Guides

Building Prompt injection prevention with OWASP LLM Top 1...

This guide provides a structured approach to securing AI applications by addressing common vulnerabilities in prompt handling, API key management, data leakage, and compliance. Each step includes implementation details and real-world tool integration.

2-3 hours5 steps
1

Conduct threat modeling for LLM interfaces

Map attack vectors for user input channels. Identify where prompt injection, data exfiltration, and API key exposure risks exist. Use OWASP LLM Top 10 as a baseline.

threat_model.md
# Threat Model

## Attack Surface
- User input fields
- API request parameters
- System prompts

## Mitigations
- Input validation
- Prompt sanitization
- Access controls

⚠ Common Pitfalls

  • •Overlooking indirect input channels (e.g., file uploads)
  • •Failing to update threat models during feature changes
2

Implement prompt injection prevention

Use framework-specific guardrails to isolate user input. Validate and sanitize prompts before LLM processing. Test with known injection patterns.

prompt_sanitize.py
from guardrails import Guard

prompt_guard = Guard().add("detect_prompt_injection")
secure_prompt = prompt_guard.validate(user_input)

⚠ Common Pitfalls

  • •Relying solely on regex-based detection
  • •Ignoring multi-turn conversation contexts
3

Secure LLM API key management

Store credentials in secret management systems. Use IAM roles for cloud providers. Rotate keys regularly and monitor access logs.

get_api_key.sh
aws secretsmanager get-secret-value --secret-id /ai/app/llm_key

⚠ Common Pitfalls

  • •Hardcoding keys in configuration files
  • •Using shared credentials across multiple services
4

Prevent data leakage in outputs

Implement content redaction for sensitive information. Use LLM output filters to block prohibited patterns. Test with synthetic data samples.

data_redact.py
import re

def redact_sensitive(text):
    return re.sub(r'\b\d{3}-\d{2}-\d{4}\b', '[REDACTED]', text)

⚠ Common Pitfalls

  • •Overlooking non-ASCII character patterns
  • •Failing to test edge cases in output formatting
5

Implement content moderation pipeline

Integrate AI content filters to block harmful outputs. Use multi-layer validation (e.g., keyword matching + LLM-based classification). Regularly update banned word lists.

content_moderate.py
from nemo_guardrails import Guard

moderation_guard = Guard().add("harmful_content_detection")
filtered_output = moderation_guard.validate(response_text)

⚠ Common Pitfalls

  • •Ignoring context-dependent content risks
  • •Overlooking false positive rates in production

What you built

Security for AI applications requires continuous validation of input handling, secret management, and output controls. Regularly update guardrails using OWASP LLM guidelines and audit implementations against compliance requirements.