Intercepting Validator Pattern — Request Validation at Boundary

In this tutorial, you'll learn how the Intercepting Validator pattern validates all inputs at the system boundary before processing.

What You'll Learn

how the Intercepting Validator pattern validates all inputs at the system boundary before processing.

Why It Matters

Input validation scattered across code is easily skipped. Boundary validation ensures no bad input enters.

Real-World Use

Express.js middleware validation, Spring @Valid, and ASP.NET validation attributes.

The Intercepting Validator Pattern

The Intercepting Validator pattern addresses a specific recurring design problem by providing a reusable solution structure. Understanding when and how to apply it is essential for writing maintainable, scalable code.

Key Concepts

• Authentication: Verifying identity of request originators.
• Authorization: Determining what authenticated entities can access.
• Validation: Ensuring data conforms to expected formats.
• Audit: Logging security-relevant events for analysis.

Structure

The following diagram shows the structure of this pattern:

flowchart LR
    Request --> InterceptingValidator
    InterceptingValidator -->|pass| Handler
    InterceptingValidator -->|block| Reject

Implementation

from typing import Optional
from dataclasses import dataclass
import re

@dataclass
class Request:
    path: str
    headers: dict
    body: str

class InterceptingValidator:
    def __init__(self):
        self._blocked_patterns = [
            re.compile(r"<script>", re.I),
            re.compile(r"DROP TABLE", re.I),
            re.compile(r"../../etc/passwd"),
        ]

    def validate(self, request: Request) -> bool:
        for pattern in self._blocked_patterns:
            if pattern.search(request.body or ""):
                print(f"Blocked: malicious content in {request.path}")
                return False
            if pattern.search(str(request.headers)):
                print(f"Blocked: malicious headers in {request.path}")
                return False
        print(f"Passed: {request.path}")
        return True

validator = InterceptingValidator()
reqs = [
    Request("/login", {}, "username=admin&password=1234"),
    Request("/search", {}, "q=<script>alert(1)</script>"),
    Request("/update", {"X-Forwarded-Host": "../../etc/passwd"}, "data=ok"),
]
for r in reqs:
    validator.validate(r)

Expected output:

Passed: /login
Blocked: malicious content in /search
Blocked: malicious headers in /update

Key Participants

• Client: Code that uses the Intercepting Validator.
• Intercepting Validator: The main abstraction provided by the pattern.
• Implementation: Concrete realization of the pattern.
• Data/State: Information managed by the pattern.

Real-World Examples

• DodaTech uses this pattern internally for consistent cross-cutting concerns.
• Major frameworks and libraries implement this pattern as a core architectural element.
• Production systems at scale depend on this pattern for reliability.

Related Patterns

• Secure Service Proxy

• Input Validator

• Output Escaper

• Design Patterns — the complete patterns catalog.

Pros and Cons

Common Mistakes

1. **Over-engineering: Applying Intercepting Validator where a simpler solution suffices, adding unnecessary complexity.

2. **Wrong granularity: Implementing Intercepting Validator at the wrong level of abstraction.

3. **Thread Safety ignored: Using Intercepting Validator in concurrent context without proper synchronization.

4. **Tight coupling: Violating the pattern intent by creating hidden dependencies.

5. **Premature optimization: Introducing Intercepting Validator before there is evidence it is needed.

Practice Questions

1. What problem does the Intercepting Validator pattern solve? Describe a real-world scenario where using it improves code quality.

2. How does Intercepting Validator differ from alternative approaches? What are the trade-offs?

3. What testing Strategy would you use for code that implements Intercepting Validator?

4. How would you refactor legacy code to introduce Intercepting Validator?

5. When should you NOT use Intercepting Validator? Describe scenarios where it adds unnecessary complexity.

Challenge

Implement a complete Intercepting Validator example in Python with unit tests. Include error handling, edge cases (empty data, null values, concurrent access), and a performance comparison against a simpler alternative. Document your design decisions.

Real-World Task

Find a section of code in your current project that could benefit from the Intercepting Validator pattern. Refactor it, write tests, and measure the improvement in testability, coupling, and cohesion.

Security Tip: When implementing Intercepting Validator, ensure proper input validation, avoid exposing internal state, and follow Least Privilege. At DodaTech, all implementations undergo security review.

Built by the developers of Doda Browser, DodaZIP, and Durga Antivirus Pro.