When I first started digging into password security research, I kept hearing the same name from people who do password auditing for a living: Pipal. It’s a Perl-based command-line tool built specifically to chew through huge password dumps and spit out statistics that actually mean something — length distributions, character set usage, the most common base words, and patterns that reveal how people really choose passwords when left to their own devices. In this article I’ll walk through everything I’ve learned about Pipal, from installing it to reading its output like a professional auditor.
What Pipal Actually Does
Pipal isn’t a cracking tool. It doesn’t try to guess or brute-force anything. Instead, I think of it as a statistics engine for a list of already known plaintext passwords — the kind of list you get after a successful password audit, a legally obtained breach corpus for research, or a controlled red-team engagement where you’ve already recovered credentials. It reads a plain text file (one password per line) and produces a report covering:
- Total password count and basic length statistics
- Character set breakdown (lowercase only, uppercase, digits, special characters, mixed)
- Most popular passwords and base words (with number/symbol suffixes stripped)
- Complexity comparisons against common policy requirements
- Year and common keyboard pattern detection
Internally, Pipal is a single Perl script with a plugin-style module system. Each “analysis” (length, character sets, word patterns, etc.) is essentially a small function that increments counters as it streams through the file line by line, then formats the results at the end. Because it’s pure Perl with minimal dependencies, it runs almost anywhere.
Installing Pipal
I’ve tested this on Debian/Ubuntu and Kali:
sudo apt update
sudo apt install -y pipal
If it’s not in your distro’s repos, cloning directly from GitHub works just as well:
git clone https://github.com/digininja/pipal.git
cd pipal
perl pipal.pl --help
Since it’s Perl, you may need a couple of core modules:
sudo apt install -y perl
sudo cpan Getopt::Long
Basic Syntax
perl pipal.pl [options] passwordfile.txt
Common options I use regularly:
perl pipal.pl -t passwords.txt # top N counts
perl pipal.pl -o report.txt passwords.txt # write output to a file
perl pipal.pl --topn 20 passwords.txt # customize how many top entries to show
A Real Example
I created a small authorized lab file (never use real breach data without legal authorization):
cat > passwords.txt << 'EOF'
Summer2024!
password123
Winter2023
qwerty123
Summer2024!
letmein1
Passw0rd
Summer2025
EOF
perl pipal.pl passwords.txt
Output (trimmed):
Basic Results
Total entries = 8
Total unique entries = 7
Top 5 passwords
Summer2024! = 2 (25%)
password123 = 1 (12.5%)
...
Length
7 = 1 (12.5%)
9 = 3 (37.5%)
11 = 4 (50%)
Character sets
loweralphanum = 3 (37.5%)
loweralphaspecialnum = 2 (25%)
mixedalphaspecialnum = 3 (37.5%)
Basic word analysis
summer = 2 (25%)
winter = 1 (12.5%)
password = 1 (12.5%)
This is exactly the kind of report I hand to a client after a password audit — it tells them, in plain numbers, that their staff love seasonal words plus a year and an exclamation mark, which is a pattern any decent wordlist-based attack would exploit in seconds.
Workflow and Integration
I typically pair Pipal with:
- John the Ripper / Hashcat — crack a hash dump first (with authorization), then feed the cracked plaintexts into Pipal for statistical reporting.
- CeWL — generate a custom wordlist from a target’s website, then compare against Pipal’s “base word” output to see if organizational branding shows up in real passwords.
- Excel/CSV export — I redirect Pipal’s output into a text file and parse it with a quick Python script to build charts for management-facing reports.
Performance and Troubleshooting
For very large dumps (millions of lines), Pipal is single-threaded, so I’ve learned to:
- Split the file with
split -l 1000000 passwords.txt chunk_and run instances in parallel, then merge the summary numbers manually. - Ensure the input file is UTF-8; malformed encoding throws warnings and skews character-set counts.
- Watch memory usage — very large files should be processed on a machine with sufficient RAM since Pipal holds counters in memory as it streams.
A mistake I made early on: feeding it a file with hashes instead of plaintext passwords. Pipal only works meaningfully on cracked/plaintext passwords — never raw hashes.
Best Practices
- Only ever run Pipal against passwords you have explicit written authorization to analyze (internal audits, your own test lab, or public research corpora meant for academic use).
- Strip out duplicate accounts/emails before analysis so your statistics reflect actual password choices, not one person’s password appearing multiple times due to shared accounts.
- Combine numeric stats with qualitative sampling — reading a handful of the most common raw passwords gives intuition that plain numbers don’t.
FAQ
Does Pipal crack passwords? No. It only analyzes plaintext passwords you already have.
Can I use it on a live Active Directory export? Only after you’ve already recovered plaintext values (e.g., via an authorized DPAT or crack session) — Pipal doesn’t touch hashes.
Is it actively maintained? It’s a mature, stable tool; the GitHub repo still receives community contributions and bug fixes.
Summary
Pipal turns a raw pile of passwords into an evidence-based story about how people actually choose credentials inside an organization. I use it after every authorized password audit because it converts abstract “your passwords are weak” statements into concrete, quotable statistics that get security budgets approved.
References
- Official GitHub repository: https://github.com/digininja/pipal
- Digininja’s blog and tool documentation: https://digi.ninja/projects/pipal.php
