Break the Code: Crack LM and NTLM Hashes Using CudaHashCat

Understanding the evolution of Windows authentication protocols helps in grasping why LM and NTLM hashes are still relevant today. LM hashes originated in the early days of LAN Manager, a network operating system developed jointly by Microsoft and 3Com. At the time, security requirements were rudimentary, and computing power was limited, making LM hashing sufficient for its era.

NTLM was introduced as a successor, designed to overcome the limitations of LM. It replaced DES encryption with MD4 hashing, allowing for passwords longer than 14 characters and case sensitivity. However, due to the absence of salting and the use of a weak hash function, NTLM still fails to meet modern security standards.

Today, most Windows systems use Kerberos as the default authentication protocol. However, NTLM remains for fallback scenarios and backward compatibility, especially in networks where legacy applications are still in use. As a result, hashes from these older protocols can still be found on modern networks.

Windows Hash Storage and Retrieval

In Windows systems, password hashes are stored in the Security Account Manager (SAM) database. This file is located at:

makefile

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C:\Windows\System32\Config\SAM

 

Accessing this file while the system is running is protected by Windows. However, attackers with administrative privileges or access to system snapshots can extract it. Common tools used in the retrieval process include:

• secretsdump.py: Part of the Impacket suite, it allows dumping password hashes remotely.

• Mimikatz: Widely known for credential extraction, it can pull password hashes from memory.

• pwdump: A family of tools for exporting hashes from local systems.

Once hashes are extracted, attackers often move them to an offline environment where they can run tools like CudaHashcat without detection or network restrictions.

Deep Dive: How CudaHashcat Works

CudaHashcat utilizes CUDA (Compute Unified Device Architecture), NVIDIA’s parallel computing platform, to leverage GPU cores for faster computation. Unlike CPUs, which may have a few powerful cores, GPUs have thousands of smaller, efficient cores optimized for parallel tasks, perfect for hash comparison loops.

CudaHashcat reads hashes from an input file and compares them against generated or listed password candidates. Depending on the selected cracking mode, it may use a static list of passwords (dictionary attack), a defined pattern (mask attack), or complex permutations (rule-based or hybrid attacks).

For example, a dictionary attack command might look like this:

bash

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cudaHashcat64.bin -m 1000 -a 0 hashes.txt wordlist.txt

 

Where:

• -m 1000 specifies NTLM hash mode.

• -a 0 defines the attack mode (dictionary).

• hashes.txt contains the target hashes.

• wordlist.txt contains the password candidates.

Each candidate is hashed in the same way as the target format and compared. A match reveals the original password.

GPU vs CPU: Why It Matters

GPU cracking is orders of magnitude faster than CPU-based cracking. For example, an NVIDIA RTX 3090 can calculate billions of NTLM hashes per second. This immense power means even moderately complex passwords can be cracked quickly without optimized defenses.

The choice of GPU also impacts performance. CudaHashcat supports a wide range of NVIDIA GPUs, but newer models with more CUDA cores and faster memory bandwidth provide significant speed advantages. Proper cooling and hardware stability are also critical, as cracking can generate sustained GPU load.

Selecting Cracking Modes Based on Target Environment

CudaHashcat offers several cracking modes, each suitable for different scenarios:

• Dictionary attack: Effective if you suspect weak or common passwords.

• Mask attack: Useful when you know a password pattern, such as Qwerty123!.

• Hybrid attack: Combines dictionary and mask approaches, e.g., adding numeric suffixes.

• Brute-force attack: Tests every possible combination up to a set length; extremely time-consuming.

• Rule-based attack: Applies transformation rules to dictionary entries, increasing complexity with minimal input size.

Professionals often start with dictionary or rule-based attacks, then escalate to mask or brute-force modes based on available resources and time constraints.

Ethical Hacking Use Cases and Risk Mitigation

Ethical hackers perform hash cracking to simulate real-world attack scenarios. Organizations use these exercises to identify weak password policies, understand credential reuse, and measure resistance to lateral movement. Results often prompt implementation of stronger password policies, such as requiring longer passwords with special characters, enforcing expiration periods, and locking out repeated login attempts.

Additionally, penetration tests might lead to disabling the storage of LM hashes via Group Policy settings:

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Computer Configuration > Windows Settings > Security Settings > Local Policies > Security Options

 

Setting “Network security: Do not store LAN Manager hash value on next password change” to Enabled prevents future LM hashes from being stored.

Real-World Scenario: Lateral Movement Through Hashes

A common scenario involves an attacker compromising one system and dumping the SAM file. If LM or NTLM hashes are present, the attacker uses CudaHashcat to crack them offline. If successful, they can reuse those credentials to access other machines in the domain,  especially if the user has administrative privileges.

This type of attack, known as pass-the-hash, doesn’t even require cracking. But once passwords are recovered, attackers may impersonate users in web apps, VPNs, or RDP connections. That’s why cracking is considered a high-impact post-exploitation technique.

Preventing Exploitation

Beyond avoiding the use of LM hashes, organizations should:

• Audit password complexity regularly.

• Enforce multifactor authentication.

• Remove legacy protocol dependencies.

• Rotate administrator credentials frequently.

• Monitor for suspicious hash extraction tools.

System hardening, user education, and active monitoring work together to make hash cracking less fruitful for attackers.

This part has provided a detailed foundation on how LM and NTLM hashes function, why they are still relevant, and how CudaHashcat is used in ethical hacking engagements to test password robustness.

Setting Up CudaHashcat for Cracking LM and NTLM Hashes

To effectively use CudaHashcat in password cracking tasks, a properly configured system environment is essential. This includes hardware compatibility, driver installation, and command-line proficiency. This part provides a detailed walk-through of installing and configuring CudaHashcat on Linux and Windows, preparing your input files, and launching your first LM and NTLM cracking tasks.

System Requirements and Considerations

CudaHashcat relies heavily on GPU performance. Here are the key system requirements:

• NVIDIA GPU with CUDA support (Maxwell or newer)

• 64-bit OS: Linux (Ubuntu, Kali) or Windows 10/11

• At least 8 GB of RAM (16 GB recommended)

• The latest NVIDIA driver is installed.

• CUDA Toolkit installed (version compatible with the driver)

• Administrative privileges

Optional but helpful: solid-state drive for wordlists and hash files, and good system cooling.

Installing CudaHashcat on Linux

System Update

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sudo apt update && sudo apt upgrade -y

Install Build Tools

bash
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sudo apt install -y build-essential linux-headers-$(uname -r)

Install NVIDIA Driver

bash
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sudo apt install nvidia-driver-535

reboot

2. Install CUDA Toolkit
   Download and install from the NVIDIA website, matching your driver version.

Download Hashcat

bash
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git clone https://github.com/hashcat/hashcat.git

cd hashcat

make

Verify Setup

bash
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./hashcat -I

Installing on Windows

1. Install the latest NVIDIA Driver from the official website.

2. Install the CUDA Toolkit matching your GPU.

3. Download Hashcat from GitHub or the official site.

Extract the package and test:

cmd
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hashcat.exe -I

Hash File Format

Hashcat expects hashes in plain text. File format:

• One hash per line

• No usernames or colons (unless using –username option)

For NTLM:

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32ed87bdb5fdc5e9cba88547376818d4

 

For LM:

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44efce164ab921cqaad3b435b51404ee

 

Cracking with Dictionary Attack

A dictionary attack tests each word from a list. Simple syntax:

bash

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./hashcat -m 1000 -a 0 hashes.txt wordlist.txt

 

Where:

• -m 1000 = NTLM

• -a 0 = Dictionary attack

• hashes.txt = list of hashes

• wordlist.txt = candidate passwords

Large lists like rockyou.txt and SecLists increase success.

Using Rules to Strengthen Attacks

Rules transform base words by adding suffixes, prefixes, replacing characters, or capitalizing letters. This increases crack rates significantly.

bash

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./hashcat -m 1000 -a 0 -r rules/best64.rule hashes.txt wordlist.txt

 

Create custom rules to match organization-specific password patterns.

Understanding Attack Modes

Hashcat supports several attack modes. Selecting the right one maximizes efficiency.

• Dictionary (a0): Try each word from a list. Fast and efficient for weak passwords.

Brute-force (-a 3): Tries every possible combination. Time-consuming but exhaustive.

bash
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./hashcat -m 1000 -a 3 hashes.txt ?a?a?a?a

•  ?a = any character; useful when pthe attern length is known.

Mask attack: Brute-force with pattern:

bash
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./hashcat -m 1000 -a 3 hashes.txt ?u?l?l?l?d?d

•  ?u = uppercase, ?l = lowercase, ?d = digit

Hybrid (-a 6 and -a 7): Combine dictionary with mask:

bash
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./hashcat -m 1000 -a 6 hashes.txt wordlist.txt ?d?d

Performance Tuning and Benchmarking

Maximize GPU performance with:

• -w 3 or -w 4: Set workload profile

• –gpu-temp-retain=80: Control GPU temperature

• –gpu-accel=8 –gpu-loops=1024: Tune execution speed

• –benchmark: Test performance

For example:

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./hashcat -m 1000 –benchmark

 

This outputhhas ashrate statistics, helping identify hardware bottlenecks.

Managing Sessions and Recovery

Hashcat supports session control, useful in long jobs:

bash

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./hashcat -m 1000 -a 0 –session=ntlm_session hashes.txt wordlist.txt

 

To pause/resume:

bash

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pkill -USR1 hashcat

pkill -CONT hashcat

 

Resume:

bash

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./hashcat –session=ntlm_session –restore

 

Viewing Cracked Results

Hashcat stores cracked passwords in a hash potfile. To view:

bash

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cat hashcat.potfile

 

Or output directly:

bash

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./hashcat -m 1000 -a 0 hashes.txt wordlist.txt -o found.txt

 

Case Study: Cracking Weak NTLM Hashes

Scenario: You’re conducting a penetration test on a corporate environment. You retrieve NTLM hashes via a credential dumping tool.

1. Load hashes into hashes.txt

Launch:

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./hashcat -m 1000 -a 0 hashes.txt rockyou.txt

Apply rules:

bash
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./hashcat -m 1000 -a 0 -r rules/best64.rule hashes.txt rockyou.txt

Switch to mask if results are low:

bash
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./hashcat -m 1000 -a 3 hashes.txt ?u?l?l?l?d?d

Within minutes, you recover passwords like Admin12, Welcome1, and Qwerty!. These are commonly used by non-technical staff. You document the findings and advise implementing a password policy and multi-factor authentication.

Best Practices for Real-world Cracking

• Always work offline to avoid detection or performance impact..t

• Preprocess hash files to remove duplicates or non-target accounts

• Use GPU monitoring tools to avoid overheating (e.g., nvidia-smi)

• Archive cracked results and compare with future audits for reuse detection. on

This part provided everything needed to set up CudaHashcat and begin cracking LM/NTLM hashes efficiently. From installing dependencies to executing attack commands, you’ve now entered the practical domain of hash auditing.

Advanced Cracking Strategies for LM and NTLM Hashes with CudaHashcat

As organizations implement stricter password policies, standard dictionary attacks are no longer sufficient. To crack resilient LM and NTLM hashes effectively, cybersecurity professionals must leverage more sophisticated techniques. This part dives into advanced cracking strategies using CudaHashcat, including hybrid attacks, mask optimization, distributed cracking setups, password reuse tactics, and GPU tuning.

Deep Dive into Hybrid Attacks

Hybrid attacks combine two powerful strategies—dictionary and mask. This approach is particularly effective when users follow predictable patterns, such as appending birth years, lucky numbers, or common suffixes to a base password.

For instance, imagine users in an organization commonly use variations like Password2021, Welcome99, or Summer#23. A traditional dictionary attack might miss these if the base word Password or Summer exists, but the year or suffix doesn’t. With hybrid attacks, we can intelligently generate such permutations.

Example 1: Appending two digits to dictionary words

, , bash

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./hashcat -m 1000 -a 6 hashes.txt rockyou.txt ?d?d

 

Example 2: Prepending a special character before each word

in bash

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./hashcat -m 1000 -a 7- s rockyou.txt

 

These techniques can be enhanced further with rules:

bash

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./hashcat -m 1000 -a 6 -r rules/best64.rule hashes.txt rockyou.txt ?d?d

 

This command simultaneously modifies base words using the best64 rule and appends digits, significantly broadening the cracking potential.

Optimizing Mask Attacks

Mask attacks systematically guess characters in a structured format, ideal for when you know or suspect a specific password schema. Mask syntax in Hashcat is powerful:

• ?l: lowercase

• ?u: uppercase

• ?d: digits

• ?s: special characters

• ?a: all printable ASCII

Cracking Example:

bash

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./hashcat -m 1000 -a 3 hashes.txt ?u?l?l?l?l?d?d

 

This targets passwords like Admin12, which are common in corporate environments.

Pattern: ?u?l?l?l?l?d?d

• 1 uppercase

• 4 lowercase

• 2 digits

You can generate your masks using maskprocessor:

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./mp64.bin -1 ?u?l?d ?1?1?1?1?1?1?1

 

Segmenting Keyspaces for Speed

Breaking large mask attacks into segments lets you parallelize workloads across multiple GPUs or sessions. This is critical when you’re under time constraints or working with a massive hash list.

Use –skip and –limit options:

bash

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./hashcat -m 1000 -a 3 hashes.txt ?a?a?a?a?a?a –skip=100000 –limit=500000

 

This setup helps split the workload across machines or timeframes.

Distributed Cracking with Hashtopolis

When facing thousands of hashes or needing faster results, distributed cracking is essential. Hashtopolis is a platform for managing hash cracking across multiple devices and teams.

Step-by-step Setup:

1. Server Installation
   • Requires MySQL, Apache, and PHP.

   • Deploy on a dedicated system.

   • Configure the web interface for hash submission and task control.

2. Agent Installation
   • Each cracking system runs a Hashtopolis agent.

   • Agents regularly fetch tasks and submit cracked results.

3. Task Management
   • Upload your hash list.

   • Assign attack modes, wordlists, and rules.

   • Track progress from a web dashboard.

Hashtopolis distributes cracking jobs intelligently based on GPU availability and system performance, allowing teams to scale their cracking efforts globally.

Tapping into Password Reuse Patterns

One of the most overlooked strategies in password auditing is exploiting user behavior. Many people reuse passwords across platforms, and organizations often recycle legacy passwords.

Step 1: Build a Potfile Database

• After each audit, archive your cracked passwords.

• Merge multiple potfiles for a cumulative database.

Step 2: Reuse Potfile on New Targets

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./hashcat -m 1000 hashes.txt– show

 

This instantly reveals reused passwords, often without additional cracking required.

You can automate the profile comparison across multiple jobs, increasing efficiency and identifying behavioral security risks.

Revisiting LM Hash Cracking

LM hashes are fundamentally weaker than NTLM due to their limited character set and case insensitivity. However, many legacy systems still rely on them.

LM cracking characteristics:

• Passwords are split into 7-character halves

• Each half is hashed separately..

• Only the uppercase alphabet is used.

Efficient brute-force attack:

bash

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./hashcat -m 3000 -a 3 hashes.txt ?u?u?u?u?u?u?u

 

Even modern rainbow tables are often slower than GPU-accelerated brute-force using masks. While LM is rare in modern Windows environments, it’s still seen in legacy systems and should not be overlooked.

Practical Scenario: Large Enterprise Audit

Let’s consider a real-world penetration test. You’re handed a dump of 10,000 NTLM hashes extracted from a Windows domain controller.

Initial Dictionary Pass

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./hashcat -m 1000 -a 0 hashes.txt rockyou.txt

1.  Result: 2,100 passwords cracked in 5 minutes

Rules-based Augmentation

bash
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./hashcat -m 1000 -a 0 -r rules/best64.rule hashes.txt rockyou.txt

1.  Result: An Additional 1,200 passwords recovered

Hybrid for User Patterns

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./hashcat -m 1000 -a 6 -r rules/best64.rule hashes.txt rockyou.txt ?d?d

1.  Result: 800 more recovered, mostly based on the Month+Year pattern

Brute-force Mask

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./hashcat -m 1000 -a 3 hashes.txt ?u?l?l?l?l?d?d

1.  Result: 300 more, mostly Admin12, Test22, etc.

By the end of the engagement, over 4,500 of 10,000 passwords were recovered, demonstrating poor password hygiene even in a modern enterprise.

Tuning the GPU for Maximum Efficiency

For peak performance, tweak CudaHashcat settings:

• -w 4: Maximum workload profile

• –gpu-temp-retain=80: Target temperature

• –gpu-accel=8: Acceleration setting

• –gpu-loops=1024: Controls workload iteration per kernel

Monitor performance in real-time:

bash

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watch nvidia-smi

 

Use these settings to balance performance with system stability.

Troubleshooting Tips

• Errors loading hash file: Check formatting—one hash per line.

• Low GPU utilization: Adjust workload settings or update drivers.

• No hashes cracked: Test with benchmark wordlists, or verify hash format.

• Job interrupted: Use– restore to resume previous sessions.

Ethical Boundaries

Advanced tools come with responsibility. Always ensure explicit authorization for engagements. Avoid extracting hashes from production systems without consent. Document findings and responsibly disclose vulnerabilities to stakeholders.

Extracting LM and NTLM Hashes and Preparing for Cracking with CudaHashcat

Understanding how to effectively crack password hashes is only half the battle. The other half involves extracting those hashes correctly and legally from target systems. In this final part of the series, we will walk through the techniques used to extract LM and NTLM hashes from Windows systems, prepare them for cracking with CudaHashcat, and ensure the process is conducted ethically and securely. We will also cover analysis strategies, data sanitization practices, and common pitfalls during hash extraction.

How Hash Storage Works in Windows Systems

To extract LM and NTLM hashes, it’s essential to understand where and how Windows stores them. Windows authentication is based on a combination of hashed credentials stored in the Security Account Manager (SAM) database and domain controller databases such as NTDS.dit.

• Local Machines (Standalone PCs):
  • Hashes are stored in the SAM file located at C:\Windows\System32\config\SAM.

  • This file is locked while the operating system is running, requiring offline access or privilege escalation.

• Domain Controllers (Active Directory):
  • Hashes are stored in NTDS.dit, which holds all domain accounts.

  • Accessing this file requires administrative rights or domain controller-level privileges.

Legal and Ethical Considerations

Before diving into extraction techniques, ensure that you are authorized to operate. Unauthorized extraction or cracking of password hashes can lead to legal consequences. Always work within the bounds of a contract or a written security policy that explicitly permits such activities. Maintain logs, reports, and chain-of-custody documentation for auditing and legal defense if needed.

Tools for Extracting Hashes

There are several tools available for extracting LM and NTLM hashes. Here are some of the most reliable ones:

1. Mimikatz

Mimikatz is a post-exploitation tool that can dump credentials, including clear-text passwords, hashes, PINs, and Kerberos tickets from memory.

Usage example:

1. Open an elevated command prompt.

Run Mimikatz:

bash
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mimikatz

Dump hashes:

mimikatz
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privilege::debug

log hashdump.txt

sekurlsa::logonpasswords

Note: Mimikatz is often flagged by antivirus software and may require bypass techniques or execution in safe environments like red team labs.

2. SecretsDump

This is a command-line tool part of the Impacket suite. It allows dumping password hashes from remote or local machines, especially useful for domain environments.

Basic usage:

bash

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secretsdump.py domain/user:password@target_ip

 

Or using NTLM hashes for authentication:

bash

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secretsdump.py -hashes <LMhash>:<NTLMhash> domain/user@target_ip

 

If the attack machine has administrative access to the remote Windows system, this tool will extract the NTDS hashes, ready for offline cracking.

3. pwdump

A classic utility to extract LM and NTLM hashes from Windows machines.

Typical syntax:

bash

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pwdump > output.txt

 

The output is formatted as:

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username:uid:LM_hash:NTLM_hash:::

 

This output format is compatible with CudaHashcat, making it a convenient tool when working locally.

4. Dumping with Volume Shadow Copy

For systems where direct access is difficult, you can use Volume Shadow Copy to create snapshots of the registry files.

c

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vssadmin create shadow /for=C:

 

Then copy the SAM and SYSTEM files from the shadow location and parse them using tools like samdump2.

5. NTDSUtil for Domain Controllers

When dealing with domain controllers, you can use NTDSUtil to extract the NTDS.Do it safely:

cmd

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ntdsutil

Activate instance ntds

ifm

Create full c:\ntds

 

This method avoids live system tampering and is better suited for large-scale audits.

Preparing Extracted Hashes for Cracking

Once the hashes are dumped, some processing may be required to format them for use with CudaHashcat.

Cleaning Output Files

Depending on the tool used, the output may contain extra fields. Here’s a sample line from a pwdump file:

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Administrator:500:31D6CFE0D16AE931B73C59D7E0C089C0:AAD3B435B51404EEAAD3B435B51404EE:::

 

This format is perfect for hash mode 1000 in CudaHashcat, which expects lines in:

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username:uid:LM:NTLM:::

 

If only the NTLM hash is needed, you can filter it:

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cut -d ‘:’ -f 3,4 hashes.txt > ntlm_hashes.txt

 

Selecting Hash Mode

Hash mode selection is critical:

• 3000: LM

• 1000: NTLM

• 5600: NetNTLMv1

• 5500: NetNTLMv2

For our purposes, the most used modes are 1000 and 3000.

Cracking LM and NTLM Separately

When both LM and NTLM hashes are present, it’s best to crack them separately due to differing characteristics:

LM cracking example:

bash

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./hashcat -m 3000 -a 3 lm_hashes.txt ?a?a?a?a?a?a?a

 

NTLM cracking with a wordlist:

bash

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./hashcat -m 1000 -a 0 ntlm_hashes.txt rockyou.txt

 

Hash Analysis Techniques

After cracking, don’t just stop at the password list. Analyze the output to identify systemic issues.

Password Reuse Detection

If the same password is used by multiple accounts, this could signal poor policy enforcement. Use tools like uniq and sort to count occurrences.

bash

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cut -d ‘:’ -f 2 cracked.txt | sort | uniq -c | sort -nr

 

Weak Password Patterns

Look for patterns in passwords—months, seasons, company names. Create a custom wordlist to target those patterns in future audits.

Account Privilege Mapping

Match cracked hashes to account privilege levels. Compromised administrative accounts should be escalated to incident response immediately.

Data Sanitization

After completion, sanitize the environment. Hashes and cracked passwords are sensitive data and should be:

• Stored encrypted

• Deleted when no longer needed

• Never transferred via insecure channels

Logs and reports should redact full hashes when possible, showing only partial information or account-level summaries.

Common Pitfalls and How to Avoid Them

1. Hash extraction fails on protected systems
   • Solution: Use volume shadow copy or a bootable offline OS for file access.

2. Anti-virus blocks extraction tools
   • Solution: Run in a sandboxed lab or use AV evasion techniques responsibly.

3. Hashes not recognized by CudaHashcat.
   • Solution: Verify formatting and ensure you selected the correct hash mode.

4. No hashes cracked
   • Solution: Use rule-based attacks, hybrid techniques, and real-world wordlists.

Workflow Summary: From Extraction to Cracking

1. Extract hashes using authorized tools (Mimikatz, secretsdump, pwdump)

2. Format and clean the hash files

3. Choose appropriate hash modes (NTLM = 1000, LM = 3000)

4. Begin with dictionary attacks, move to hybrid and mask attacks.

5. Analyze cracked passwords for reuse and policy violations.s

6. Store results securely and responsibly disclose findings.

Cracking LM and NTLM hashes isn’t just about raw GPU power—it’s a blend of technical knowledge, ethical practice, and analytical skill. With tools like CudaHashcat, combined with proper hash extraction and cracking strategy, even strong defenses can be tested effectively.

Final Thoughts

Cracking LM and NTLM hashes using CudaHashcat is a powerful demonstration of how password security can be tested under real-world conditions. But it’s more than just a technical challenge—it’s a serious responsibility. Throughout this series, we’ve explored the architecture of LM and NTLM hashes, set up a high-performance CudaHashcat environment, employed advanced attack strategies, and learned how to extract hashes ethically and prepare them for cracking.

The key takeaway is this: password hashes are only as strong as the policies behind them. Organizations relying on outdated or weak hashing algorithms like LM are essentially inviting attackers in. Even NTLM, though more secure, is vulnerable to brute force and dictionary attacks if passwords aren’t strong and unique. This highlights the critical need for layered security—encryption, multifactor authentication, access controls, and regular auditing.

Security professionals must balance technical skill with ethical judgment. Always ensure proper authorization, maintain documentation, and disclose findings responsibly. Misuse of these techniques—even unintentionally—can lead to severe consequences.

In a world where data breaches are increasingly common, the ability to evaluate and improve password security is invaluable. Whether you’re a penetration tester, red teamer, or security analyst, mastering tools like CudaHashcat gives you an edge in identifying vulnerabilities before real attackers do.

Use this knowledge wisely—and always with the intent to protect, not to exploit.

 

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• Tags: Crack LM, CudaHashCat, NTLM