We actively maintain and provide security updates for the following versions of Neural Forge:

We take the security of Neural Forge seriously. If you believe you have found a security vulnerability, please report it to us as described below.

Please do NOT report security vulnerabilities through public GitHub issues.

Instead, please report sensitive security information by email to:

• Email: fenil.sonani@example.com
• Subject: [SECURITY] Neural Forge Vulnerability Report

When reporting a vulnerability, please include:

1. Description - A clear description of the vulnerability
2. Impact - What kind of vulnerability it is and who might be affected
3. Reproduction - Step-by-step instructions to reproduce the issue
4. Proof of Concept - If applicable, include code or screenshots
5. Suggested Fix - If you have ideas on how to fix it
6. Disclosure Timeline - Your preferred timeline for public disclosure

## Vulnerability Summary
Brief description of the vulnerability

## Vulnerability Details
### Type
[e.g., Code Injection, Buffer Overflow, Unauthorized Access]

### Severity
[Critical/High/Medium/Low]

### Affected Components
[e.g., Tensor operations, CUDA backend, File I/O]

### Impact
What an attacker could achieve by exploiting this vulnerability

## Reproduction Steps
1. Step 1
2. Step 2
3. Step 3

## Proof of Concept
```python
# Example code demonstrating the vulnerability
import neural_forge as nf
# ... vulnerable code here

Your ideas for fixing the vulnerability

Any other relevant details

### ⏱️ Response Timeline

We will acknowledge your report within **48 hours** and provide a detailed response within **7 days** indicating:
- Confirmation of the issue
- Our planned timeline for addressing it
- Any additional information needed

### 🎯 Scope

This security policy applies to vulnerabilities in:

#### ✅ In Scope
- **Core tensor operations** that could lead to memory corruption
- **File I/O operations** that could enable directory traversal or file system access
- **Network operations** in distributed training that could enable remote code execution
- **Serialization/deserialization** that could enable code injection
- **CUDA/GPU operations** that could cause system instability
- **Memory management** issues that could lead to information disclosure
- **Input validation** bypasses that could lead to crashes or unexpected behavior

#### ❌ Out of Scope
- **Denial of Service** through resource exhaustion (unless it leads to system compromise)
- **Issues requiring physical access** to the machine
- **Vulnerabilities in dependencies** (please report to the respective projects)
- **Theoretical attacks** without practical proof of concept
- **Self-XSS** or similar issues requiring user interaction to exploit themselves

### 🏆 Recognition

We believe in recognizing security researchers who help make Neural Forge more secure:

#### Acknowledgments
- We maintain a **Security Hall of Fame** for researchers who report valid vulnerabilities
- With your permission, we'll acknowledge your contribution in our security advisories
- We may reference your research in our documentation (with your consent)

#### Responsible Disclosure
We follow responsible disclosure practices:
1. **Private report** received and acknowledged
2. **Investigation and fix** development (with regular updates to reporter)
3. **Security release** with fix deployed
4. **Public disclosure** of the vulnerability details (after fix is widely available)
5. **Credit given** to the researcher (if desired)

### 🔧 Security Best Practices for Users

#### For Developers
```python
# ✅ Good: Validate input tensors
def safe_operation(tensor):
    if not isinstance(tensor, Tensor):
        raise TypeError("Expected Tensor input")
    if tensor.shape[0] > MAX_ALLOWED_SIZE:
        raise ValueError("Tensor too large")
    return process(tensor)

# ❌ Bad: No input validation
def unsafe_operation(tensor):
    return process(tensor)  # Could crash with invalid input

• Keep Neural Forge updated to the latest stable version
• Validate all inputs before processing with Neural Forge
• Run with minimal privileges - don't run training as root
• Monitor resource usage to detect potential DoS attempts
• Use secure file permissions for model checkpoints and data
• Sanitize file paths when loading/saving models

# ✅ Good: Secure model loading
def load_model_safely(model_path):
    # Validate path is in allowed directory
    safe_path = validate_path(model_path)
    # Load with size limits
    return load_model(safe_path, max_size=MAX_MODEL_SIZE)

# ❌ Bad: Unsafe model loading
def load_model_unsafely(model_path):
    return load_model(model_path)  # Could load from anywhere

• Large tensor allocation can cause out-of-memory conditions
• Invalid shapes in operations can cause crashes
• Numerical overflow in computations can lead to unexpected behavior

• Model serialization uses pickle by default - only load trusted models
• Data loading should validate file paths and sizes
• Checkpoint files may contain arbitrary Python code

• GPU memory allocation failures can crash the process
• Memory leaks in long-running training can exhaust system resources
• Shared memory in distributed training needs proper access controls

• Automated security scanning in CI/CD pipeline
• Dependency vulnerability monitoring with automated updates
• Code review requirements for all changes
• Static analysis to catch common vulnerabilities
• Fuzzing tests for input validation

• OWASP Machine Learning Security Top 10
• NIST AI Risk Management Framework
• Python Security Best Practices

We will:

• Monitor dependencies for known vulnerabilities
• Release security patches promptly for confirmed issues
• Maintain security changelog documenting all security-related changes
• Provide migration guides when security fixes require API changes

For non-security related issues:

• GitHub Issues: Issues Page
• General Questions: Use GitHub Discussions

For security concerns:

• Security Email: fenil.sonani@example.com (GPG key available upon request)
• Emergency Contact: Include "[URGENT SECURITY]" in subject line

This security policy is provided in good faith to encourage responsible disclosure of security vulnerabilities. We reserve the right to:

• Modify this policy at any time
• Determine the validity and scope of reported vulnerabilities
• Choose our response timeline based on severity and complexity

We will not pursue legal action against security researchers who:

• Report vulnerabilities in good faith according to this policy
• Make a good faith effort to avoid privacy violations and data destruction
• Do not exploit vulnerabilities beyond what is necessary to demonstrate the issue

Thank you for helping keep Neural Forge and our community safe! 🛡️