This commit implements a Markov Chain-based feedback mechanism for ffuf that enables adaptive fuzzing based on response patterns. The system learns from HTTP response characteristics and adjusts input selection strategy in real-time.

Key features added:

• Markov Chain implementation tracks response states (status code, content length, word count, etc.)
• Adaptive input selection that prioritizes inputs leading to interesting responses
• Thread-safe implementation with proper synchronization to prevent race conditions
• Verbose parameter display showing Markov Chain statistics during execution
• Integration with existing ffuf job execution flow maintaining full backward compatibility

The Markov Chain feedback operates by:

1. Categorizing responses into states based on characteristics
2. Learning transition probabilities between states
3. Adapting input selection based on learned patterns
4. Displaying real-time statistics when verbose mode is enabled

Files added/modified:

• pkg/ffuf/markov_impl.go: Core Markov Chain implementation
• pkg/ffuf/markov_interface.go: Markov Feedback interface
• pkg/ffuf/job.go: Integration with job execution flow
• pkg/ffuf/markov_test.go: Tests for Markov functionality
• README.md: Documentation update
• TESTING.md: Testing instructions
• test/test_server.go: Test server for demonstrating functionality
• test_wordlist.txt: Comprehensive test wordlist
• test_markov.sh: Test script

The implementation passes all existing tests and demonstrates the adaptive behavior during real fuzzing operations.

Description

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If this PR addresses an existing issue, please add the issue number below.

Fixes: #(issue number)

Additonally

• If this is the first time you are contributing to ffuf, add your name to CONTRIBUTORS.md.
  The file should be alphabetically ordered.
• Add a short description of the fix to CHANGELOG.md

Thanks for contributing to ffuf :)