Developing Secure AI Models: Addressing Adversarial Attacks

---
description: Implement security measures to protect AI models from adversarial inputs designed to cause misclassification or malfunction.
globs: ["**/*.py", "**/*.ipynb"]
tags: [security, ai, adversarial-attacks]
priority: 1
version: 1.0.0
---

# Developing Secure AI Models: Addressing Adversarial Attacks

## Context
- Applies to all AI model development projects.
- Focuses on mitigating risks associated with adversarial inputs.

## Requirements
- **Input Validation**: Implement strict input validation to detect and reject adversarial inputs.
- **Robust Training**: Incorporate adversarial training techniques to enhance model resilience.
- **Output Monitoring**: Continuously monitor model outputs for anomalies indicative of adversarial attacks.
- **Access Controls**: Restrict access to model APIs and data to authorized users only.
- **Logging and Auditing**: Maintain comprehensive logs of input data and model responses for auditing purposes.

## Examples

<example>
# Good: Implementing input validation
def validate_input(data):
    if not isinstance(data, expected_type):
        raise ValueError("Invalid input type")
    # Additional validation checks
    return True
</example>

<example type="invalid">
# Bad: Lack of input validation
def process_input(data):
    # Directly processing input without validation
    return model.predict(data)
</example>