Sriram Sriram

Posted on Feb 8

Building an HTTP Header Analyser in Python:

#webdev #security #python #opensource

HTTP headers are one of the most overlooked components of web security. They quietly dictate how browsers behave, how data is cached, and how resilient an application is against common attack vectors — yet they’re often ignored until something breaks.

This post walks through the design, architecture, and documentation philosophy behind a Python-based HTTP Header Analyser: a focused CLI tool that converts raw HTTP response metadata into actionable security and performance insights.

Why HTTP Headers Matter More Than Most Teams Realize

HTTP headers act as policy enforcers between a client and a server. They control everything from script execution to transport security and caching behavior.

Headers such as:

Content-Security-Policy (CSP) — mitigates XSS and script injection
Strict-Transport-Security (HSTS) — enforces HTTPS and prevents downgrade attacks
X-Frame-Options — protects against clickjacking

When these are missing or misconfigured, applications become unnecessarily exposed.

The practical problem is this:

Manual inspection via browser developer tools is slow and inconsistent
Full vulnerability scanners are often heavy, intrusive, or unsuitable for CI

This creates a gap for a lightweight, automation-friendly diagnostic tool — and that’s exactly what this project targets.

Introducing the HTTP Header Analyser

The HTTP Header Analyser is a Python-based command-line diagnostic utility designed to retrieve, evaluate, and score HTTP response headers.

Instead of merely listing headers, it analyzes them across security, information disclosure, and performance dimensions, then presents the findings in formats suitable for both humans and machines.

Core capabilities

Parallel scanning of multiple URLs
JSON-structured reporting for CI/CD integration
Clear, colorized terminal output using Rich

It intentionally sits between manual inspection and full-scale scanners — fast, passive, and safe to run against production systems.

What the Tool Analyses

To provide a balanced and practical assessment, the analyser evaluates headers across four primary domains.

1. Security Configuration Analysis

The tool checks for the presence and configuration of key security headers, including:

Content-Security-Policy
Strict-Transport-Security
X-Frame-Options
X-Content-Type-Options

Missing or weak configurations are flagged to highlight potential security gaps.

2. Information Leakage Detection

Certain headers unintentionally expose backend details that attackers can leverage during reconnaissance. The analyser identifies common leakage points such as:

Server — web server software and version details
X-Powered-By — framework or language indicators

3. CORS Policy Validation

Overly permissive Cross-Origin Resource Sharing (CORS) policies are a frequent misconfiguration. The tool flags dangerous patterns like:

Access-Control-Allow-Origin: *

Such configurations can expose sensitive resources to untrusted origins.

4. Caching and Performance Policies

By examining Cache-Control and Expires headers, the analyser detects misconfigurations that may:

Serve stale content
Cache sensitive responses
Negatively impact application performance

This ensures the analysis remains both security-aware and operationally relevant.

Architecture: How the Tool Is Built

The project follows a modular architecture, keeping responsibilities clearly separated and the codebase easy to extend.

Core modules

cli.py

Command-line entry point. Handles argument parsing, validation, and execution flow.
requester.py

Manages HTTP/HEAD requests, URL normalization, redirects, and timeout handling.
analyzer.py

The analysis engine. Evaluates headers against predefined security, CORS, and caching rules.
reporter.py

Responsible solely for output — formatted terminal views and JSON serialization.
schemas.py

Defines structured data models to keep reports consistent across CLI, API, and frontend use.
utils.py

Shared helper utilities used across the codebase.

This separation improves testability, readability, and long-term maintainability.

Project Structure and Design Decisions

The repository is organized to reflect real-world engineering discipline, not just “what works.”


http-header-analyser-using-python/
├─ src/        → Core analysis engine
├─ frontend/   → Optional visualization layer
├─ tests/      → Unit and integration tests
├─ assets/     → Documentation visuals
├─ report/     → Sample JSON outputs

Why this structure matters

Core logic is isolated from UI and deployment concerns
Output is reusable across CLI, CI pipelines, and dashboards
Tests ensure reliability — critical for any security-related tool

This structure allows the project to scale without rewriting its foundation.

Frontend Visualization Layer

In addition to the CLI, the project includes a lightweight frontend dashboard that consumes JSON reports and renders them visually.

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Purpose of the frontend

Demonstrates that the JSON output is well-structured
Enables non-CLI users to interpret reports
Proves compatibility with dashboards and monitoring tools

This elevates the project beyond a CLI-only utility.

CLI Features and Operational Flexibility

The analyser is designed to fit naturally into both local workflows and automation pipelines.

Key CLI options

Single or multi-URL scanning
--parallel for concurrent analysis
--json <filename> for structured output
--no-redirect to inspect initial responses
--timeout <seconds> to prevent hanging requests

Deployment options

Local execution using Python virtual environments
Containerized execution via Docker with volume mounting

This makes the tool portable, reproducible, and CI-friendly.

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