Glaurung Large 001

A RoBERTa-based masked language model trained on binary executable files for security research and binary analysis. Part of the Glaurung project: a modern reverse engineering framework with first-class AI integration.

Overview

Glaurung Large 001 is a transformer model specifically designed for understanding binary executable files. It uses a custom BPE (Byte Pair Encoding) tokenizer trained on multi-byte patterns from various binary formats across multiple architectures (x86-64, ARM64, etc.) and operating systems (Linux, Alpine, Ubuntu, Debian, Rocky).

This is the large variant (371M parameters, 24 layers) offering enhanced understanding of binary patterns. For faster inference, see glaurung-small-001 (160M parameters).

Key Features

• Custom Binary Tokenizer: BPE tokenizer that creates efficient multi-byte tokens from binary data
• Binary-Aware: Trained on actual executable files, not hex strings
• Multi-Architecture: Understands patterns from various CPU architectures and file formats
• Latin-1 Encoding: Preserves all byte values (0-255) without loss
• Large Model: 371M parameters with deeper architecture for enhanced binary understanding

Model Details

• Architecture: RoBERTa for Masked Language Modeling
• Hidden Size: 1024
• Layers: 24
• Attention Heads: 16
• Intermediate Size: 4096
• Vocabulary Size: 65,536 tokens
• Tokenizer: binary-tokenizer-005
• Max Position Embeddings: 520
• Parameters: ~371M
• Special Tokens:
  • <|start|> (0): Beginning of sequence
  • <|end|> (1): End token
  • <|sep|> (2): Separator/EOS
  • <|cls|> (3): Classification token
  • <|pad|> (4): Padding
  • <|mask|> (5): Mask token for MLM
  • <|unk|> (6): Unknown token

Glaurung Ecosystem

This model is part of the Glaurung project ecosystem:

🔧 Main Project

• Glaurung - A modern reverse engineering framework designed to replace Ghidra with first-class AI integration throughout the analysis pipeline. Built with Rust's performance and Python's accessibility, featuring AI agents integrated at every level from format detection to decompilation.

🤖 Model Family

• glaurung-large-001 (this model) - 371M parameters, 24 layers
• glaurung-small-001 - 160M parameters, 12 layers, faster inference

🔤 Tokenizer

• binary-tokenizer-005 - 65K vocabulary BPE tokenizer trained on multi-byte patterns

Performance Comparison vs Glaurung Small 001

Key Improvements:

• Dramatically improved confidence on binary pattern prediction (+21pp on ELF magic)
• Deeper architecture enables better long-range dependencies in binary code
• More stable predictions with near-perfect accuracy on structured headers
• Larger capacity for learning complex multi-architecture binary patterns

Installation & Loading

pip install transformers torch

from transformers import AutoTokenizer, AutoModelForMaskedLM, AutoModel, pipeline

# Method 1: Load with pipeline for fill-mask tasks
fill_mask = pipeline('fill-mask', model='mjbommar/glaurung-large-001', device=-1)

# Method 2: Load model and tokenizer directly for fill-mask
model = AutoModelForMaskedLM.from_pretrained('mjbommar/glaurung-large-001')
tokenizer = AutoTokenizer.from_pretrained('mjbommar/glaurung-large-001')

# Method 3: Load base model for feature extraction/embeddings
model_base = AutoModel.from_pretrained('mjbommar/glaurung-large-001')

Usage Guide

1. Loading Binary Data (Critical!)

Binary files MUST be read as bytes and converted to latin-1 encoding:

# CORRECT: Read as bytes, decode with latin-1
with open('/usr/bin/ls', 'rb') as f:
    binary_data = f.read()  # Read first 512 bytes or as needed
    text = binary_data.decode('latin-1', errors='ignore')

# WRONG: Never use hex strings or other encodings
# hex_string = "7f454c46..."  # ❌ Will not work
# utf8_text = binary_data.decode('utf-8')  # ❌ Will lose bytes

2. Understanding the BPE Tokenizer

The tokenizer creates multi-byte tokens from common binary patterns:

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('mjbommar/glaurung-large-001')

# Example: ELF header tokenization
elf_header = b'\x7fELF\x02\x01\x01\x00'
text = elf_header.decode('latin-1')

tokens = tokenizer(text, return_tensors='pt')
token_ids = tokens['input_ids'][0].tolist()

# Decode tokens individually to see multi-byte patterns
for token_id in token_ids[1:5]:  # Skip special tokens
    decoded = tokenizer.decode([token_id], skip_special_tokens=True)
    print(f"Token {token_id}: {repr(decoded)}")

# Output:
# Token 45689: '\x7fEL'    # ELF magic compressed to one token!
# Token 3665:  'F\x02'     # Format byte + 64-bit flag
# Token 458:   '\x01\x01'  # Little-endian + version
# Token 600:   '\x00\x00\x00\x00\x00\x00\x00\x00\x00'  # Padding

3. Fill-Mask Task (Token-Level Prediction)

Important: Masking works at the TOKEN level, not byte level!

from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch

model = AutoModelForMaskedLM.from_pretrained('mjbommar/glaurung-large-001')
tokenizer = AutoTokenizer.from_pretrained('mjbommar/glaurung-large-001')

# Read binary file
with open('/usr/bin/ls', 'rb') as f:
    binary_data = f.read(512)
    text = binary_data.decode('latin-1', errors='ignore')

# Tokenize
tokens = tokenizer(text, return_tensors='pt')
token_ids = tokens['input_ids'][0].tolist()

# Mask the second token (first content token after <|start|>)
masked_ids = token_ids.copy()
original_token = masked_ids[1]  # Save original
masked_ids[1] = tokenizer.mask_token_id

# Prepare input
tokens_masked = {
    'input_ids': torch.tensor([masked_ids]),
    'attention_mask': torch.tensor([[1]*len(masked_ids)])
}

# Predict
with torch.no_grad():
    outputs = model(**tokens_masked)
    predictions = outputs.logits[0, 1].softmax(dim=-1)
    top5 = predictions.topk(5)

# Show results
print(f"Original: {repr(tokenizer.decode([original_token]))}")
for score, token_id in zip(top5.values, top5.indices):
    token_text = tokenizer.decode([token_id.item()], skip_special_tokens=True)
    print(f"Predicted: {repr(token_text)} (confidence: {score:.2%})")

# Example output:
# Original: '\x7fEL'
# Predicted: '\x7fEL' (confidence: 59.23%)  ✓ Correct!
# Predicted: '\x00\x00\x00\x00\x00\x00\x00\x00' (confidence: 9.87%)
# Predicted: '\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' (confidence: 4.45%)

4. Using Pipeline for Fill-Mask

The pipeline handles tokenization automatically but requires understanding multi-byte tokens:

from transformers import pipeline

# Load pipeline
fill_mask = pipeline('fill-mask', model='mjbommar/glaurung-large-001', device=-1)

# Read binary
with open('/usr/bin/ls', 'rb') as f:
    binary_data = f.read(100)
    text = binary_data.decode('latin-1', errors='ignore')

# Create masked input at token boundaries
# First, tokenize to understand token boundaries
tokenizer = fill_mask.tokenizer
tokens = tokenizer(text)
decoded_tokens = [tokenizer.decode([tid], skip_special_tokens=True) for tid in tokens['input_ids']]

# Reconstruct with mask at token boundary
masked_text = ''.join([
    decoded_tokens[0],  # <|start|>
    fill_mask.tokenizer.mask_token,  # Mask the ELF magic
    ''.join(decoded_tokens[2:])  # Rest of tokens
])

# Predict
predictions = fill_mask(masked_text, top_k=3)
for pred in predictions:
    print(f"{repr(pred['token_str'])}: {pred['score']:.2%}")

5. Feature Extraction & Embedding Similarity

Compare binary files by their learned embeddings:

from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
from pathlib import Path

# Load for embeddings (not MaskedLM)
tokenizer = AutoTokenizer.from_pretrained('mjbommar/glaurung-large-001')
model = AutoModel.from_pretrained('mjbommar/glaurung-large-001')
model.eval()

def get_binary_embedding(file_path, max_bytes=512):
    """Extract embedding for a binary file using mean pooling"""
    with open(file_path, 'rb') as f:
        binary_data = f.read(max_bytes)
        text = binary_data.decode('latin-1', errors='ignore')

    # Tokenize
    tokens = tokenizer(text, return_tensors='pt',
                      padding=True, truncation=True, max_length=512)

    # Get embeddings with mean pooling
    with torch.no_grad():
        outputs = model(**tokens)
        # Mean pooling (better than CLS token for this model)
        attention_mask = tokens['attention_mask']
        hidden_states = outputs.last_hidden_state

        # Mask padding tokens
        mask_expanded = attention_mask.unsqueeze(-1).expand(hidden_states.size()).float()
        sum_embeddings = torch.sum(hidden_states * mask_expanded, dim=1)
        sum_mask = torch.clamp(mask_expanded.sum(dim=1), min=1e-9)
        embedding = sum_embeddings / sum_mask

    return embedding

# Compare multiple binaries
files = ['/usr/bin/ls', '/usr/bin/cat', '/usr/bin/echo', '/etc/passwd']
embeddings = {}

for file_path in files:
    if Path(file_path).exists():
        name = Path(file_path).name
        embeddings[name] = get_binary_embedding(file_path)

# Calculate similarities
print("Cosine Similarity Matrix:")
names = list(embeddings.keys())
for name1 in names:
    similarities = []
    for name2 in names:
        sim = F.cosine_similarity(embeddings[name1], embeddings[name2], dim=-1).item()
        similarities.append(f"{sim:.3f}")
    print(f"{name1:10s}: {' '.join(similarities)}")

# Expected output:
# ELF executables (ls, cat, echo) will have high similarity (0.85-0.95)
# Text file (passwd) will have low similarity (0.25-0.30) to ELF files

Real-World Example: ELF Header Analysis

from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch

# Load model and tokenizer
model = AutoModelForMaskedLM.from_pretrained('mjbommar/glaurung-large-001')
tokenizer = AutoTokenizer.from_pretrained('mjbommar/glaurung-large-001')

# Analyze ELF executable structure
with open('/usr/bin/ls', 'rb') as f:
    binary_data = f.read(512)  # Read enough for context

print(f"Raw bytes (hex): {binary_data[:16].hex()}")
# Output: 7f454c46020101000000000000000000

# Convert to latin-1 for model
text = binary_data.decode('latin-1', errors='ignore')

# Tokenize to see learned patterns
tokens = tokenizer(text, return_tensors='pt')
token_ids = tokens['input_ids'][0].tolist()

# Show what tokens the model learned
print("\nTokenized ELF header:")
for i in range(1, min(5, len(token_ids)-1)):  # First few content tokens
    token_text = tokenizer.decode([token_ids[i]], skip_special_tokens=True)
    print(f"Token {i}: {token_ids[i]:5d} = {repr(token_text)}")

# Output:
# Token 1: 45689 = '\x7fEL'  - ELF magic compressed to one token!
# Token 2:  3665 = 'F\x02'   - 'F' + 64-bit flag
# Token 3:   458 = '\x01\x01' - Little-endian + version
# Token 4:   600 = '\x00\x00\x00\x00\x00\x00\x00\x00\x00' - Padding

# Test model's understanding by masking each token
print("\nTesting model predictions:")
for position in [1, 2, 3]:  # Test first 3 content tokens
    masked_ids = token_ids.copy()
    original_token = masked_ids[position]
    masked_ids[position] = tokenizer.mask_token_id

    # Create input tensors
    tokens_masked = {
        'input_ids': torch.tensor([masked_ids]),
        'attention_mask': torch.tensor([[1]*len(masked_ids)])
    }

    # Get prediction
    with torch.no_grad():
        outputs = model(**tokens_masked)
        predictions = outputs.logits[0, position].softmax(dim=-1)
        predicted_token = predictions.argmax().item()
        confidence = predictions.max().item()

    # Show results
    original_text = tokenizer.decode([original_token], skip_special_tokens=True)
    predicted_text = tokenizer.decode([predicted_token], skip_special_tokens=True)
    correct = "✓" if predicted_token == original_token else "✗"

    print(f"Position {position}: {correct}")
    print(f"  Original:  {repr(original_text)}")
    print(f"  Predicted: {repr(predicted_text)} (confidence: {confidence:.1%})")

# Expected Output:
# Position 1: ✓
#   Original:  '\x7fEL'
#   Predicted: '\x7fEL' (confidence: 59.2%)
# Position 2: ✗ (prefers single 'F')
#   Original:  'F\x02'
#   Predicted: 'F' (confidence: 96.0%)
# Position 3: ✗ (not in top 5)
#   Original:  '\x01\x01'
#   Predicted: '\x00\x00\x00\x00\x00\x00\x00\x00' (confidence: 59.1%)

Multi-Format Analysis: ELF vs PE Headers & x86 Instructions

Systematic testing reveals performance varies by format and training data exposure:

Performance Summary Table

ELF Header Recognition (Strong)

# Test: /usr/bin/ls with 152 bytes of context
# Token 1: '\x7fEL' (3-byte ELF magic)
# Result: 59.23% confidence, rank #1 ✓

The model strongly recognizes ELF headers (94.6% of training data).

PE Header Recognition (Limited)

# Test: Realistic DOS/PE header with 152 bytes of context
# Token 1: 'MZ' (2-byte PE signature)
# Result: 7.34% confidence, rank #2 (null bytes ranked #1 at 29.95%)

PE recognition reflects limited training exposure (5.4% of training data, 647 files).

x86 Instructions (Context-Dependent)

# Test: Function prologue in /usr/bin/ls at offset 0x4e05
# Token: 'UH\x89å' = 0x554889e5 (4 bytes: PUSH RBP; MOV RBP, RSP)
# Result: 100.00% confidence, rank #1 ✓

Key Finding: The BPE tokenizer learned to respect x86 instruction boundaries!

• 1-byte tokens: PUSH reg (0x55), RET (0xc3)
• 2-byte tokens: MOV reg,reg with ModR/M (0x89e5)
• 4-byte tokens: Common prologues (0x554889e5)

Performance is excellent with full binary context but degrades on isolated instruction bytes.

Training Data Distribution & Performance Correlation

The model was trained on the following binary distribution:

Key Metrics:

• By file count: 17.5:1 (ELF:PE)
• By data size: 2.2:1 (ELF:PE)
• PE files are 8x larger on average (3.19 MB vs 0.40 MB per file)

This distribution explains the observed performance:

Key Takeaway: The model's PE performance reflects training data composition. While PE is only 5% by file count, it represents 31% by size due to larger average file sizes. The 8.1x performance gap (59.2% vs 7.3%) roughly correlates with the 17.5x file count imbalance, though size-based exposure is more balanced.

Practical Guidance:

• ✅ Use for: Linux/Unix binary analysis, ELF malware analysis, x86-64 code patterns
• ⚠️ Limited for: Windows PE analysis (consider retraining with balanced PE dataset)
• ✅ Tokenizer learned: Instruction-level boundaries across both formats

Training Details

• MLM Objective: 20% masking probability
• Training Data: Binary executables from various architectures
• Optimization: AdamW with warmup, dropout 0.01
• Special Design: Increased position embeddings (520) to handle RoBERTa's position offset
• Model Size: Large variant with 24 layers and 1024 hidden dimensions

Limitations

• Maximum sequence length: 512 tokens
• Optimized for executable files (ELF, PE, Mach-O)
• Mean pooling recommended for embeddings (pooler layer not specifically trained)
• Larger model size requires more memory (consider using device_map="auto" for large batches)

Citation

If using this model in research:

@software{glaurung-large-001,
  title = {Glaurung Large 001: Binary Analysis Transformer},
  author = {Glaurung Project},
  year = {2024},
  url = {https://github.com/mjbommar/glaurung-models}
}