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venus 4dc0a24152 coming allong nicely. adding more commands, tests, and

artifacts for testing. basic framework down just adding features now

2026-07-18 02:38:13 -05:00

Project Status & Roadmap

This file tracks the completed progress and upcoming development milestones for the AI-Enhanced CTF Toolchain.

Current Status: Forensics Utilities Implemented 🚀

Project Structuring: Set up a modern Python src/ layout with uv as the package manager and hatchling as the build backend.
Config Management: Implemented basic TOML configuration loading and writing functions in utils.py using config.toml.
Directory Scanners: Implemented functions to list active competitions and categories automatically skipping helper folders like tools.
CLI Entry Points & Subcommands: Configured the Click main group in main.py to register nested subcommands properly.
Forensics Analysis (New): Implemented ctf forensics info and ctf forensics flag-search in forensics.py to inspect magic bytes/signatures, verify file extension matches, and search for flag pattern regular expressions.
Testing Environment: Established a sandbox folder at tests/env and implemented tests in test_utils.py validating the CLI command executions and boundary cases.
Documentation: Created the project ARCHITECTURE.md to define standard layouts, modules, and testing behavior.

Pipeline Automation: Integrate the existing forensics command-line utilities into a cohesive analysis pipeline where files are automatically checked for magic bytes, file extensions, and flag patterns.
Metadata & Extraction Toolkit: Extend forensics tools to extract specific metadata (e.g., EXIF records, archive tables) and automate extraction/carving of nested data structures (e.g., binwalk-like carving, automated unzipping, extraction of hidden payloads).
QOL Utilities: Add standard format outputs (JSON, Rich logs) and automatic logging of analysis artifacts to speed up user-led inspections.

Challenge Organization: Implement automated download management, challenge creation, directory configuration, and context management (set-challenge).
Solving Assistants: Build automated helper scripts for common solving needs (e.g., basic cryptography decoders, web request templates, PSK cracking utility integration).
Solve Tracker & Note-taking QOL: Create a command-line interface to capture solver actions, record active notes, log flag attempts, and update challenge statuses.
Per-Challenge Progress Logging: Design a challenge-specific progress logger that creates and maintains isolated log files for each active challenge in a specified workspace directory, tracking attempts, timestamps, solver notes, and command history.

Sandbox Environments: Prepare secure, isolated environments to run untrusted challenge scripts or binaries.
Agent Orchestration: Equip the toolchain with LLM agents capable of viewing the forensics pipeline outputs, reading challenge text, suggesting next steps, executing terminal tools, and recursively working to solve the challenge autonomously.