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

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

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

xoreaxeaxeax Ecosystem (19K+ stars)

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 GitHub
• Battelle Blog Post
• DEF CON talks by xoreaxeaxeax

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