Claude 24f816b6a3
Consolidate 22 sibling repos into layered organism structure
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):

- brain/        LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
                dispatch, A51 channels, and the OSINT cluster
- knowledge/    LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
                MITRE ATT&CK data
- reference/    defensive threat-reference (C3, shhbruh doc) + AdaYaml parser

License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.

Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.

https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
2026-06-10 06:53:01 +00:00

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2.3 KiB
Markdown

# API Reference: Steganography Detection Agent
## Overview
Detects hidden data in images and media using LSB analysis with Pillow/numpy, trailing data detection, and subprocess wrappers for binwalk, zsteg, and steghide.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| Pillow | >= 9.0 | Image loading and pixel manipulation |
| numpy | >= 1.23 | Array-based LSB bit extraction and statistics |
## External Tools (Optional)
| Tool | Purpose |
|------|---------|
| binwalk | Embedded file and data detection |
| zsteg | PNG/BMP LSB steganography detection |
| steghide | JPEG/BMP/WAV/AU data extraction with passwords |
## Core Functions
### `check_trailing_data(filepath)`
Detects data appended after JPEG (FF D9) or PNG (IEND) end markers, and embedded ZIP/RAR archives.
- **Returns**: `dict` with `trailing_bytes`, `embedded_zip`, `embedded_rar`
### `lsb_analysis(filepath)`
Analyzes LSB bit distribution across RGB channels. Flags `NEAR_RANDOM` (possible stego) or `SIGNIFICANT_DEVIATION`.
- **Returns**: `dict[str, dict]` - per-channel zeros, ones, ratio, anomaly
### `extract_lsb_data(filepath, output_path)`
Extracts red channel LSB data and checks for known file signatures (ZIP, PNG, JPEG, PDF, GIF).
- **Returns**: `dict` with `output`, `header_hex`, `detected_format`
### `run_binwalk(filepath)`
Subprocess wrapper for binwalk embedded file detection.
- **Returns**: `dict` with `tool` and `output`
### `run_zsteg(filepath)`
Subprocess wrapper for zsteg PNG/BMP LSB analysis.
- **Returns**: `dict` with `tool` and `output`
### `run_steghide_extract(filepath, passwords=None)`
Attempts steghide extraction with a password list.
- **Default passwords**: empty, password, secret, hidden, stego, test, 123456
- **Returns**: `list[dict]` - successful extractions with password and output path
### `analyze_file(filepath, output_dir=None)`
Full analysis pipeline combining all detection methods.
- **Returns**: `dict` - complete report with findings list
## Finding Types
| Type | Description |
|------|-------------|
| `trailing_data` | Data after image end marker |
| `embedded_archive` | ZIP/RAR found within file |
| `lsb_hidden_file` | Known file format in LSB data |
| `steghide_extraction` | Successfully extracted hidden data |
## Usage
```bash
python agent.py suspect_image.png
```