning

Injecting malicious data into training data. Specifically manipulates model behavior.

Model Evasion

Bypassing model classification/guardrails. Uses adversarial input techniques.

Defense Strategies

Prompt Shielding

• Instruction separation (separating system prompt from user input)
• Input validation and sanitization
• Using structured output formats

Multi-Layer Guardrails

• Input guard: Check user input first
• Model guard: Check model output
• Output guard: Filter before sending to end user

Red Teaming

Enterprise-level AI security testing:

1. Create attack scenarios
2. Run automated test scripts
3. Classify vulnerabilities (critical, high, medium, low)
4. Fix and re-test

Tools

• LLM Guard: Prompt injection detection
• Guardrails AI: Output validation
• Prompt Armor: Jailbreak protection
• Langfuse: Monitoring and observability

Conclusion

AI security is a mandatory part of using LLM systems in production. Vulnerabilities should be proactively identified through red teaming, and defense provided through multi-layer guardrails.