wfh-wordlist 2.7.2

WordList For Hacking — Unified wordlist generation toolkit for pentest and red team operations

WordListsForHacking (WFH)

Unified wordlist generation toolkit for pentest and red team operations — 44 subcommands in a single CLI. Charset/mask generation, personal & corporate target profiling, web scraping (JS/CSS/PDF extraction), OCR, document parsing (PDF/XLSX/DOCX), leet speak permutations, XOR crypto, DNS/subdomain fuzzing, phone number generation, corporate user enumeration, retail/pharmacy chain credential patterns, default credential databases (IoT/ICS/SCADA/PLC/HMI), ISP WiFi keyspace generation, password-DNA behavioral analysis, keyword combiner, word mangling, merge & sanitize, ML-based ranking with SecLists corpus training, statistical analysis, PCFG probabilistic grammar generation, OMEN-style Markov chain generation, keyboard walk generation, automatic hashcat rule generation, PRINCE combinatorial chaining, wordlist quality benchmarking, phrase-initials acrostic generation, existing-password mutation engine, digit-to-text variants (EN/PT/BR/ES), global length filters, and disk-space safety checks.

Full documentation: Wiki

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DISCLAIMER: This tool is intended exclusively for authorized security testing, penetration testing, and educational purposes. Unauthorized use against systems you do not own or have explicit written permission to test is illegal and unethical. The author assumes no liability for misuse.

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Quick Start

Install via pip (optional)

pip install wfh-wordlist                # core
pip install wfh-wordlist[full]         # all extras (OCR, docs, scrape)

Clone from source

git clone https://github.com/mrhenrike/WordListsForHacking.git
cd WordListsForHacking

pip install -r requirements.txt pyyaml

# Linux / macOS / Termux (optional venv)
chmod +x setup_venv.sh && ./setup_venv.sh && source .venv/bin/activate

# Windows PowerShell
.\setup_venv.ps1; .\.venv\Scripts\Activate.ps1

Run

python wfh.py              # interactive menu
python wfh.py --help       # full CLI help (44 subcommands)
wfh --help                 # after pip install

Packaging: PyPI package wfh-wordlist mirrors this repo. Local pyproject.toml is not tracked in git; copy pyproject.toml.example for editable installs.

OS prerequisites (OCR only): see the Installation wiki page.

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Subcommands

#	Command	Description
1	charset	Charset/mask generation (crunch-style + hashcat masks)
2	pattern	Template-based generation with variables
3	profile	Personal target profiling (CUPP-style)
4	corp	Corporate target profiling
5	corp-users	Corporate domain user/password generation (50+ patterns)
6	phone	Phone number wordlists (BR, US, UK)
7	scrape	Web scraping (CeWL/CeWLeR-style) with JS/CSS/PDF extraction
8	ocr	OCR text extraction from images
9	extract	Extract words from PDF/XLSX/DOCX
10	leet	Leet speak permutations
11	xor	XOR encrypt/decrypt/brute-force
12	analyze	Statistical analysis (pipal-style)
13	merge	Merge & deduplicate wordlists
14	dns	DNS/subdomain fuzzing (alterx-style)
15	pharma	Healthcare/pharmacy credential patterns
16	sanitize	Clean & normalize wordlists
17	reverse	Reverse line order
18	corp-prefixes	Corporate prefix usernames (MSP/SOC/DevOps)
19	train	Train ML pattern model (local + SecLists corpus)
20	sysinfo	Hardware & compute info
21	mangle	Word mangling rules
22	default-creds	Query default credentials database (IoT/routers/printers/ICS)
23	isp-keygen	ISP default WiFi password keyspace generator
24	combiner	Keyword combiner (intelligence-wordlist-generator style)
25	password-dna	Analyze password patterns and generate behavioral variants
26	pcfg	PCFG probabilistic grammar — train and generate (Weir et al.)
27	markov	OMEN-style positional Markov chain generator
28	kwalk	Keyboard walk password generator (kwprocessor-style)
29	rulegen	Auto-generate hashcat .rule files from password analysis
30	benchmark	Wordlist quality benchmarking (MAYA-inspired metrics)
31	prince	PRINCE attack — chained element combination
32	phrase	Phrase-initials acrostic password generator (@0x90 / hacker-suffix style)
33	mutate	Existing-password mutation engine (case / leet / prefix / suffix)
34	pharma	Retail/pharmacy chain credential patterns (brand+id, system+taxid, usernames)
35	br-names	Brazilian name-based username generator
36	num2text	Digit-to-text wordlist generator with case/leet/separator variants (EN/PT/BR/ES)

Detailed syntax and examples for each subcommand: Wiki — Subcommands

Global Flags

python wfh.py --threads 20 --compute cuda --no-ml --min-len 8 --max-len 20 <subcommand>

Flag	Default	Description
--threads N	5	Thread count (1–300)
--compute MODE	auto	auto / cpu / gpu / cuda / rocm / mps / hybrid
--no-ml	off	Disable ML ranking
--min-len N	0	Global minimum word length filter (applied to all commands)
--max-len N	0	Global maximum word length filter (applied to all commands)
-v	off	Verbose logging

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Common Usage Examples

Corporate pentest — generate users + passwords

python wfh.py corp-users --domain acme.com.br --file employees.txt --passwords --combo -o acme_combo.lst

Personal target profiling

python wfh.py profile --name "João Silva" --nick joao --birth 15/03/1990 --leet aggressive -o target.lst

Charset with hashcat mask

python wfh.py charset 8 8 --mask "?u?l?l?l?d?d?d?s" -o passwords.lst

Template-based patterns

python wfh.py pattern -t "{company}{year}!" --vars company=acme,globex year=2020-2026 -o patterns.lst

DNS subdomain fuzzing

python wfh.py dns -d acme.com.br --words dev staging api admin portal -o subdomains.lst

Analyze an existing wordlist

python wfh.py analyze passwords.lst --top 30 --masks --format json -o analysis.json

Default credentials lookup

python wfh.py default-creds --list-vendors
python wfh.py default-creds --vendor mikrotik --format combo -o mikrotik_creds.lst
python wfh.py default-creds --protocol snmp --format user -o snmp_users.lst

ISP WiFi keyspace generation

python wfh.py isp-keygen --list
python wfh.py isp-keygen --isp xfinity_comcast --estimate
python wfh.py isp-keygen --isp xfinity_comcast --limit 100000 -o xfinity.lst

Web scraping with JS/CSS/PDF

python wfh.py scrape https://target.com --include-js --include-css --include-pdf --lowercase -o words.lst
python wfh.py scrape https://target.com --emails --output-emails emails.txt --output-urls urls.txt
python wfh.py scrape https://target.com --subdomain-strategy children --stream -o stream.lst

Merge & sanitize

python wfh.py merge list1.lst list2.lst --min-len 6 --sort -o merged.lst
python wfh.py sanitize merged.lst --inplace

More examples and scenarios: Wiki — Quick Start

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Password DNA

Analyze password patterns and generate behavioral variants. The password-dna subcommand extracts structural "DNA" from known passwords (uppercase, lowercase, digit, symbol positions) and produces new candidates that follow the same behavioral patterns.

# Analyze a leaked/known password list and generate variants
python wfh.py password-dna --input known_passwords.lst --depth 2 -o dna_variants.lst

# Generate variants from a single seed with aggressive expansion
python wfh.py password-dna --seed "Company2024!" --depth 3 --leet -o seed_variants.lst

# DNA analysis report only (no generation)
python wfh.py password-dna --input known_passwords.lst --analyze-only --format json -o dna_report.json

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PCFG Grammar Engine

Train a Probabilistic Context-Free Grammar from a password corpus and generate candidates in probability order (most likely first). Based on Weir et al. (IEEE S&P 2009).

# Train a grammar from a password corpus
python wfh.py pcfg train --wordlist rockyou.txt

# Generate candidates (probability-ordered)
python wfh.py pcfg generate -o candidates.lst --limit 1000000

# Fine-tune with structure/terminal limits
python wfh.py pcfg generate --top-structures 50 --top-terminals 100 --min-len 8

Markov Chain Generator

OMEN-style positional Markov chain generator. Learns per-position character transitions and generates in ascending cost order (most probable first).

# Train a Markov model (order 3)
python wfh.py markov train --wordlist leaked.txt --order 3

# Generate candidates with cost threshold
python wfh.py markov generate --min-len 6 --max-len 12 --max-cost 30 --limit 500000

Keyboard Walk Generator

Generate passwords based on physical keyboard adjacency walks. Supports QWERTY, AZERTY, QWERTZ, Dvorak, and numpad layouts.

# Generate QWERTY walks (length 4-10)
python wfh.py kwalk --min-len 4 --max-len 10 -o walks.lst

# Multiple layouts, no shift layer
python wfh.py kwalk --layout qwerty,numpad --no-shift --max-changes 2

# List available layouts
python wfh.py kwalk --list-layouts

Hashcat Rule Auto-Generation

Analyze real passwords and automatically generate hashcat-compatible .rule files by reverse-engineering transformation patterns.

# Generate a .rule file from password analysis
python wfh.py rulegen --wordlist leaked.txt -o rules.rule --top-rules 200

# With a dictionary for better base-word matching
python wfh.py rulegen --wordlist passwords.lst --dictionary english.txt -o optimized.rule

PRINCE Attack Mode

PRINCE (PRobability INfinite Chained Elements) generates passwords by combining multiple words from a wordlist. Discovers multi-word passwords like correcthorsebatterystaple.

# Chain 2-4 elements from a base wordlist
python wfh.py prince --wordlist top1000.txt --min-elem 2 --max-elem 4 -o prince.lst

# With separator and case permutations
python wfh.py prince --wordlist words.txt --separator "-" --case-permute --min-len 8

Wordlist Quality Benchmark

Measure the effectiveness of a generated wordlist against a reference set. Reports hit rate, efficiency, diversity, coverage by length/charset, and estimated crack times.

# Benchmark a wordlist against a known password set
python wfh.py benchmark --wordlist generated.lst --reference rockyou_sample.txt

# Save JSON report
python wfh.py benchmark --wordlist output.lst --reference test_set.txt --json report.json

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Default Credentials Database

Query the built-in database of 1,329+ factory-default credentials covering 88 vendors and 14 protocols — routers, switches, printers, IP cameras, ICS/SCADA (PLCs, HMIs, RTUs), IoT gateways, and more.

# List all supported vendors
python wfh.py default-creds --list-vendors

# Export credentials for a specific vendor
python wfh.py default-creds --vendor siemens --format combo -o siemens_creds.lst

# Filter by protocol (telnet, ssh, http, snmp, modbus, s7comm, etc.)
python wfh.py default-creds --protocol modbus --format user -o modbus_users.lst

# Search by device category
python wfh.py default-creds --category ics --format combo -o ics_defaults.lst

# Export full database as JSON
python wfh.py default-creds --export-all --format json -o all_defaults.json

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Wordlists

File	Description	Entries
passwords/wlist_brasil.lst	Brazilian password corpus — cultural word banks, corporate patterns, leet speak, keyboard walks. Company names and CNPJs are public OSINT data.	~3.88M
passwords/default-creds-combo.lst	Default credential user:password combos (routers, printers, ICS/SCADA)	~3K
data/default_credentials.json	Structured default credentials database (1,329 entries, 88 vendors, 14 protocols)	—
fuzzing/discovery_br.lst	Brazilian web discovery & API fuzzing paths	~900
usernames/username_br.lst	Brazilian + global username patterns	~1.6K
labs/*.lst	Workshop & training wordlists	—

Details: Wiki — Brazilian Wordlist

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Is My Password in This List?

# Linux/macOS
grep -qxF 'YourPassword' passwords/wlist_brasil.lst && echo "FOUND!" || echo "Not found"

# Windows PowerShell
Select-String -Path passwords\wlist_brasil.lst -Pattern '^YourPassword$' -SimpleMatch -Quiet

If found: change it immediately, enable MFA/2FA, use a password manager, and never reuse passwords.

Full guide: Wiki — Password Check

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ML Model

WFH includes a lightweight ML model that ranks generated candidates by structural pattern probability. Train it with local data or the SecLists corpus:

python wfh.py train --auto                    # local wordlists only
python wfh.py train --seclists                # SecLists corpus (auto-discover)
python wfh.py train --auto --seclists         # combined (recommended)
python wfh.py train --seclists /path/to/SecLists --seclists-categories password frequency

The model stores only structural patterns — no PII, passwords, or company names.

Details: Wiki — ML Model

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New in v2.6 — Additional Generators

Phrase-Initials Password Generator

Generate passwords from the first letter of each word in a phrase, with case mutations, leet substitutions, and hacker-style suffixes.

# Phrase → acrostic + variations
python wfh.py phrase "my secret corporate phrase" -o phrase.lst

# With custom prefixes and suffixes
python wfh.py phrase "my secret corporate phrase" --prefixes _,__ --suffixes @0x90,#0x90 -o phrase.lst

Existing Password Mutation Engine

Generate an exhaustive set of variants from an existing base password.

# Mutate a known password
python wfh.py mutate "Summer2024" -o mutated.lst

# Control leet depth and length range
python wfh.py mutate "password123" --leet-mode aggressive --min-len 10 --max-len 25 -o mutated.lst

Retail / Pharmacy Chain Credential Generator

Generates passwords and usernames following patterns common in retail environments: brand + store-id, system + tax-id, internal login prefixes.

# Both passwords and usernames for a brand
python wfh.py pharma --brand AcmePharma --ids 1200-1210 -o pharma.lst

# Passwords only, with tax ID (CNPJ)
python wfh.py pharma --brand RetailCo --abbrevs RC,RET --cnpj 01234567890123 --mode passwords

# Usernames only, custom domain
python wfh.py pharma --brand BrandX --ids 1000-2000 --domains corp.com.br --mode usernames

Digit-to-Text Wordlist Generator

Converts numbers (up to 12 digits) into their text word representations with full variant generation. Supports EN, PT, BR (with feminine forms), and ES.

# Single number in English (default)
python wfh.py num2text --number 123
# → onetwothree, ONETWOTHREE, OneTwoThree, 0n37w07hr33, one-two-three, ...

# Brazilian Portuguese (includes feminine variants: uma, duas)
python wfh.py num2text --number 12 --lang br
# → umdois, umaduas, Um-Duas, um_duas, ...

# Spanish
python wfh.py num2text --number 123 --lang es
# → unodostres, UNODOSTRES, uno-dos-tres, una-dos-tres, ...

# Batch range, saved to file
python wfh.py num2text --range 0-9999 --lang en -o number_words.lst
python wfh.py num2text --range 2000-2030 --lang pt -o years_pt.lst

Accepted --lang aliases:

Code	Also accepts	Language
en	en-us, en-gb	English (default)
pt	pt-pt	European Portuguese
br	pt-br	Brazilian Portuguese
es	es-es, es-mx, es-la	Spanish

Global Length Filters

Apply minimum/maximum word length filtering to any subcommand output.

python wfh.py --min-len 8 --max-len 20 charset 8 12 -o filtered.lst
python wfh.py --min-len 10 mutate "admin" -o long_variants.lst

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Credits & Inspiration

Project	Inspiration
CUPP	Personal target profiling
Crunch	Charset-based generation
CeWL	Web scraping for wordlists
CeWLeR	Modern Python web scraping (JS/CSS/PDF)
routersploit	Default credentials for IoT/routers
alterx	DNS/subdomain fuzzing
pipal	Statistical analysis
SecLists	Curated security lists
elpscrk	Permutation-based generation
BEWGor	Biographical wordlist generator
pnwgen	Phone number generation
intelligence-wordlist-generator	Keyword combiner
SCaDAPass	ICS/SCADA default credentials
pcfg_cracker	PCFG probabilistic grammar (Weir et al.)
OMEN	Ordered Markov ENumerator
kwprocessor	Keyboard walk generation
PACK	Password Analysis and Cracking Kit (rulegen)
princeprocessor	PRINCE attack mode
MAYA	Wordlist quality benchmarking framework

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Contact

• Support / general inquiries: [e-mail]
• Security issues: SECURITY.md
• Organization: União Geek

Contributing

Contributions welcome. See CONTRIBUTING.md.

License

MIT License — Copyright (c) 2026 André Henrique (@mrhenrike)

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Created by André Henrique (@mrhenrike) — União Geek
[e-mail]

Leia em Português · Full Documentation (Wiki)