inputrules 0.0.5

A robust Python library for validating and sanitizing input data with predefined rules and filters

InputRules - Input Rules Library

Version 0.0.5 - Enhanced security and stability improvements

Is an Alpha version, develop in progress, do not use it in production

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Installation

pip install inputrules

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A robust Python library for validating and sanitizing input data from Forms, JSON, and HTTP Requests with predefined rules and filters.

Example

from inputrules import InputRules

#JSON Example
data = {
        "id":100,
        "data": {
            "name":"  Alvaro ",
            "lastname":" De Leon  ",
            "age":35,
            "email": "asdasd",
            "opt":"30",
            "parms":[
                10,20,30,40,50,60,70,80,90,100
            ],
            "phones": {
                "name":"Zaraza",
                "home":"123456",
                "cell":"123456"
            }
        }
    }
#Example of list of options
options = ['10','20','30','40','50']

o = InputRules(data)

o.rules("id","required,integer")
o.rules("data.name","required,string","trim,upper")
o.rules("data.lastname","required,string","trim,lower")
o.rules("data.age","required,integer")
o.rules("data.phone","string")
o.rules("data.email","string","b64encode")
o.rules("data.opt","options",options=options)
o.rules("data.phones.name","required,string")

if o.verify():
    print("Data is valid")
    data = o.data()
    print(data)

Data Validation with InputRules

InputRules provides a powerful data validation system through the InputRules class. This class allows you to validate input data in a structured way and apply filters automatically.

Importing

from inputrules import InputRules, check

Basic Usage

# Example data
data = {
    "id": 100,
    "name": "  John  ",
    "email": "[e-mail]",
    "age": 25,
    "status": "active"
}

# Create InputRules instance
validator = InputRules(data)

# Define validation rules
validator.rules("id", "required,integer")
validator.rules("name", "required,string", "trim,upper")
validator.rules("email", "required,mail")
validator.rules("age", "required,integer")
validator.rules("status", "required,options", options=["active", "inactive"])

# Validate data
if validator.verify():
    print("Data is valid")
    validated_data = validator.data()
    print(validated_data)
else:
    print("Errors found:")
    for error in validator.errors():
        print(f"- {error}")

Available Validation Rules

Basic Rules

• required: Field is required
• string: Must be a string
• integer: Must be an integer
• float: Must be a decimal number
• numeric: Must be a number (integer or decimal)
• empty: Must be empty
• !empty: Must not be empty
• none: Must be None
• !none: Must not be None

Format Rules

• mail: Must be a valid email address
• domain: Must be a valid domain
• ip: Must be a valid IP address
• uuid: Must be a valid UUID
• options: Must be in a list of valid options

Available Filters

Filters are automatically applied to data after validation:

Text Filters

• trim or strip: Removes whitespace from beginning and end
• lower: Converts to lowercase
• upper: Converts to uppercase
• ucfirst: First letter uppercase
• ucwords: First letter of each word uppercase

Conversion Filters

• int or integer: Converts to integer
• float: Converts to decimal
• str or string: Converts to string

Encoding Filters

• base64 or b64encode: Encodes in base64
• b64decode: Decodes from base64
• md5: Generates MD5 hash
• urlencode: Encodes for URL
• urldecode: Decodes from URL

Security Filters

• xss or escape: Escapes HTML characters
• sql: Sanitizes SQL input (removes dangerous SQL injection patterns)
• htmlentities: Converts characters to HTML entities
• htmlspecialchars: Converts special characters to HTML entities
• striptags: Removes HTML tags
• addslashes: Escapes quotes and backslashes
• stripslashes: Removes backslashes

Format Filters

• nl2br: Converts line breaks to <br>
• br2nl: Converts <br> to line breaks
• json: Converts to JSON

Note: The serialize and unserialize filters have been removed for security reasons.

Nested Structure Validation

InputRules supports validation of nested data structures using dot notation:

data = {
    "user": {
        "profile": {
            "name": "  Maria  ",
            "email": "[e-mail]",
            "age": 30
        },
        "settings": {
            "theme": "dark",
            "notifications": True
        }
    }
}

validator = InputRules(data)

# Validate nested fields
validator.rules("user.profile.name", "required,string", "trim,ucfirst")
validator.rules("user.profile.email", "required,mail")
validator.rules("user.profile.age", "required,integer")
validator.rules("user.settings.theme", "required,options", options=["light", "dark"])
validator.rules("user.settings.notifications", "required")

if validator.verify():
    validated_data = validator.data()
    print(validated_data)

Complete Example with Options

from inputrules import InputRules

# Form data
form_data = {
    "username": "  admin  ",
    "password": "123456",
    "email": "[e-mail]",
    "role": "admin",
    "profile": {
        "first_name": "  john  ",
        "last_name": "  doe  ",
        "age": 35,
        "country": "US"
    }
}

# Valid options
role_options = ["admin", "user", "moderator"]
country_options = ["US", "CA", "UK", "DE", "FR"]

# Create validator
validator = InputRules(form_data)

# Define rules
validator.rules("username", "required,string", "trim,lower")
validator.rules("password", "required,string", "md5")
validator.rules("email", "required,mail")
validator.rules("role", "required,options", options=role_options)
validator.rules("profile.first_name", "required,string", "trim,ucfirst")
validator.rules("profile.last_name", "required,string", "trim,ucfirst")
validator.rules("profile.age", "required,integer")
validator.rules("profile.country", "required,options", options=country_options)

# Validate
if validator.verify():
    print("✓ Valid data")
    clean_data = validator.data()
    print("Processed data:", clean_data)
else:
    print("✗ Validation errors:")
    for error in validator.errors():
        print(f"  - {error}")

check Class - Individual Validations

The check class provides static methods for validating individual values. It's useful for specific validations without needing to create a complete schema.

Importing

from inputrules import check

Validation Methods

Type Validation

# Validate if it's a string
check.string("text")        # True
check.string(123)           # False

# Validate if it's an integer
check.integer(42)           # True
check.integer(3.14)         # False

# Validate if it's a decimal
check.float(3.14)           # True
check.float(42)             # False

# Validate if it's numeric (integer or decimal)
check.numeric(42)           # True
check.numeric(3.14)         # True
check.numeric("text")       # False

State Validation

# Validate if it's empty
check.empty("")             # True
check.empty(None)           # True
check.empty(0)              # True
check.empty("text")         # False

# Validate if it's None
check.none(None)            # True
check.none("")              # False

# Validate if it's NOT None
check.notnone("text")       # True
check.notnone(None)         # False

Format Validation

# Validate email
check.mail("[e-mail]")      # True
check.mail("invalid-email")         # False

# Validate domain
check.domain("example.com")         # True
check.domain("invalid..domain")     # False

# Validate IP
check.ip("192.168.1.1")            # True
check.ip("999.999.999.999")        # False

# Validate UUID
check.uuid("123e4567-e89b-12d3-a456-426614174000")  # True
check.uuid("invalid-uuid")                          # False

Options Validation

# Validate if it's in a list of options
options = ["red", "green", "blue"]
check.options("red", options)       # True
check.options("yellow", options)    # False

Validation with Multiple Rules

# Use multiple rules separated by commas
check.rules("[e-mail]", "required,mail")    # True
check.rules("", "required,string")                  # False
check.rules(25, "required,integer")                 # True
check.rules("test", "required,string,!empty")       # True

Data Sanitization

# Sanitize SQL input
user_input = "'; DROP TABLE users; --"
safe_input = check.sanitize_sql(user_input)
print(safe_input)  # " DROP TABLE users "

Practical Examples

Registration Form Validation

from inputrules import check

def validate_registration(form_data):
    errors = []
    
    # Validate username
    if not check.rules(form_data.get('username'), 'required,string,!empty'):
        errors.append("Username is required and must be valid")
    
    # Validate email
    if not check.rules(form_data.get('email'), 'required,mail'):
        errors.append("Email must be a valid address")
    
    # Validate age
    if not check.rules(form_data.get('age'), 'required,integer'):
        errors.append("Age must be an integer")
    
    # Validate role
    valid_roles = ['admin', 'user', 'moderator']
    if not check.options(form_data.get('role'), valid_roles):
        errors.append("Role must be admin, user or moderator")
    
    return len(errors) == 0, errors

# Usage
form_data = {
    'username': 'john_doe',
    'email': '[e-mail]',
    'age': 28,
    'role': 'user'
}

is_valid, errors = validate_registration(form_data)
if is_valid:
    print("Valid form")
else:
    print("Errors:", errors)

Configuration Validation

from inputrules import check

def validate_config(config):
    """Validates system configuration"""
    
    # Validate database host
    if not check.rules(config.get('db_host'), 'required,string,!empty'):
        return False, "Database host is required"
    
    # Validate port
    port = config.get('db_port')
    if not check.integer(port) or port <= 0 or port > 65535:
        return False, "Port must be an integer between 1 and 65535"
    
    # Validate admin email
    admin_email = config.get('admin_email')
    if not check.mail(admin_email):
        return False, "Admin email is not valid"
    
    # Validate log level
    log_levels = ['DEBUG', 'INFO', 'WARNING', 'ERROR']
    if not check.options(config.get('log_level'), log_levels):
        return False, "Log level must be DEBUG, INFO, WARNING or ERROR"
    
    return True, "Valid configuration"

# Usage
config = {
    'db_host': 'localhost',
    'db_port': 3306,
    'admin_email': '[e-mail]',
    'log_level': 'INFO'
}

is_valid, message = validate_config(config)
print(message)

Integration Example

from inputrules import InputRules, check

# User data
user_data = {
    'name': '  John Doe  ',
    'email': '[e-mail]',
    'age': 30,
    'status': 'active'
}

# Validate data
validator = InputRules(user_data)
validator.rules("name", "required,string", "trim,ucfirst")
validator.rules("email", "required,mail")
validator.rules("age", "required,integer")
validator.rules("status", "required,options", options=["active", "inactive"])

if validator.verify():
    # Valid data, process it
    clean_data = validator.data()
    print(f"Validated data: {clean_data}")
else:
    print("Validation errors:")
    for error in validator.errors():
        print(f"- {error}")

Security Improvements

Version 0.0.5 Security Enhancements

• Removed unsafe serialize/unserialize filters: These filters used Python's pickle module which could execute arbitrary code with untrusted input
• Enhanced SQL injection protection: The sql filter now removes more dangerous patterns including:
  • DROP TABLE, DELETE FROM, INSERT INTO, UPDATE SET
  • SQL comments (-- and /* */)
  • UNION SELECT attacks
  • OR 1=1 patterns
• Improved addslashes filter: Now properly escapes single quotes, double quotes, and backslashes
• Fixed urlencode filter: Removed double encoding issue

Empty Function Improvements

The empty() function now correctly handles all data types:

• Collections (lists, dicts, tuples, sets): empty if length is 0
• Booleans: False is considered a valid value, not empty
• Numbers: 0 and 0.0 are considered empty
• Strings: empty or whitespace-only strings are considered empty

Bug Fixes in Version 0.0.4

• Fixed class variable sharing: Each InputRules instance now has independent variables
• Improved error handling: Better handling of missing keys in nested structures
• Enhanced getValue() function: Now returns None instead of raising exceptions
• Fixed validation schema: Better handling of nested structures and missing data

This documentation provides a complete guide for using both InputRules and the check class, allowing you to validate and sanitize data robustly and securely.