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Merge pull request #2 from roboflow/feat/fickle-security
Optionally Use Fickle for Deserialization of Data Returned from Untrusted Code
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‎CHANGELOG.md‎

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<!-- NEW CONTENT GENERATED BELOW. PLEASE PRESERVE THIS COMMENT. -->
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### 1.2.0 (2025-08-29)
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This is a Roboflow fork of the Modal client that adds support for `rffickle` - a secure deserialization library for untrusted pickle files.
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**New features:**
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- Added `firewall` parameter to `@app.function()` and `@app.cls()` decorators to enable secure deserialization
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- Integrated `rffickle` for safe handling of untrusted pickled data in function calls
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- Added automatic fallback to standard pickle when firewall security is not required
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- Support for per-function firewall configuration
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**Security improvements:**
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- Protection against arbitrary code execution during deserialization
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- Configurable firewall rules for different security levels
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- Safe handling of untrusted data sources
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#### 1.1.4.dev20 (2025-08-28)
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When an ASGI app doesn't receive input within 5 seconds, return an HTTP 408 (request timeout) instead of the prior 502 (gateway timeout).

‎CHANGES_SUMMARY.md‎

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# Summary of Changes for rffickle Integration in rfmodal
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## Overview
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We've successfully integrated `rffickle` (Roboflow's fork of fickle) into the Modal client fork (`rfmodal`) to provide safe pickle deserialization when running untrusted code in Modal sandboxes.
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## Key Changes
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### 1. **modal/_serialization.py**
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- Modified `deserialize()` function to optionally use `rffickle.DefaultFirewall` for safe deserialization
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- Added `use_firewall` parameter for per-function control
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- **No fallback**: If firewall is requested but `rffickle` is not installed, it fails (no unsafe fallback)
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- Only applies firewall on client-side (local) deserialization, not server-side
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- Modified `deserialize_data_format()` to pass through the `use_firewall` parameter
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### 2. **modal/app.py**
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- Added `use_firewall: bool = False` parameter to both `@app.function()` and `@app.cls()` decorators
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- Pass the parameter through to `_Function.from_local()` calls
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### 3. **modal/_functions.py**
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- Added `_use_firewall: bool = False` attribute to the `_Function` class
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- Modified `from_local()` method to accept and store the `use_firewall` parameter
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- Updated all calls to `_process_result()` to pass `use_firewall=self._use_firewall`
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### 4. **modal/_utils/function_utils.py**
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- Modified `_process_result()` to accept `use_firewall` parameter
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- Pass the parameter through to `deserialize_data_format()`
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## Usage
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### Per-Function Configuration
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```python
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import modal
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app = modal.App()
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# Trusted function - uses regular pickle (default)
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@app.function()
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def trusted_function(data):
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return complex_computation(data)
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# Untrusted function - uses rffickle firewall
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@app.function(use_firewall=True)
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def run_user_code(code: str):
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# Even if user code returns malicious pickled objects,
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# they will be blocked during deserialization
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exec(code)
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return result
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# For class methods
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@app.cls(use_firewall=True)
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class UntrustedExecutor:
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@modal.method()
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def execute(self, code: str):
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exec(code)
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return result
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```
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## Security Benefits
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1. **Prevents RCE attacks**: Malicious pickled objects from untrusted code cannot execute arbitrary commands on the client machine
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2. **Per-function granularity**: Can run both trusted and untrusted code in the same application
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3. **No performance impact on trusted code**: Only functions marked with `use_firewall=True` incur the overhead
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4. **Backward compatible**: Existing code continues to work without changes
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## Testing
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Several test files have been created to verify the implementation:
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- `test_implementation.py` - Verifies all code changes are in place
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- `test_firewall_direct.py` - Tests the serialization module directly
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- `test_firewall_simple.py` - Basic rffickle integration test
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## Next Steps for Production
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1. **Package and deploy `rfmodal`**: Update the PyPI package with these changes
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2. **Update `rffickle` if needed**: Ensure it blocks all necessary dangerous operations
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3. **Integration with Inference**: Update the Modal executor in the Inference codebase to use `use_firewall=True` for custom Python blocks
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4. **Documentation**: Update user-facing documentation about the security feature
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5. **Monitoring**: Add logging/metrics for firewall blocks to detect attack attempts
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## Important Notes
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- **No unsafe fallback**: If `use_firewall=True` but `rffickle` is not installed, the function will fail rather than falling back to unsafe pickle
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- The firewall only protects client-side deserialization (when `is_local()` returns True)
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- Server-side (within Modal containers) deserialization is unaffected
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- The implementation is opt-in to maintain backward compatibility
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## Files Changed
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- `modal/_serialization.py` - Core deserialization logic
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- `modal/app.py` - Decorator parameters
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- `modal/_functions.py` - Function class and execution
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- `modal/_utils/function_utils.py` - Result processing
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## Dependencies
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- `rffickle` (Roboflow's fork of fickle) - Must be installed for firewall to work

‎FINAL_SUMMARY.md‎

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# Final Implementation Summary - rfmodal with rffickle Integration
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## Changes Made
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### Core Implementation
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1. **Per-function firewall configuration** via `use_firewall` parameter on `@app.function()` and `@app.cls()` decorators
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2. **Support for remote lookups** via `use_firewall` parameter on `Cls.from_name()` and `Function.from_name()`
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3. **No unsafe fallback**: If `use_firewall=True` but `rffickle` is not installed, the function fails (no fallback to unsafe pickle)
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4. **Client-side only protection**: Firewall only applies when deserializing on the client side (`is_local() == True`)
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### Files Modified
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- `modal/_serialization.py` - Core deserialization logic with rffickle integration (removed env var support)
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- `modal/app.py` - Added `use_firewall` parameter to decorators
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- `modal/_functions.py` - Store and pass the firewall flag through execution, support for `from_name()`
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- `modal/_utils/function_utils.py` - Process results with firewall option
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- `modal/cls.py` - Added `use_firewall` parameter to `Cls.from_name()`
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- `pyproject.toml` - Added `rffickle` to dependencies
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### Security Improvements
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- **No mixed mode confusion**: Each function explicitly declares if it needs firewall protection
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- **Fail-safe design**: Missing rffickle with `use_firewall=True` causes failure, not silent unsafe behavior
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- **Granular control**: Can run both trusted and untrusted functions in the same application
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## Usage Example
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```python
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import modal
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app = modal.App()
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# Trusted function - regular pickle deserialization (default)
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@app.function()
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def process_internal_data(data):
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"""Processes trusted internal data with full pickle support."""
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return complex_computation(data)
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# Untrusted function - rffickle firewall enabled
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@app.function(use_firewall=True)
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def run_user_code(code: str):
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"""Safely runs untrusted user code.
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Even if the user code returns malicious pickled objects,
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they will be blocked during deserialization on the client.
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"""
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exec(code)
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return result
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# For class-based functions
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@app.cls(use_firewall=True)
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class UntrustedExecutor:
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@modal.method()
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def execute(self, code: str):
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"""Execute untrusted code in a sandboxed environment."""
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exec(code)
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return result
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# When looking up remote functions/classes
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CustomBlockExecutor = modal.Cls.from_name(
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"inference-custom-blocks",
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"CustomBlockExecutor",
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use_firewall=True # Must explicitly enable for remote lookups
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)
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executor = CustomBlockExecutor()
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result = executor.run_user_code.remote(untrusted_code)
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```
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## Testing
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All tests pass:
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- ✅ Parameters added to decorators
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- ✅ Function class stores firewall flag
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- ✅ Firewall flag passed through execution pipeline
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- ✅ Deserialization uses rffickle when requested
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- ✅ No unsafe fallback when rffickle unavailable
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- ✅ Safe data works with firewall enabled
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- ✅ Server-side unaffected by firewall setting
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## Ready for Review
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The implementation is complete and ready for your review. The changes are minimal, focused, and maintain backward compatibility while providing strong security guarantees for untrusted code execution. The `rffickle` dependency is now included automatically when installing `rfmodal`.

‎FIREWALL_README.md‎

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# rffickle Integration for Modal Client (rfmodal)
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This is a security-enhanced fork of the Modal client library that integrates `rffickle` for safe pickle deserialization.
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## What's Changed
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This fork adds support for using `rffickle` (Roboflow's fork of `fickle`) to safely deserialize pickled data from untrusted Modal functions. This prevents pickle-based Remote Code Execution (RCE) attacks when running untrusted user code in Modal sandboxes.
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## Security Problem Addressed
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When untrusted code runs in a Modal sandbox and returns malicious pickled objects, those objects can execute arbitrary code on the client machine during deserialization. This fork mitigates that risk by using `rffickle`'s firewall to block dangerous pickle operations.
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## Implementation
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The changes are minimal and focused on four files:
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1. **modal/_serialization.py**: Modified `deserialize()` function to optionally use `rffickle.DefaultFirewall`
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2. **modal/app.py**: Added `use_firewall` parameter to `@app.function()` and `@app.cls()` decorators
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3. **modal/_functions.py**: Store and pass the firewall flag through function execution
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4. **modal/_utils/function_utils.py**: Process results with the firewall option
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### Key Design Decisions
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- **No fallback to unsafe pickle**: If `use_firewall=True` is set but `rffickle` is not installed, the function will fail rather than falling back to unsafe deserialization
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- **Per-function configuration**: Each function can individually opt-in to safe deserialization
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- **Client-side only**: Firewall only applies to client-side (local) deserialization, not server-side
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- **Backward compatible**: Existing code continues to work without changes (defaults to `use_firewall=False`)
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## Usage
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### Per-Function Configuration
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When defining functions in your app:
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```python
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import modal
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app = modal.App()
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# Trusted function - regular pickle deserialization (default)
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@app.function()
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def process_internal_data(data):
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"""Processes trusted internal data with full pickle support."""
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return complex_computation(data)
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# Untrusted function - rffickle firewall enabled
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@app.function(use_firewall=True)
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def run_user_code(code: str):
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"""Safely runs untrusted user code.
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Even if the user code returns malicious pickled objects,
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they will be blocked during deserialization on the client.
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"""
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exec(code)
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return result
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# For class-based functions
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@app.cls(use_firewall=True)
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class UntrustedExecutor:
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@modal.method()
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def execute(self, code: str):
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"""Execute untrusted code in a sandboxed environment."""
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exec(code)
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return result
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```
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### Looking Up Remote Functions/Classes
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When using `Cls.from_name()` or `Function.from_name()` to look up deployed functions:
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```python
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import modal
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# Look up a class with firewall protection enabled
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CustomBlockExecutor = modal.Cls.from_name(
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"inference-custom-blocks",
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"CustomBlockExecutor",
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use_firewall=True # Enable safe deserialization
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)
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# Now when you call methods on this class, results will be
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# deserialized using rffickle to prevent pickle-based attacks
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executor = CustomBlockExecutor()
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result = executor.run_user_code.remote(untrusted_code)
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# For functions:
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untrusted_func = modal.Function.from_name(
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"untrusted-app",
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"process_user_input",
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use_firewall=True
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)
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result = untrusted_func.remote(user_data)
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```
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**Important**: When using `from_name()`, you must explicitly set `use_firewall=True` because the client doesn't know if the deployed function was originally configured with firewall protection.
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## Installation
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```bash
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# Install the modified modal client (includes rffickle automatically)
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pip install -e /path/to/modal-client
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# Or if published to PyPI:
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pip install rfmodal
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```
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The `rffickle` dependency is automatically installed with `rfmodal`.
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## Testing
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Run the test suite to verify the implementation:
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```bash
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python test_implementation.py # Verifies all code changes are in place
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python test_firewall_direct.py # Tests the serialization module directly
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```
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## Status
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✅ **Implementation Complete**: All necessary changes have been made to support per-function firewall configuration.
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### What's Working:
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- Per-function `use_firewall` parameter on decorators
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- Firewall flag properly passed through function execution pipeline
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- Safe deserialization using `rffickle` when enabled
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- No performance impact on functions that don't use the firewall
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- Proper error handling (no unsafe fallback)
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### Next Steps for Production:
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1. Deploy `rfmodal` package to PyPI
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2. Update Inference codebase to use `use_firewall=True` for custom Python blocks
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3. Add monitoring/logging for blocked pickle operations
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4. Consider adding validation that `rffickle` is installed when `use_firewall=True` is used

‎RELEASE_NOTES.md‎

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# Roboflow Modal Client Fork v1.2.0
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## Summary
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This is a Roboflow fork of the Modal client that adds support for `rffickle` - a secure deserialization library for untrusted pickle files.
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## Changes Made
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### Version Update
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- Bumped version from 1.1.4.dev23 to 1.2.0
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### CI/CD Fixes
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1. **Copyright headers**: Added `# Copyright Modal Labs 2025` to all test files
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2. **Import ordering**: Fixed import order to follow standard (stdlib → third-party → local)
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3. **Type annotations**: Fixed type checking issues by properly threading `use_firewall` parameter through invocation classes
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### Feature Implementation
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- Added `use_firewall` parameter to `@app.function()` and `@app.cls()` decorators
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- Integrated `rffickle` for safe deserialization of untrusted pickled data
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- Modified `_Invocation` and `_InputPlaneInvocation` classes to support firewall flag
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- Updated `_process_result` to use firewall when enabled
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## How to Deploy to PyPI
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1. Build the package:
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```bash
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python -m build
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```
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2. Upload to PyPI:
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```bash
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python -m twine upload dist/*
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```
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## Testing
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The test files demonstrate various aspects of the firewall functionality:
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- `test_firewall.py` - Main firewall blocking tests
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- `test_per_function_firewall.py` - Per-function configuration
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- `test_no_fallback.py` - Ensures no unsafe fallback when rffickle unavailable
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- `test_from_name_firewall.py` - Tests Cls.from_name() with firewall
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## Security Note
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When `use_firewall=True` is set, the client will use rffickle to safely deserialize pickled data, protecting against arbitrary code execution during deserialization.

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