107 lines
7.7 KiB
Markdown
107 lines
7.7 KiB
Markdown
# Project Architecture: AI-Enhanced CTF Toolchain
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This document describes the architecture, directory layout, core modules, testing setup, and planned components of the CTF Toolchain project.
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---
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## 1. Directory Structure
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The project follows a standard modern Python layout (utilizing `src/` directory layout) and is managed via the `uv` toolchain.
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```text
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├── config.toml # Mock/Default configuration for local testing
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├── pyproject.toml # Hatchling build configuration & project dependencies
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├── uv.lock # uv lockfile for exact dependency resolution
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├── src/
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│ └── ctf/
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│ ├── __init__.py # Module initializer
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│ ├── main.py # CLI Entry Point
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│ ├── commands.py # CLI Commands and action functions
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│ ├── utils.py # Core utility functions (file parsing, config, paths)
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│ └── forensics.py # Forensics analysis tools
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└── tests/
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├── env/ # Sandboxed, persistent test environment directories
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└── test_utils.py # Unit/Integration tests for utility functions
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```
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---
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## 2. Core Modules & Configuration
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### A. Configuration & State ([config.toml](file:///home/venus/code/ctf/config.toml))
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The toolchain requires configuration of directories and active challenges:
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* `Competition`: Keeps track of the current `producer`, `competition`, `category`, and `challenge`.
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* `Environment`: Declares the base directory (`ctf_dir`) where all CTF files are stored.
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### B. Utilities ([utils.py](file:///home/venus/code/ctf/src/ctf/utils.py))
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Provides helper functions for filesystem management and configuration parsing:
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* `load_config(path)`: Loads and parses configuration data from a TOML file.
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* `write_config(data, path)`: Serializes/updates a dictionary to a TOML file.
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* `active_categories(path)`: Iterates over a competition path to return all active categories.
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* `active_competitions(dir)`: Scans the base directory for active competitions, skipping designated helper directories (like `tools`).
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* `suggest_patterns(flag, comp_name)`: Analyzes a provided sample flag string and generates a prioritized, deduplicated list of regular expression pattern candidates, optionally incorporating the competition name.
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### C. Commands ([commands.py](file:///home/venus/code/ctf/src/ctf/commands.py))
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Houses the logic for generic CLI context and active competition commands:
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* `Set_Challenge(comp, chal, setDirectory)`: Sets the current active challenge/competition context. *(Note: Currently has a `NameError` due to reference to an undefined `state` object.)*
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* `set-competition (name)`: Sets the name of the active competition in configuration.
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* `set-flag-format (pattern)`: Sets the flag regex format in configuration. Supports optional `PATTERN` positional argument or an interactive choice using `-o`/`--original` which takes a sample flag and prompts the user to select from suggested regex patterns, warning if the competition name is not found in the sample.
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### D. Forensics ([forensics.py](file:///home/venus/code/ctf/src/ctf/forensics.py))
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Implements specialized forensic inspection utilities registered as a nested subgroup under the CLI:
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* `info`: Inspects target file sizes, reads magic bytes, and warns if extensions do not match detected signatures.
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* `flag-search`: Extracts printable string sequences (equivalent to GNU `strings`) and matches them against regular expression patterns to find potential flags.
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---
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## 3. CLI Entry Point ([main.py](file:///home/venus/code/ctf/src/ctf/main.py))
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* Serves as the central CLI entry point via the `main()` function.
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* Initializes the root Click `cli` group and registers nested sub-groups, such as `forensics_group`.
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---
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## 4. Test Infrastructure
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* **Framework**: `pytest` (run via `uv run pytest`).
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* **Sandbox**: [tests/env](file:///home/venus/code/ctf/tests/env) acts as a persistent mock directory tree containing temporary competition directories (like `comp1`, `comp2`) and mock files (e.g. valid PNGs, mismatching PDFs, text files with flag payloads) to safely test scanning, parsing, and CLI command execution without touching actual user data.
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---
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## 5. Planned Architectural Components
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### A. Forensics Pipeline & Extended Metadata
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* **Pipeline Coordination**: A central runner that coordinates file inspections, signature verification, and string searches sequentially on target files.
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* **Carving & Extraction Toolkit**: Deep inspection and carving utilities (such as archive extracting, custom chunk/payload carvers, and EXIF/metadata header decoders).
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### B. Core Solving Infrastructure & QOL
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* **Solving Assistants**: Standard templates and modular helper scripts for repeating CTF patterns (e.g., base conversions, cipher decoding, and request generation).
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* **Context & Progress Management**: Full CLI commands to organize active challenges, automate note tracking, and manage workspace folders dynamically.
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* **Per-Challenge Progress Logging**: A logging interface that creates separate progress logs for each challenge inside a configurable directory, tracking actions, milestones, attempts, and command history.
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### C. Agentic Solving Orchestration
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* **Action Space & Sandboxing**: API boundaries and isolated docker/sandbox execution paths to run unknown commands and binaries safely.
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* **Autonomous Solving Agent**: Integration with LLM controllers that parse output from the forensics pipeline, suggest plans, iterate on solving steps, and verify flag captures.
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---
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## 6. CLI Data Flow & Presentation Guidelines
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### A. Model-View Separation
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All CLI command modules (such as [forensics.py](file:///home/venus/code/ctf/src/ctf/forensics.py)) must separate data extraction logic from command-line rendering.
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* **Data Models**: Standard Python `@dataclass` objects should be defined to house parsed metadata (e.g., file size, magic bytes, detected types, warnings, and format-specific attributes).
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* **Decoupled Parsers**: Extraction helper functions must return these dataclass instances instead of printing directly to standard output. This keeps the core parser functions purely functional and fully testable in unit tests.
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### B. Console Rendering with `rich`
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To provide a clean, modern, and easily readable console output without building a full terminal user interface (TUI):
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* **Tables**: Use `rich.table.Table` to align and structure multi-column metadata outputs.
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* **Formatting & Alerts**: Utilize `rich.console` or `rich.panel.Panel` to highlight warnings (such as signature/extension mismatches) with distinct styling and colors.
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* **JSON Serialization**: Dataclasses should be easily convertible to dictionaries to support raw JSON output options for scripting pipelines.
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### C. Function Isolation & Pipeline Composition
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To support fully automated or agentic solving, all processing functions must be isolated and highly composable:
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* **Pure Functions**: Core analysis and transformation functions (e.g., file carving, cipher decoders, and metadata extraction) must remain pure. They must accept standard Python types (e.g., `Path`, `bytes`, `str`) and return structured dataclasses without depending on global CLI state, click contexts, or interactive user prompts.
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* **Pipeline Chaining**: The output of one tool must serve as valid input for another. For example, a list of files or byte arrays carved from a file-carver must be pipeable directly into the string parser or decompression module.
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* **Programmatic API**: The entire core logic must be importable as standard Python APIs (`import ctf.utils`, `import ctf.forensics`) separate from CLI bindings. This allows external scripts, test cases, or autonomous agents to programmatically chain functions together to build complex, self-contained solving pipelines.
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