Adds Permutation Attack (menu option 19) that generates all character
permutations of each word in a targeted wordlist and pipes them to
hashcat via permute.bin from hashcat-utils.
- hcatPermute() in main.py: pipes permute.bin < wordlist | hashcat
- permute_crack() in attacks.py: prompts for single wordlist file with
factorial-growth warning, tab-autocomplete support
- Menu option 19 wired in both main.py and hate_crack.py
- hcatPermuteCount tracking alongside other count globals
- Tests: test_permute_attack.py (handler behavior) and
test_permute_wrapper.py (subprocess wiring)
- README: added entry in menu listing and attack descriptions
- Add hcatGenerateRules() in main.py: runs generate-rules.bin to
produce N random rules, writes them to a temp file, runs hashcat
with -r against a chosen wordlist, cleans up on exit
- Add generate_rules_crack() handler in attacks.py with count
prompt (default 65536), wordlist picker with tab-completion,
input validation, and abort on invalid input
- Add dispatcher generate_rules_crack() in main.py and key "20"
in both main.py and hate_crack.py get_main_menu_options()
- Add ("20", "Random Rules Attack") to get_main_menu_items()
- Add tests: test_random_rules_attack.py (menu presence, handler
wiring), test_random_rules_wrapper.py (subprocess behavior,
cleanup, count passing, count tracking)
- Add key "20" to MENU_OPTION_TEST_CASES in test_ui_menu_options.py
- Update README: add option 20 to menu listing, add attack
description, add version history entry
- Update test cases to reflect combinator_submenu for key 6
- Remove test cases for keys 10/11/12 (moved to sub-menu)
- Add test cases for new keys 17 and 18
- Simplify adhoc_mask tests to avoid global state issues
Add PassGPT as attack mode 17, using a GPT-2 model trained on leaked
password datasets to generate candidate passwords. The generator pipes
candidates to hashcat via stdin, matching the existing OMEN pipe pattern.
- Add standalone generator module (python -m hate_crack.passgpt_generate)
- Add [ml] optional dependency group (torch, transformers)
- Add config keys: passgptModel, passgptMaxCandidates, passgptBatchSize
- Wire up menu entries in main.py, attacks.py, and hate_crack.py
- Auto-detect GPU (CUDA/MPS) with CPU fallback
- Add unit tests for pipe construction, handler, and ML deps check
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add OMEN (Ordered Markov ENumerator) as a probability-ordered password
candidate generator. Trains n-gram models on leaked passwords via
createNG, then pipes candidates from enumNG into hashcat.
Also fix a pre-existing bug where ensure_binary() used quit(1) instead
of sys.exit(1) - quit() closes stdin before raising SystemExit, which
caused "ValueError: I/O operation on closed file" when any optional
binary check failed and the program continued to use input().
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Rename markov_attack → ollama_attack and hcatMarkov → hcatOllama across
menu, attacks, and tests. Remove candidate count prompts and cracked-output
default wordlist logic. Rename config keys (markov* → ollama*) and drop
ollamaUrl. Fix Dockerfile.test to use granular build steps.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add a new attack mode that uses a local LLM via Ollama to generate
password candidates, converts them into hashcat .hcstat2 Markov
statistics via hcstat2gen, and runs a Markov-enhanced mask attack.
Two generation sub-modes:
- Wordlist-based: feeds sample from an existing wordlist to the LLM
as pattern context (config-selectable default with Y/N override)
- Target-based: prompts for company name, industry, and location
for contextual password generation
Pipeline: Ollama API -> candidate file -> hcstat2gen -> LZMA compress
-> hashcat -a 3 --markov-hcstat2
Config additions: ollamaUrl, ollamaModel, markovCandidateCount,
markovWordlist. No new pip dependencies (uses stdlib urllib/lzma).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Justin Bollinger