# Security Measures in AbstractLLM Tool Execution

This document outlines the security measures implemented in AbstractLLM to ensure safe tool execution.

## Overview

AbstractLLM follows an "LLM-First" architecture where all tool calls are initiated by the LLM, not directly by pattern matching on user input. However, even with this approach, proper security measures are essential because:

1. LLMs might be vulnerable to prompt injection attacks
2. Users could attempt to access files or resources outside allowed boundaries
3. Malicious inputs might be passed through the LLM to tools
4. Tools could execute potentially dangerous operations or consume excessive resources

## Security Features Implemented

### 1. Path Validation

All file operations are protected by path validation to prevent access to unauthorized directories:

```python
def is_safe_path(file_path: str, allowed_directories: List[str]) -> bool:
    """Check if a file path is within allowed directories."""
    abs_path = os.path.abspath(os.path.normpath(file_path))
    
    return any(
        os.path.commonpath([abs_path, allowed_dir]) == allowed_dir
        for allowed_dir in allowed_directories
    )
```

This prevents path traversal attacks such as `../../../etc/passwd`.

### 2. Tool Parameter Validation

All tool parameters are validated before execution:

```python
def validate_tool_parameters(tool_name: str, parameters: Dict[str, Any]) -> Tuple[bool, Optional[str]]:
    """Validate tool parameters for security."""
    # Tool-specific validation logic
    # ...
```

This prevents:
- Missing required parameters
- Invalid parameter types
- Values outside acceptable ranges
- Suspicious patterns in file paths

### 3. Execution Timeouts

All tool executions are protected with timeouts to prevent resource exhaustion:

```python
@contextmanager
def timeout(seconds):
    """Context manager for timing out function execution."""
    def timeout_handler(signum, frame):
        raise TimeoutError(f"Function execution timed out after {seconds} seconds")
    
    # Set up timeout using signals
    # ...
```

### 4. Secure Tool Wrappers

All tools are wrapped with secure wrappers that enforce:
- Parameter validation
- Execution timeouts
- Result sanitization
- Comprehensive logging

```python
def create_secure_tool_wrapper(func: Callable, max_execution_time: int = 5) -> Callable:
    """Create a wrapper that adds security measures to any tool function."""
    @functools.wraps(func)
    def secure_wrapper(*args, **kwargs):
        # Validate parameters
        # Execute with timeout
        # Sanitize results
        # ...
    return secure_wrapper
```

### 5. Output Sanitization

All tool outputs are sanitized to:
- Limit output size
- Redact sensitive information like SSNs and credit card numbers
- Provide clear truncation indicators

```python
def sanitize_tool_output(output: Any, tool_name: str) -> Any:
    """Sanitize tool outputs to prevent security issues."""
    # Size limits
    # Sensitive data redaction
    # ...
```

### 6. Configurable Security Settings

Security settings are configurable through the `TOOL_SECURITY_CONFIG` dictionary:

```python
TOOL_SECURITY_CONFIG = {
    "read_file": {
        "max_file_size": 10 * 1024 * 1024,  # 10MB
        "max_execution_time": 5,  # seconds
        "allowed_directories": [os.getcwd()],  # Current working directory
        "max_lines": 10000,  # Maximum number of lines to read
    }
}
```

### 7. Comprehensive Logging

All tool executions are logged with detailed information:
- Tool name
- Parameters
- Execution duration
- Results or errors

## Security Best Practices

When adding new tools to AbstractLLM, follow these best practices:

1. Always wrap tools with `create_secure_tool_wrapper`
2. Validate all inputs before processing
3. Handle exceptions gracefully with informative error messages
4. Set appropriate timeouts for each tool type
5. Define clear boundaries for allowed operations
6. Sanitize all outputs to avoid leaking sensitive information
7. Add comprehensive logging for auditing purposes

## Testing Security Measures

The security measures are verified by automated tests in `test_security.py`:

1. Path validation tests
2. Parameter validation tests
3. Timeout tests
4. Output sanitization tests
5. Resource limit tests

Run the tests with:
```bash
python test_security.py
```

## Future Enhancements

Planned security enhancements include:
1. Rate limiting for tool executions
2. User-based access controls
3. Enhanced sensitive data detection
4. More granular permission models
5. Tool-specific security policies

## Conclusion

The security measures implemented in AbstractLLM's tool execution system provide robust protection against common vulnerabilities. By following the LLM-First architecture and layering these security controls, we ensure that all tool calls are properly validated, executed within defined constraints, and sanitized before returning results. 