Results Analysis & Post-Processing

Cracking hashes is only half the job. This lesson covers how to save, organize, analyze, and report on cracked passwords using hashcat's built-in tools and Python post-processing.

Learning Objectives

• Configure output file formats with --outfile-format
• Save and manage results with -o and session management
• Use --show and --left to inspect cracked and uncracked hashes
• Build Python scripts for cleaning, analyzing, and visualizing password data
• Create professional password analysis reports

Output File Formats

The --outfile-format flag controls the structure of saved results. Choose the format that matches your reporting needs.

Saving Results & Session Management

Hashcat provides robust mechanisms for saving output and resuming interrupted sessions.

Saving Results

# Save cracked passwords to a file
hashcat -m 0 hashes.txt wordlist.txt -o results.txt

# Save with specific format (login:password)
hashcat -m 0 hashes.txt wordlist.txt -o results.txt --outfile-format 4

Session Management

# Start a named session
hashcat -m 0 hashes.txt wordlist.txt --session mysession

# Resume an interrupted session
hashcat --session mysession --restore

Inspecting Results: --show and --left

After cracking, use these flags to review what was and was not recovered.

hashcat -m 0 --show hashes.txt

hashcat -m 0 --left hashes.txt

Python Post-Processing

Raw hashcat output needs cleaning and analysis. Python with pandas and matplotlib is ideal for this.

Cleaning Results

import pandas as pd

# Read hashcat output (format 3: hash:password)
df = pd.read_csv('results.txt', sep=':', header=None,
                 names=['hash', 'password'])
df.drop_duplicates(inplace=True)
df.to_csv('clean_results.csv', index=False)
print(f"Unique passwords: {df['password'].nunique()}")

Frequency Analysis

# Top 10 most common passwords
top = df['password'].value_counts().head(10)
print(top)

Visualization & Reporting

Transform raw data into actionable insights with charts and summary statistics.

import matplotlib.pyplot as plt

# Password length distribution
df['length'] = df['password'].str.len()
df['length'].hist(bins=20, color='#1f6feb', edgecolor='#0d1117')
plt.xlabel('Password Length')
plt.ylabel('Count')
plt.title('Password Length Distribution')
plt.savefig('length_distribution.png', dpi=150, bbox_inches='tight')
plt.show()

Key Report Metrics

• Crack rate: % of hashes recovered vs total
• Top passwords: Most common passwords (often "password", "123456")
• Length distribution: How many passwords are under 8 characters
• Complexity analysis: % with uppercase, digits, special characters

Complexity Analysis

Categorize cracked passwords by their complexity to identify policy weaknesses.

import re

def classify(pw):
    has_upper = bool(re.search(r'[A-Z]', pw))
    has_lower = bool(re.search(r'[a-z]', pw))
    has_digit = bool(re.search(r'[0-9]', pw))
    has_special = bool(re.search(r'[^A-Za-z0-9]', pw))
    score = sum([has_upper, has_lower, has_digit, has_special])
    if score <= 1: return 'Weak'
    if score == 2: return 'Fair'
    if score == 3: return 'Good'
    return 'Strong'

df['complexity'] = df['password'].apply(classify)
print(df['complexity'].value_counts())

Lab & Homework