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

Flash Dynamic Mask Attention includes CUDA kernels and C++ extensions that execute on your GPU. When using this library:

• Only install from trusted sources (official PyPI releases or verified builds)
• Be cautious when building from source with modifications
• Verify checksums when downloading pre-built binaries

This library depends on:

• PyTorch (with CUDA support)
• NVIDIA CUTLASS library
• Standard Python scientific computing libraries

We recommend keeping all dependencies up to date and using virtual environments for isolation.

Our CUDA kernels are designed with memory safety in mind:

• Bounds checking is implemented where performance allows
• Memory allocation patterns are tested across different input sizes
• We use established patterns from Flash Attention and CUTLASS

However, as with any low-level CUDA code:

• Very large input tensors may cause out-of-memory errors
• Invalid input shapes may cause undefined behavior
• Custom modifications to kernel code should be thoroughly tested

If you discover a security vulnerability, please report it responsibly:

For security issues:

• Email: [email protected]
• Subject: [SECURITY] Flash-DMA Vulnerability Report
• Include: Detailed description, reproduction steps, and potential impact

For general bugs:

• Use our GitHub Issues
• Follow our contributing guidelines

• Acknowledgment: Within 48 hours
• Initial Assessment: Within 1 week
• Resolution: Depends on severity and complexity

Critical security issues will be prioritized and may result in emergency releases.

When using Flash Dynamic Mask Attention:

1. Environment Isolation

   # Use virtual environments
   python -m venv flash_dma_env
   source flash_dma_env/bin/activate  # Linux/Mac
   # or
   flash_dma_env\Scripts\activate     # Windows

2. Dependency Management

   # Keep dependencies updated
   pip install --upgrade torch flash-dmattn

3. Input Validation

   # Validate tensor shapes and dtypes before processing
   assert query.dtype in [torch.float16, torch.bfloat16, torch.float32]
   assert query.shape == key.shape == value.shape

4. Resource Monitoring

   # Monitor GPU memory usage
   import torch
   print(f"GPU Memory: {torch.cuda.memory_allocated() / 1e9:.2f} GB")

• Confirmed vulnerabilities will be disclosed responsibly
• Security fixes will be released as soon as safely possible
• CVE numbers will be requested for significant vulnerabilities
• Credit will be given to security researchers who report issues responsibly

For security-related questions or concerns:

• Primary: [email protected]
• Project maintainers: See AUTHORS file

For general support:

• GitHub Issues: https://github.com/SmallDoges/flash-dmattn/issues
• Documentation: https://github.com/SmallDoges/flash-dmattn/tree/main/docs/