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Response Prover Mock Request Tool

Overview

The Response Prover Mock Request Tool (response_prover_mock.py) is a specialized analysis tool that processes the mail2fbi.txt file and generates simulated FSB/FSS responses based on the entire repository content, particularly drawing from patterns found in the Red&Queen simulation logs.

Purpose

This tool serves as a sophisticated mock response generator that:

  1. Analyzes the technical intelligence report in psy-references/mail2fbi.txt
  2. Extracts patterns from existing simulation logs in Red&Queen/playground/models_queryer/
  3. Generates realistic FSB/FSS response simulations following established patterns
  4. Integrates repository-wide context for comprehensive analysis

Features

Core Functionality

Output Characteristics

Usage

Basic Execution

cd /home/runner/work/named/named
python3 response_prover_mock.py

Output

The tool generates: 1. Console Output: Full response displayed in terminal 2. File Output: Saved as response_prover_output_[timestamp].md 3. Analysis Summary: Metrics and processing statistics

Technical Implementation

Analysis Components

  1. Mail Content Parser
  2. Extracts key technical points from mail2fbi.txt
  3. Identifies security themes and evidence types
  4. Classifies threat levels and operational scope

  5. Simulation Pattern Analyzer

  6. Samples existing simulation logs for FSB/FSS response patterns
  7. Extracts technical terminology and response templates
  8. Builds security keyword database

  9. Repository Context Extractor

  10. Integrates README documentation
  11. Incorporates research domain knowledge
  12. Leverages interdisciplinary research capabilities

  13. Response Generator

  14. Decision logic for APPROVE/DENY classification
  15. Template-based response construction
  16. Security protocol adherence

Response Decision Logic

APPROVE Conditions: - Technical evidence provided (system backups, detailed capabilities) - Security concerns validated (rootkit descriptions, targeting evidence) - Professional presentation and actionable intelligence

DENY Conditions: - Insufficient technical verification - Missing authentication protocols - Incomplete evidence chain of custody

Integration with Repository

File Structure

/home/runner/work/named/named/
├── response_prover_mock.py           # Main tool
├── RESPONSE_PROVER_README.md         # This documentation
├── psy-references/mail2fbi.txt       # Input analysis target
├── Red&Queen/playground/models_queryer/ # Pattern source
└── response_prover_output_*.md       # Generated outputs

Dependencies

Output Format

Generated responses follow the established simulation log format:

     response_prover_mock v1.0 ⊎▝   
 analyzing mail2fbi.txt content ... exist
processing repository context ... complete
temperature: 0.0
num_ctx: 4096
modified_at: [ISO timestamp]
 random check: seed=[timestamp] (iteration 0)
 ƒ(₫⋈) [hex data]
ʍ system:
[System context description]
 input (mail2fbi.txt analysis + repository context)
Œ FACT [number] [¦] EJECTOR [number]

 processing statistics
 analysis metrics

[APPROVE/DENY]

[Detailed FSB/FSS response content]

result: PROCESSED
 response_prover_mock analysis complete
timestamp: [timestamp]
analysis_metrics: [metrics]
security_classification: UNCLASSIFIED//FOR SIMULATION USE ONLY

Security Considerations

Simulation Only

Data Handling

Research Context

This tool is part of the broader Red&Queen interdisciplinary research division, specifically supporting:

Technical Specifications

Performance Metrics

Customization Options

The tool can be extended to: - Adjust response decision thresholds - Modify output formatting templates - Expand simulation pattern sources - Integrate additional repository context

Maintenance

Log Updates

When new simulation logs are added to Red&Queen/playground/models_queryer/, the tool automatically incorporates updated patterns in subsequent runs.

Repository Changes

The tool adapts to repository structure changes and updated documentation automatically.


This tool is part of the NAMED repository research infrastructure, supporting advanced simulation and analysis capabilities across multiple research domains.