Cantordust Binary Visualization

cantordust-viz

Binary visualization for human pattern recognition - Ghidra plugin by Chris Domas (xoreaxeaxeax)

plurigrid/asi·0installs67starsSecurity

SKILL.md

Full skill instructions

Cantordust Binary Visualization

Use when embeddings fail: humans see patterns algorithms miss.

Visual binary analysis tool for Ghidra. Converts binary data to bitmaps/visualizations where structural patterns become visible to human pattern recognition.

GF(3) Triad

cantordust-viz (-1) ⊗ skill-embedding-vss (0) ⊗ radare2-hatchery (+1) = 0 ✓

Lineage: 2020 Binary Analysis

Tool	Approach	Strength
Cantordust	Visual/human	Sees patterns ML misses
Zignatures	Soft signatures	Fuzzy matching + keyspace reduction
skill-embedding-vss	MLX embeddings	O(1) similarity at scale

Installation

git clone https://github.com/Battelle/cantordust.git
# Add to Ghidra Script Manager

Key Insight

From xoreaxeaxeax's work:

movfuscator: All x86 can be MOV (Turing-complete)
sandsifter: Fuzzing reveals undocumented CPU instructions
Cantordust: Binary structure visible in 2D projections

When to Use

Embedding similarity unclear → visualize both binaries
Obfuscation suspected → visual patterns survive obfuscation
Cross-architecture comparison → structural similarity visible
Malware family classification → visual fingerprinting

xoreaxeaxeax Ecosystem (19K+ stars)

Repo	Stars	Category
movfuscator	10,075	obfuscation
sandsifter	4,998	hardware security
rosenbridge	2,380	hardware backdoors
REpsych	1,031	anti-RE

Integration with skill-embedding-vss

# When embeddings show high similarity but you want visual confirmation
from cantordust import visualize_binary
from skill_embedding_vss import SkillEmbeddingVSS

vss = SkillEmbeddingVSS('/path/to/skills')
similar = vss.find_nearest('target', k=5)

# Visual confirm top matches
for name, dist in similar[:3]:
    visualize_binary(f'/path/to/{name}')  # Human reviews

References

Cantordust ↔ Gay.jl Bridge

# cantordust_gay_bridge.jl connects:
# 1. Cantordust 2-tuple byte pair visualization
# 2. CJ Carr spectral features (diffusion transformers)  
# 3. Gay.jl deterministic coloring (SPI)

result = analyze_binary_with_gay("target.bin")
# Returns: matrix, diagonal_score, ascii_score, trit_sum, sample_colors

Pattern Theory

Domain	Representation	Gay.jl Mapping
Binary (Cantordust)	2-tuple → 256×256	entropy → trit → color
Audio (CJ Carr)	Mel spectrogram	centroid/flatness → HSL
Color (Gay.jl)	SplitMix64 + golden angle	SPI deterministic

Install skill

Copy and run in your terminal

npx -y skills add https://github.com/plurigrid/asi --skill cantordust-viz

View on GitHub

plugins/asi/skills/cantordust-viz/SKILL.md

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