36 lines
3.5 KiB
Markdown
36 lines
3.5 KiB
Markdown
# Project Status & Roadmap
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This file tracks the completed progress and upcoming development milestones for the AI-Enhanced CTF Toolchain.
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---
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## Current Status: Forensics Utilities Implemented 🚀
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### Progress Made So Far
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1. **Project Structuring**: Set up a modern Python `src/` layout with `uv` as the package manager and `hatchling` as the build backend.
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2. **Config Management**: Implemented basic TOML configuration loading and writing functions in [utils.py](file:///home/venus/code/ctf/src/ctf/utils.py) using [config.toml](file:///home/venus/code/ctf/config.toml).
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3. **Directory Scanners**: Implemented functions to list active competitions and categories automatically skipping helper folders like `tools`.
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4. **CLI Entry Points & Subcommands**: Configured the Click main group in [main.py](file:///home/venus/code/ctf/src/ctf/main.py) to register nested subcommands properly.
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5. **Forensics Analysis (New)**: Implemented `ctf forensics info` and `ctf forensics flag-search` in [forensics.py](file:///home/venus/code/ctf/src/ctf/forensics.py) to inspect magic bytes/signatures, verify file extension matches, and search for flag pattern regular expressions.
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6. **Testing Environment**: Established a sandbox folder at [tests/env](file:///home/venus/code/ctf/tests/env) and implemented tests in [test_utils.py](file:///home/venus/code/ctf/tests/test_utils.py) validating the CLI command executions and boundary cases.
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7. **Documentation**: Created the project [ARCHITECTURE.md](file:///home/venus/code/ctf/ARCHITECTURE.md) to define standard layouts, modules, and testing behavior.
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---
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## Upcoming Milestones & Features
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### 📅 Phase 1: Functional Forensics Pipeline & Toolkit
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* **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.
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* **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).
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* **QOL Utilities**: Add standard format outputs (JSON, Rich logs) and automatic logging of analysis artifacts to speed up user-led inspections.
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### 📅 Phase 2: Core CTF Solving & Solver QOL
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* **Challenge Organization**: Implement automated download management, challenge creation, directory configuration, and context management (`set-challenge`).
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* **Solving Assistants**: Build automated helper scripts for common solving needs (e.g., basic cryptography decoders, web request templates, PSK cracking utility integration).
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* **Solve Tracker & Note-taking QOL**: Create a command-line interface to capture solver actions, record active notes, log flag attempts, and update challenge statuses.
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* **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.
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### 📅 Phase 3: Agentic Solving Capabilities
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* **Sandbox Environments**: Prepare secure, isolated environments to run untrusted challenge scripts or binaries.
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* **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.
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