passeval 0.1.1

Offline ML-powered password strength evaluation using a trained Random Forest model.

passeval

Offline ML-powered password strength evaluation for Python developers.

passeval uses a trained Random Forest classifier to score passwords as Weak, Medium, or Strong — with confidence scores and actionable feedback. Everything runs locally: no API calls, no servers, no internet required.

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Installation

pip install passeval

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Quick Start

from passmeter import evaluate_password

result = evaluate_password("Monkey2024!")
print(result)

Output:

{
  "score": 0,
  "label": "Weak",
  "confidence": 1.0,
  "features": {
    "length": 11,
    "entropy": 3.2776,
    "num_upper": 1,
    "num_digits": 4,
    "num_special": 1
  },
  "feedback": [
    "Longer passwords are significantly harder to crack",
    "Avoid predictable patterns like years or repeated digits"
  ]
}

Note: The Python import is from passmeter import ... — this is the name of the internal package module. The PyPI distribution name (used for pip install) is passeval.

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API Reference

evaluate_password(password: str) → dict

Evaluates a password and returns a structured result.

Key	Type	Description
score	int	Numeric strength: 0 = Weak, 1 = Medium, 2 = Strong
label	str	Human-readable label matching the score
confidence	float	Model probability for the predicted class (0.0 – 1.0)
features	dict	Key statistics extracted from the password
feedback	list	Actionable suggestions; empty list means no issues found

Raises:

• TypeError – if input is not a string
• ValueError – if input is an empty string

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extract_features(password: str) → dict

Returns all 10 raw statistical features used by the model.

from passmeter import extract_features

extract_features("Monkey2024!")
# {
#   'length': 11, 'num_upper': 1, 'num_lower': 6,
#   'num_digits': 4, 'num_special': 1, 'entropy': 3.2776,
#   'unique_chars': 10, 'has_upper': 1, 'has_digit': 1, 'has_special': 1
# }

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More Examples

from passmeter import evaluate_password

# Weak password
r = evaluate_password("hunter2")
print(r["label"])       # Weak
print(r["feedback"])    # ['Use at least 8 characters', 'Add at least one uppercase letter', ...]

# Medium password
r = evaluate_password("Password1")
print(r["label"])       # Medium
print(r["confidence"])  # 0.86

# Strong password
r = evaluate_password("xK9#mP2$vL8@")
print(r["label"])       # Strong
print(r["confidence"])  # 1.0
print(r["feedback"])    # []

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How It Works

Feature Extraction

passeval computes 10 statistical features from each password:

Feature	Description
length	Total number of characters
num_upper	Count of uppercase letters
num_lower	Count of lowercase letters
num_digits	Count of digit characters
num_special	Count of non-alphanumeric characters
entropy	Shannon entropy (bits per character)
unique_chars	Number of distinct characters used
has_upper	Boolean: contains at least one uppercase
has_digit	Boolean: contains at least one digit
has_special	Boolean: contains at least one special char

ML Model

A Random Forest classifier was trained on a labeled password dataset. It learns non-linear relationships between these features and password strength — going beyond simple rule matching.

predict_proba() is used to surface a confidence score alongside the predicted class, giving callers a sense of how certain the model is.

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ML vs Rule-Based: Why Not zxcvbn?

zxcvbn is a well-known rule-based estimator that checks passwords against wordlists, keyboard patterns, and dates. It is excellent for its use case, but it has some differences from passeval:

Feature	passeval (ML)	zxcvbn (rule-based)
Approach	Random Forest classifier	Pattern + dictionary rules
Scores derived from	Learned statistical patterns	Hardcoded heuristics
Confidence score	Yes (model probability)	No
Customisable training data	Yes (retrain on your data)	No
Wordlist dependency	None	Bundled ~30k word list
Generalises to novel patterns	Yes (via feature stats)	Limited to known patterns
Fully offline	Yes	Yes

Both tools have value. passeval is better suited when you want ML-derived confidence scores, a trainable model, or a smaller dependency footprint.

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Offline Advantage

passeval ships the trained model file (passmeter_model.pkl) inside the package itself. Once installed via pip, it works in:

• Air-gapped servers and secure environments
• CI/CD pipelines with no outbound network access
• Embedded systems and edge deployments
• Any environment where calling an external API is undesirable

No credentials, no rate limits, no latency from a remote service.

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License

MIT — see LICENSE.