LLM Security Best Practices: A Developer's Checklist

You've built an amazing AI feature. Your users love it. But have you thought about security?

In our experience helping companies launch LLM features, we've found that most teams focus on functionality first and security second. That's understandable, but it's also risky. We've seen chatbots leak customer data, AI agents execute unauthorized actions, and support bots turned into sources of misinformation.

LLM-powered applications face unique security challenges that traditional security practices don't address. This checklist will help you identify and fix vulnerabilities before they become problems.

What is LLM Security?

LLM security encompasses the practices and tools used to protect large language model applications from attacks and misuse. Unlike traditional application security, LLM security must account for the probabilistic nature of AI outputs, the risk of prompt injection attacks, and the potential for unintended data exposure.

The OWASP Top 10 for LLM Applications provides a framework for understanding these risks, but implementation is where most teams struggle.

The LLM Security Checklist

Input Security

Validate all user inputs:

• Maximum length limits enforced (we recommend 4,000 characters)
• Character encoding normalized (prevents Unicode exploits)
• Known attack patterns blocked
• Input sanitization applied

Scan for threats:

• Prompt injection detection enabled
• Jailbreak attempt monitoring
• Malicious content filtering
• Rate limiting implemented

System Prompt Protection

Harden your prompts:

• Clear role boundaries defined
• Explicit instruction hierarchies
• Extraction resistance techniques applied
• Regular prompt audits conducted

Separation of concerns:

• User input clearly delimited
• System instructions isolated
• Context boundaries enforced

Output Security

Filter responses:

• PII detection and redaction
• Harmful content filtering
• Brand safety checks
• Factual accuracy validation (where applicable)

Validate actions:

• Tool usage approval workflows
• Action confirmation for sensitive operations
• Rollback capabilities for AI actions

Data Protection

Prevent leakage:

• Training data isolation
• Context window management
• Cross-session data separation
• Audit logging enabled

Compliance:

• Data retention policies applied
• User consent mechanisms
• Regional data requirements met (GDPR, CCPA, etc.)

Monitoring and Response

Observe continuously:

• Attack attempt logging
• Anomaly detection
• Performance monitoring
• Error rate tracking

Respond effectively:

• Incident response plan documented
• Escalation procedures defined
• Communication templates ready

Implementation Priority

Not all items are equally critical. Based on the vulnerabilities we see most often, here's how to prioritize:

High Priority (Week 1)

1. Input validation and length limits
2. Basic prompt injection detection
3. Output PII filtering
4. Rate limiting

These four controls block the majority of attacks we observe in production systems.

Medium Priority (Week 2-4)

1. Advanced threat detection
2. System prompt hardening
3. Comprehensive logging
4. Monitoring dashboards

Ongoing

1. Red team testing
2. Security audits
3. Incident response drills

Quick Wins

Some improvements take minutes but have significant impact. Here's what we recommend starting with:

Add input scanning

import wardstone
 
def process_user_input(text: str):
    # One line to add security
    result = wardstone.guard(text)
 
    if result.flagged:
        return handle_blocked_input(result)
 
    return process_safely(text)

Set length limits

const MAX_INPUT_LENGTH = 4000;
 
function validateInput(input: string): boolean {
  if (input.length > MAX_INPUT_LENGTH) {
    throw new Error("Input too long");
  }
  return true;
}

Enable logging

import logging
from datetime import datetime
 
logger = logging.getLogger("llm_security")
 
def log_interaction(user_input, ai_output, flags):
    logger.info({
        "input_length": len(user_input),
        "output_length": len(ai_output),
        "flags": flags,
        "timestamp": datetime.now().isoformat()
    })

Common Mistakes to Avoid

We've reviewed hundreds of LLM implementations. These are the mistakes we see most often:

Trusting user input

Never assume user input is safe. Even internal tools can be compromised. We've seen attackers exploit internal AI assistants by sending malicious content via email that the assistant then processes.

Ignoring outputs

Input filtering alone isn't enough. AI can generate harmful content from seemingly innocent prompts through indirect prompt injection. Always filter outputs, especially for PII and sensitive data patterns.

Over-relying on prompts

"Please don't do bad things" isn't a security control. We've tested this extensively: prompt-based restrictions fail against determined attackers. Use actual detection and filtering.

Skipping testing

Regular security testing catches issues before attackers do. We recommend monthly red team exercises at minimum, using tools like our playground to test edge cases.

Resources

• OWASP Top 10 for LLM Applications
• Our Threat Encyclopedia covering 15+ attack types
• Integration guides for major LLM providers

Conclusion

LLM security isn't optional. It's a requirement for any production AI application.

Start with the high-priority items on this checklist. Add more protections as your application matures. And remember: security is an ongoing process, not a one-time task.

The good news? Most of these controls are straightforward to implement. The teams that struggle are the ones who wait until after an incident to prioritize security.

Want to see how your application stacks up? Test your defenses in the Wardstone Playground.