GPU/FPGA Password Hashing

TL;DR

Yes, several frameworks offer password hashing implementations that can utilise GPUs and FPGAs for significantly faster cracking resistance. This guide outlines options in Python (using libraries like Hashcat), Java, .NET, and Go, focusing on practical integration rather than deep hardware specifics.

1. Understanding the Benefit

Standard CPU-based password hashing is becoming vulnerable to brute-force attacks due to increasing computing power. GPUs and FPGAs offer massive parallel processing capabilities, making them far more efficient at trying many password combinations simultaneously. Using these can drastically increase the time it takes for an attacker to crack passwords.

2. Python & Hashcat

Python itself doesn’t directly handle GPU/FPGA acceleration for hashing. Instead, you typically use external tools like Hashcat and interact with them from Python scripts.

• Hashcat: A powerful password cracking tool that supports various algorithms and hardware acceleration.
• Installation (Linux):

  sudo apt-get install hashcat

• Example Usage (from Python using subprocess):

  import subprocess
  
  command = ['hashcat', '-m', '3200', 'hashfile.txt', '-a', '0', '--force'] # 3200 is MD5, -a 0 for brute-force
  process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
  out, err = process.communicate()
  print(out.decode())
  

  Note: This is a very basic example. Hashcat has many options for specifying password lists, rules, and hardware.

3. Java & Argon2

Java offers libraries like jBCrypt which can be extended to use native implementations that support GPU acceleration. However, the most common approach is using Argon2.

• Argon2: A key derivation function designed to resist password cracking attacks. Implementations exist for Java with potential for hardware acceleration via native libraries (e.g., libargon2).
• Maven Dependency:

  <dependency>
      <groupId>org.ergon-argon2</groupId>
      <artifactId>argon2-jvm</artifactId>
      <version>3.2.0</version>
  </dependency>

• Example Usage:

  import org.ergon.argon2.Argon2Factory;
  import org.ergon.argon2.Argon2PasswordEncoder;
  
  public class PasswordHasher {
      public static void main(String[] args) {
          Argon2PasswordEncoder passwordEncoder = Argon2Factory.createPasswordEncoder();
          String encodedPassword = passwordEncoder.encode("mysecretpassword");
          System.out.println(encodedPassword);
      }
  }

  Note: Check the library documentation for specific GPU/FPGA acceleration options, as it often requires installing native dependencies and configuring your system.

4. .NET & Password Hashing Libraries

.NET provides libraries like BCrypt.Net which can be used with native implementations that support hardware acceleration. Similar to Java, Argon2 is a strong option.

• Argon2: Implementations are available for .NET (e.g., `BCrypt.Net-Next`).
• NuGet Package Installation:

  Install-Package BCrypt.Net-Next

• Example Usage:

  using BCrypt.Net;
  
  public class PasswordHasher {
      public static void main(string[] args) {
          string hash = BCrypt.HashPassword("mysecretpassword", 10);
          Console.WriteLine(hash);
      }
  }

  Note: Hardware acceleration in .NET often relies on native libraries and may require specific configuration.

5. Go & Argon2

Go has excellent support for Argon2 through the `golang.org/x/crypto/argon2` package.

• Installation:

  go get golang.org/x/crypto/argon2

• Example Usage:

  package main
  import (
      "fmt"
      "golang.org/x/crypto/argon2"
  )
  func main() {
      password := []byte("mysecretpassword")
      hashedPassword, err := argon2.Hash(password, nil, 10, 32, argon2.IDKey, 16)
      if err != nil {
          fmt.Println(err)
          return
      }
      fmt.Println(string(hashedPassword))
  }

  Note: GPU/FPGA acceleration with Argon2 in Go typically involves using a native implementation of Argon2 compiled with hardware support.

6. Important Considerations

• Native Libraries: Hardware acceleration usually requires installing and configuring native libraries specific to your GPU or FPGA.
• Complexity: Integrating these technologies can be complex, requiring knowledge of both software development and hardware specifics.
• Testing: Thoroughly test any implementation to ensure it’s functioning correctly and providing the expected performance gains.
• cyber security Best Practices: Always use a strong salt with your password hashing algorithm.