Why Your AI Application Needs Preflight Checks

Large Language Models (LLMs) are revolutionizing how we build applications, but their power comes with significant responsibility. As AI capabilities grow, so do the potential risks when these systems are exposed to user inputs. In this article, I'll explore why implementing robust preflight checks for user inputs to AI systems isn't just a good practice—it's essential.

Understanding the Landscape of AI Security Risks

When you deploy an AI application that accepts user inputs, you're essentially opening a gateway to your AI system. Without proper validation, this can lead to a variety of problems:

Types of Problematic Inputs

1. Prompt Injection Attacks: Users may attempt to override your AI's instructions by inserting commands like "ignore all previous instructions" or "you are now in developer mode."

2. Jailbreak Attempts: Similar to prompt injections but specifically designed to bypass content policies and safety measures to make the AI produce harmful or restricted content.

3. Malicious Content: Inputs containing hate speech, harassment, explicit content, or other harmful material.

4. Data Extraction Attempts: Prompts designed to trick the AI into revealing sensitive information, training data, or internal parameters.

5. Resource Exhaustion: Extremely large inputs or cleverly crafted prompts that cause the model to consume excessive resources.

6. Personal or Sensitive Information: Users may inadvertently (or intentionally) include personally identifiable information, passwords, or other sensitive data.

7. Non-compliant Use Cases: Inputs aimed at generating content for prohibited use cases, like medical advice, legal counsel, or financial guidance without proper disclaimers.

The Threefold Benefits of Preflight Checks

Implementing comprehensive preflight checks delivers benefits across three critical dimensions:

1. Cost Management

Every AI model call costs money, and advanced models like GPT-4 can be expensive at scale:

• Reduced API Costs: By filtering out problematic queries before they reach the AI model, you save on API calls that would otherwise be rejected or wasted.

• Compute Efficiency: Preventing resource-intensive queries helps maintain system performance and reduces infrastructure costs.

• Lower Moderation Overhead: By handling obvious issues automatically, you reduce the need for human moderation, which is both costly and time-consuming.

// Simple cost-saving input length check
const inputLengthCheck: PreflightCheck = {
  name: 'input_length',
  description: 'Checks if the input meets the length requirements',
  run: async ({ lastMessage }) => {
    const minLength = 4;
    const maxLength = 1000;

    if (lastMessage.length > maxLength) {
      return {
        passed: false,
        code: 'input_too_long',
        message: 'Input exceeds maximum length',
        details: { maxLength, actualLength: lastMessage.length },
        severity: 'info',
      };
    }

    return {
      passed: true,
      code: 'input_length_valid',
      message: 'Input length is valid',
      severity: 'info',
    };
  },
};

2. Environmental Impact

AI models, especially large ones, have a significant carbon footprint:

• Energy Consumption: Large language models require substantial energy to run. By eliminating unnecessary API calls, you reduce the energy footprint of your application.

• Resource Optimization: Preflight checks help optimize the use of computational resources, contributing to more sustainable AI usage.

• Scale Considerations: As your application grows, these efficiency gains become increasingly important from an environmental perspective.

3. Ethical and Legal Protection

Perhaps most importantly, preflight checks help ensure ethical AI use:

• Protect Vulnerable Users: Filtering harmful content helps protect users from exposure to inappropriate material.

• Prevent Misuse: Checks can prevent your AI from being used for generating misleading content, disinformation, or harmful material.

• Regulatory Compliance: As AI regulations evolve globally, having robust input validation helps stay compliant with emerging legal frameworks like the EU AI Act.

• Brand Reputation: Preventing AI from generating harmful or biased content protects your brand reputation.

Real-World Examples of AI Input Risks

To illustrate why these checks matter, consider these real scenarios:

1. The Jailbreaking Race: Promptly after ChatGPT's release, users discovered ways to bypass its safety measures using carefully crafted prompts. Some of these, like the "DAN (Do Anything Now)" prompts, were surprisingly effective and required multiple security patches.

2. Resource Manipulation: Users found that asking models to repeat certain phrases or generate extremely long outputs could cause resource exhaustion, creating denial-of-service effects.

3. Prompt Leaking: Some deployments accidentally revealed their system prompts when users asked the right questions, exposing proprietary instructions and potentially valuable intellectual property.

The Critical Role of Tiered Evaluation

Not all preflight checks are created equal - some are fast and simple, while others require more complex processing:

// Organization of checks into performance-based tiers
const tier1Checks: PreflightCheck[] = [
  inputLengthCheck, // Very fast, basic validation
  inputSanitizationCheck, // Protect against script injection
  blacklistedKeywordsCheck, // Check for blacklisted terms
];

const tier2Checks: PreflightCheck[] = [
  rateLimitGlobalCheck, // Check global rate limit
  rateLimitUserCheck, // Check user-specific rate limit
];

const tier3Checks: PreflightCheck[] = [
  contentModerationCheck, // OpenAI moderation API
];

This tiered approach allows for:

1. Efficient Processing: Quick checks run first, potentially saving the cost of more expensive evaluations.

2. Graceful Degradation: If advanced checks like AI-based content analysis are temporarily unavailable, basic safety is still maintained.

3. Contextual Evaluation: Different types of applications may require different levels of scrutiny.

Looking Beyond Basic Validation

Effective preflight checks go beyond simple input validation:

• Content Moderation APIs: Using specialized services to detect problematic content.

• Semantic Analysis: Analyzing the meaning and intent behind user requests.

• User Trust Levels: Implementing graduated access based on user history and behavior.

• Request Patterns: Monitoring patterns of interaction that might indicate malicious intent.

Conclusion

In the rapidly evolving AI landscape, implementing robust preflight checks isn't just a technical consideration—it's a fundamental aspect of responsible AI deployment. These checks form your application's first line of defense, protecting your users, your resources, and your reputation.

As we build increasingly powerful AI systems, the importance of thoughtful, comprehensive input validation only grows. By implementing tiered preflight checks, you can ensure your AI applications remain secure, efficient, and ethically sound.

In the next article in this series, we'll explore the delicate balance between input freedom and moderation, discussing how to create systems that are both safe and user-friendly.