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coming allong nicely. adding more commands, tests, and

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artifacts for testing. basic framework down just adding features now
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venus
2026-07-18 02:38:13 -05:00
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ARCHITECTURE.md
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@@ -71,16 +71,18 @@ Implements specialized forensic inspection utilities registered as a nested subg
## 5. Planned Architectural Components
### A. File Scraper & Extended Configuration
* A scraping module to fetch details/files for challenges or competitions.
* Integration with an expanded configuration schema in [config.toml](file:///home/venus/code/ctf/config.toml) to store credentials, URLs, and directory preferences.
### A. Forensics Pipeline & Extended Metadata
* **Pipeline Coordination**: A central runner that coordinates file inspections, signature verification, and string searches sequentially on target files.
* **Carving & Extraction Toolkit**: Deep inspection and carving utilities (such as archive extracting, custom chunk/payload carvers, and EXIF/metadata header decoders).
### B. Download Organizer & Challenge Progress Documenter
* Monitoring or organizing downloaded challenge assets (e.g., from the browser's downloads folder) and sorting them into the correct competition/challenge subdirectories.
* An automated mechanism to log commands, notes, and milestones, providing clean progress documentation.
### B. Core Solving Infrastructure & QOL
* **Solving Assistants**: Standard templates and modular helper scripts for repeating CTF patterns (e.g., base conversions, cipher decoding, and request generation).
* **Context & Progress Management**: Full CLI commands to organize active challenges, automate note tracking, and manage workspace folders dynamically.
* **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.
### C. Forensics Metadata Expansion
* Extend forensics capabilities inside [forensics.py](file:///home/venus/code/ctf/src/ctf/forensics.py) to extract file-specific metadata (e.g., EXIF header extraction for JPG/PNG files, archive contents listing, and PE section analysis).
### C. Agentic Solving Orchestration
* **Action Space & Sandboxing**: API boundaries and isolated docker/sandbox execution paths to run unknown commands and binaries safely.
* **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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@@ -96,3 +98,9 @@ To provide a clean, modern, and easily readable console output without building
* **Tables**: Use `rich.table.Table` to align and structure multi-column metadata outputs.
* **Formatting & Alerts**: Utilize `rich.console` or `rich.panel.Panel` to highlight warnings (such as signature/extension mismatches) with distinct styling and colors.
* **JSON Serialization**: Dataclasses should be easily convertible to dictionaries to support raw JSON output options for scripting pipelines.
### C. Function Isolation & Pipeline Composition
To support fully automated or agentic solving, all processing functions must be isolated and highly composable:
* **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.
* **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.
* **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.