Input validation and sanitization in Agentic AI

Introduction

Input validation and sanitization help make sure the data going into a machine learning system is safe and correct. This stops errors and keeps the system working well.

When receiving user data like text or numbers before using it in a model

When cleaning data from external sources to avoid bad or harmful input

Before feeding data into a machine learning pipeline to prevent crashes

When building AI agents that interact with users or other systems

When you want to avoid security risks like code injection or corrupted data

Syntax

Agentic AI

def validate_input(data):
    # Check if data meets rules
    if not isinstance(data, str):
        raise ValueError('Input must be a string')
    if len(data) == 0:
        raise ValueError('Input cannot be empty')
    return data

def sanitize_input(data):
    # Remove unwanted characters
    clean_data = data.strip().lower()
    return clean_data

Validation checks if input is the right type and format.

Sanitization cleans the input to remove or fix bad parts.

Examples

This checks if the input is a non-empty string and passes it through.

Agentic AI

validate_input('Hello World')

This removes spaces around the text and converts it to lowercase.

Agentic AI

sanitize_input('  Hello World!  ')

This raises an error because the input is not a string.

Agentic AI

validate_input(123)

Sample Model

This program checks a list of inputs. It validates each input to be a non-empty string, then cleans it by trimming spaces and making it lowercase. It prints the cleaned input or an error message.

Agentic AI

def validate_input(data):
    if not isinstance(data, str):
        raise ValueError('Input must be a string')
    if len(data) == 0:
        raise ValueError('Input cannot be empty')
    return data

def sanitize_input(data):
    clean_data = data.strip().lower()
    return clean_data

# Example usage
inputs = ['  Hello AI  ', '', 42, 'Goodbye!']
for i, item in enumerate(inputs):
    try:
        valid = validate_input(item)
        clean = sanitize_input(valid)
        print(f'Input {i}: Valid and sanitized -> "{clean}"')
    except ValueError as e:
        print(f'Input {i}: Error - {e}')

Important Notes

Always validate before sanitizing to catch wrong data early.

Sanitization depends on your use case; for example, removing HTML tags if needed.

Good input handling improves model reliability and security.

Summary

Input validation checks if data is the right type and format.

Input sanitization cleans data to remove unwanted parts.

Both steps help keep machine learning systems safe and working well.

Practice

1. What is the main purpose of input validation in machine learning systems?

easy

A. To train the model with new data

B. To clean the data by removing unwanted characters

C. To check if the input data is the correct type and format

D. To store data securely in a database

Solution

1. Step 1: Understand input validation

   Input validation means checking if the data is the right type and format before using it.

2. Step 2: Differentiate from sanitization

   Input sanitization cleans data, but validation focuses on correctness and format.

3. Final Answer:

   To check if the input data is the correct type and format -> Option C

4. Quick Check:

   Input validation = Check data type and format [OK]

Hint: Validation means checking data type and format [OK]

Common Mistakes:

• Confusing validation with sanitization
• Thinking validation trains the model
• Assuming validation stores data

2. Which of the following is the correct way to validate that an input is a positive integer in Python?

easy

A. if isinstance(input_value, int) and input_value > 0:

B. if type(input_value) == 'int' and input_value > 0:

C. if input_value.isdigit() and input_value > 0:

D. if input_value > 0:

Solution

1. Step 1: Check type correctly

   Use isinstance(input_value, int) to check if input is an integer.

2. Step 2: Check positivity

   Ensure the integer is greater than zero with input_value > 0.

3. Final Answer:

   if isinstance(input_value, int) and input_value > 0: -> Option A

4. Quick Check:

   Use isinstance and > 0 for positive integer check [OK]

Hint: Use isinstance() to check type, then compare value [OK]

Common Mistakes:

• Using type() == 'int' (wrong syntax)
• Calling isdigit() on non-string input
• Skipping type check before comparison

3. Given the code below, what will be the output?

def sanitize_input(text):
    return text.strip().lower()

user_input = '  Hello World!  '
cleaned = sanitize_input(user_input)
print(cleaned)

medium

A. Hello World!

B. !dlroW olleH

C. HELLO WORLD!

D. hello world!

Solution

1. Step 1: Understand strip()

   The strip() method removes spaces from the start and end of the string.

2. Step 2: Understand lower()

   The lower() method converts all letters to lowercase.

3. Final Answer:

   hello world! -> Option D

4. Quick Check:

   strip + lower = 'hello world!' [OK]

Hint: strip removes spaces, lower makes all letters small [OK]

Common Mistakes:

• Ignoring strip() effect on spaces
• Confusing lower() with upper()
• Expecting original casing in output

4. Identify the error in this input validation code snippet:

def validate_age(age):
    if age.isdigit() and age > 0:
        return True
    else:
        return False

medium

A. Comparing string with integer using > operator

B. Using isdigit() on a non-string type

C. Missing return statement in else block

D. Function name is invalid

Solution

1. Step 1: Check isdigit() usage

   isdigit() works on strings, so age should be a string here.

2. Step 2: Identify type mismatch in comparison

   Comparing age > 0 compares string to int, which causes error.

3. Final Answer:

   Comparing string with integer using > operator -> Option A

4. Quick Check:

   String > int comparison causes error [OK]

Hint: Check types before comparing values [OK]

Common Mistakes:

• Assuming isdigit() converts type
• Ignoring type mismatch in comparisons
• Thinking function name affects validation

5. You receive user data as a list of strings representing ages: ['25', ' 30', 'twenty', '40', '']. Which code snippet correctly validates and sanitizes this data to keep only valid positive integers?

hard

A. valid_ages = [age for age in ages if age.isdigit() and age > 0]

B. valid_ages = [int(age.strip()) for age in ages if age.strip().isdigit() and int(age.strip()) > 0]

C. valid_ages = [int(age) for age in ages if age.isnumeric()]

D. valid_ages = [int(age) for age in ages if age.strip() != '']

Solution

1. Step 1: Sanitize input by stripping spaces

   Use age.strip() to remove spaces before validation.

2. Step 2: Validate with isdigit() and positive check

   Check if stripped string is digits only and convert to int to check > 0.

3. Step 3: Convert valid strings to integers

   Use int(age.strip()) to convert valid strings to integers.

4. Final Answer:

   valid_ages = [int(age.strip()) for age in ages if age.strip().isdigit() and int(age.strip()) > 0] -> Option B

5. Quick Check:

   Strip spaces, check digits, convert to int > 0 [OK]

Hint: Strip spaces before isdigit(), then convert and check > 0 [OK]

Common Mistakes:

• Not stripping spaces before validation
• Comparing strings directly to numbers
• Including empty or non-digit strings