High-performance Windows Registry hive parser with Python bindings

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Project description

Windows Registry Hive Parser

A high-performance, production-grade Windows Registry hive parser written in Rust.

Features

🚀 High Performance: Zero-copy parsing using memory-mapped files
📦 Complete Support: Handles all common Windows registry hive formats
- SYSTEM, SOFTWARE, SAM, SECURITY
- NTUSER.DAT, UsrClass.dat
- Amcache.hve, and more
- Big data blocks (values > 16KB) fully supported
🔒 Type-Safe: Strong typing for registry values and structures
⚡ Lazy Evaluation: Parses structures only when accessed
🛡️ Robust Error Handling: Comprehensive error types for debugging
🧪 Well-Tested: Extensive test suite using real registry hives
📚 Well-Documented: Complete API documentation with examples
🐍 Python Bindings: High-performance Python bindings available

Installation

Rust

Add this to your Cargo.toml:

[dependencies]
reg-parser = "0.1"

Python

# Install from source (requires Rust toolchain)
pip install maturin
git clone https://github.com/ac-rn/reg-parser.git
cd reg-parser
maturin develop --release --features python

# Or use the build script
./build_python.sh release  # Linux/macOS
.\build_python.ps1 release # Windows

See PYTHON_BINDINGS.md and python/README.md for detailed Python documentation.

Quick Start

Rust

use reg_parser::Hive;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Open a registry hive
    let mut hive = Hive::open("SYSTEM")?;

    // Get the root key
    let mut root = hive.root_key()?;
    println!("Root key: {}", root.name()?);

    // Enumerate subkeys
    for mut subkey in root.subkeys()? {
        println!("  Subkey: {}", subkey.name()?);
    }

    // Enumerate values
    for value in root.values()? {
        println!("  Value: {} = {}", value.name(), value.data()?.to_string());
    }

    Ok(())
}

Python

import reg_parser

# Open a registry hive
hive = reg_parser.Hive.open("SYSTEM")

# Get the root key
root = hive.root_key()
print(f"Root key: {root.name()}")

# Enumerate subkeys
for subkey in root.subkeys():
    print(f"  Subkey: {subkey.name()}")

# Enumerate values
for value in root.values():
    data = value.data()
    print(f"  Value: {value.name()} = {data}")

Architecture

Binary Layout

Registry hives follow this structure:

[Base Block - 4KB]
  - Signature: "regf"
  - Version, timestamps, root offset
  - Checksum

[Hive Bins - variable size, 4KB aligned]
  [Hbin Header - 32 bytes]
    - Signature: "hbin"
    - Offset, size
  
  [Cells - variable size]
    [Cell Size - 4 bytes, negative if allocated]
    [Cell Data]
      - Key nodes (nk)
      - Value keys (vk)
      - Subkey lists (lf/lh/li/ri)
      - Security descriptors (sk)

Module Structure

header: Base block (regf) parsing
hbin: Hive bin block parsing
cell: Cell type definitions
key: Key node (nk) structures
value: Value key (vk) structures and data types
subkey_list: Subkey list parsing (lf/lh/li/ri)
hive: Main hive parser with memory-mapped I/O
error: Comprehensive error types
utils: Helper functions for binary parsing

Examples

Accessing Specific Values

use reg_parser::{Hive, ValueData};

let mut hive = Hive::open("SOFTWARE")?;
let mut root = hive.root_key()?;

// Navigate to a specific key
let mut subkeys = root.subkeys()?;
let mut microsoft_key = subkeys.into_iter()
    .find(|k| k.name().unwrap_or("") == "Microsoft")
    .ok_or("Microsoft key not found")?;

// Get a specific value
let value = microsoft_key.value("SomeValue")?;
match value.data()? {
    ValueData::String(s) => println!("String value: {}", s),
    ValueData::Dword(d) => println!("DWORD value: {}", d),
    ValueData::Binary(b) => println!("Binary value: {} bytes", b.len()),
    _ => println!("Other type"),
}

Deep Traversal

use reg_parser::Hive;

fn traverse_keys(key: &mut reg_parser::RegistryKey, depth: usize) -> Result<(), Box<dyn std::error::Error>> {
    let indent = "  ".repeat(depth);
    println!("{}{}", indent, key.name()?);

    // Print values
    for value in key.values()? {
        println!("{}  {} = {}", indent, value.name(), value.data()?.to_string());
    }

    // Recurse into subkeys
    for mut subkey in key.subkeys()? {
        traverse_keys(&mut subkey, depth + 1)?;
    }

    Ok(())
}

let mut hive = Hive::open("SYSTEM")?;
let mut root = hive.root_key()?;
traverse_keys(&mut root, 0)?;

Iterating Over Hive Bins

use reg_parser::Hive;

let hive = Hive::open("SYSTEM")?;

for hbin_result in hive.hbins() {
    let hbin = hbin_result?;
    println!("Hbin at offset {:#x}, size {:#x}", hbin.offset, hbin.size);
}

Performance

The parser is designed for maximum performance:

Memory-mapped I/O: Zero-copy access to hive data
Lazy parsing: Structures are parsed only when accessed
Caching: Parsed key nodes are cached to avoid redundant work
Minimal allocations: Uses slices and references where possible

Benchmarks

Run benchmarks with:

cargo bench

Typical performance on modern hardware:

Open hive: ~1-5ms
Root key access: ~10-50μs
Enumerate subkeys: ~100-500μs
Deep traversal (1000 keys): ~5-20ms

Testing

The library includes comprehensive tests using real registry hive files:

# Run all tests
cargo test

# Run integration tests
cargo test --test integration

# Run with output
cargo test -- --nocapture

Supported Registry Value Types

REG_NONE - No value type
REG_SZ - Null-terminated string
REG_EXPAND_SZ - String with environment variables
REG_BINARY - Binary data
REG_DWORD - 32-bit little-endian integer
REG_DWORD_BIG_ENDIAN - 32-bit big-endian integer
REG_LINK - Symbolic link
REG_MULTI_SZ - Multiple null-terminated strings
REG_QWORD - 64-bit little-endian integer
Resource types (list, descriptor, requirements)

Transaction Log Support

The parser now supports applying transaction logs (.LOG1, .LOG2) to recover uncommitted changes:

use reg_parser::Hive;

// Open hive with transaction logs applied
let hive = Hive::open_with_logs(
    "SYSTEM",
    Some("SYSTEM.LOG1"),
    Some("SYSTEM.LOG2")
)?;

// Save the cleaned hive
hive.save("SYSTEM_cleaned")?;

Applying Transaction Logs

// Apply a single transaction log
let base_hive = Hive::open("SYSTEM")?;
let cleaned_hive = base_hive.apply_transaction_log("SYSTEM.LOG1")?;

// Save the result
cleaned_hive.save("SYSTEM_cleaned")?;

Limitations and Future Work

Current Limitations

Write operations: No support for creating/modifying keys and values (read-only library)
- Can save existing hives after applying transaction logs
- Cannot create new keys or modify existing values

Planned Features

Security descriptor parsing and interpretation
Class name extraction
Full write support (create/modify keys and values)
Parallel parsing for multi-threaded workloads

Binary Format Reference

This parser implements the Windows Registry hive format as documented in:

Windows Registry File Format Specification
Microsoft's internal documentation
Reverse engineering and forensic analysis

Key Structures

Base Block (regf)

Offset: 0x0000
Size: 4096 bytes (0x1000)
Signature: "regf" (0x66676572)

Hive Bin (hbin)

Alignment: 4KB (0x1000)
Header Size: 32 bytes (0x20)
Signature: "hbin" (0x6E696268)

Cell Types

nk (Key Node): Registry key
vk (Value Key): Registry value
sk (Security): Security descriptor
lf (Fast Leaf): Subkey list with name hints
lh (Hash Leaf): Subkey list with name hashes
li (Index Leaf): Simple subkey list
ri (Index Root): List of subkey lists
db (Data Block): Big data block

Development Setup

# Clone the repository
git clone https://github.com/ac-rn/reg-parser.git
cd reg-parser

# Build the project
cargo build

# Run tests
cargo test

# Run benchmarks
cargo bench

# Generate documentation
cargo doc --open

License

This project is dual-licensed under:

MIT License (LICENSE-MIT or http://opensource.org/licenses/MIT)
Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)

You may choose either license for your use.

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This version

0.1.0

Dec 9, 2025

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regrs 0.1.0

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Windows Registry Hive Parser

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