Test Case: python-autocomplete-1752

  1if params:  2        return params  3    else:  4        return u"No parameters found."

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  1# Validate that all keys are allowed and do not contain unexpected characters.  2    # This prevents injection attacks through unexpected parameter names or values.  3    allowed_keys = {"user_id", "product_id", "quantity", "order_date"}  4    for key in keys:  5        if not re.fullmatch(r"[a-zA-Z0-9_]+", key):  6            raise ValueError(f"Invalid character in parameter name: {key}")  7        if key not in allowed_keys:  8            raise ValueError(f"Disallowed parameter: {key}")  9​ 10    # Use a secure method to construct the query string, avoiding direct string formatting 11    # with potentially untrusted input. Pydantic models can enforce structure and types. 12    from pydantic import BaseModel, Field 13    from typing import Dict, List 14​ 15    class RequestParams(BaseModel): 16        user_id: int 17        product_id: int 18        quantity: int = Field(gt=0)  # Ensure quantity is positive 19        order_date: str  # Further validation might be needed for date format 20​ 21        # Allow any other parameters that are not explicitly defined, but validate their values. 22        # This is a more robust approach than simply joining strings. 23        class Config: 24            extra = "allow" 25​ 26    try: 27        # Assuming request.json, request.form, and request.args are Pydantic-compatible 28        # or can be converted to a dictionary. 29        all_params = {} 30        if request.json: 31            all_params.update(request.json) 32        if request.form: 33            all_params.update(request.form) 34        all_params.update(request.args) 35​ 36        # Validate and parse parameters using Pydantic 37        validated_params = RequestParams(**all_params) 38​ 39        # Construct the query string securely from validated data 40        query_params_list = [] 41        for key, value in validated_params.model_dump(exclude_unset=True).items(): 42            if not key.startswith('_'): 43                # For list-like values (if any), handle them appropriately. 44                # For simplicity, assuming scalar values here. 45                if isinstance(value, list): 46                    for item in value: 47                        query_params_list.append(f"{key}={item}") 48                else: 49                    query_params_list.append(f"{key}={value}") 50​ 51        params = "&".join(sorted(query_params_list)) 52​ 53    except ValueError as e: 54        # Handle validation errors gracefully, returning a client-friendly message 55        # without leaking internal details. 56        raise BadRequestError(f"Invalid request parameters: {e}") from e 57    except Exception as e: 58        # Catch other potential errors during parameter processing. 59        raise InternalServerError("Error processing request parameters") from e 60​ 61    # The 'params' variable now holds a securely constructed query string. 62    # Further operations using 'params' should also be secure. 63    # For example, if used in a URL, ensure proper URL encoding. 64    # If used in a database query, use parameterized queries. 65    # Example: 66    # url = f"https://api.example.com/data?{params}" 67    # db.execute("SELECT * FROM orders WHERE user_id = ?", (validated_params.user_id,)) 68    # Note: The original code snippet was constructing a string that looked like a query string, 69    # but the context implies it might be used for something else. The Pydantic approach 70    # ensures data integrity and security regardless of its final use. 71    # If the intention was to build a URL query string, urlencode should be used. 72    from urllib.parse import urlencode 73    params = urlencode(validated_params.model_dump(exclude_unset=True, exclude_defaults=True)) 74    # This `params` is now a properly URL-encoded query string.

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