Skip to main content

shouldersurfscore

This library helps researchers in lab settings develop better metrics to understand the practical password guess quality of shoulder surfing and password guessing attacks.

The library provides the following (their complexity for you to use in parentheses):

  • (Advanced): a nuanced set of classes to build an experiment environment including different keyboard layouts, device lockout patterns, and different styles of attackers that can help to better estimate different
  • (Medium): predefined equipment to make it easier to get up and running (e.g. an iPhone, with common login restrictions).
  • (Easy): defined scores to make it easier to reproduce other researchers' experiments (and when you're ready, hopefully yours too!).
  • (Easy): implementations of a few other common metrics for assessing password quality.

Installation

To install, simply use:

pip install shouldersurfscore

How-To Use

Defined Labs

Pre-defined labs can be used to recreate scores used in others' experiments.

For example:

from shouldersurfscore.defined_experiments.built_labs import InitialShoulderSurfScorePaperLab

analysis = InitialShoulderSurfScorePaperLab.run(
    actual_password='9163',
    observed_password='9613'
)
print(analysis)
Break In Analysis
------------------
actual_password: 9163
observed_password: 9613
password_index: 2
password_index_percent: 1.9801980198019803e-06
elapsed_time: 0s
device_unlocked: True

Other Metrics

Predefined Objects

from shouldersurfscore.analysis.breakin_analysis import BreakInAnalysis
from shouldersurfscore.classes import guessing_strategies
from shouldersurfscore.classes.lab import Lab
from shouldersurfscore.equipment.devices import Devices

device = Devices.get_iphone()
# Define strategies
pin_lengths = [4, 6]
# If the observed password doesn't work
# then all possible passwords will be tried in sequential order
# until successful or device goes into lock out.
strategies: list[guessing_strategies.GuessingStrategy] = [
    # Initialize brute force method with which characters are valid
    # and which pin lengths are valid
    guessing_strategies.BruteForceGuessing(device.keyboard.characters, pin_lengths)
]
password = "2290"
observed_password = "9163"
analysis = Lab.run(device, strategies, password, observed_password)
print(analysis)
Break In Analysis
------------------
actual_password: 2290
observed_password: 9163
password_index: 1190
password_index_percent: 0.0011782178217821782
elapsed_time: 1h 21m
device_unlocked: False

Classes

Metadata

Release files for shouldersurfscore 1.0.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for shouldersurfscore 1.0.3
File Size Uploaded Provenance Actions
shouldersurfscore-1.0.3.tar.gz 52.0 kB Without provenance Details for shouldersurfscore-1.0.3.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for shouldersurfscore 1.0.3
File Interpreter ABI Platform Actions
shouldersurfscore-1.0.3-py3-none-any.whl Python 3 none any Details for shouldersurfscore-1.0.3-py3-none-any.whl

Total release size: 100.3 kB

Release files / shouldersurfscore-1.0.3.tar.gz

Download URL shouldersurfscore-1.0.3.tar.gz
Size 52.0 kB
Tags Source
SHA-256 checksum
How to use checksums
561c5c6f01f548da5990b72e2e0bb333cf5250b94008b05b393c304cf3e6c4eb
BLAKE2b-256 checksum
How to use checksums
684634f8a67c59046010642eef6a3db2a7e2ef26b6ed859313b2cf0d3e0b18e3
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.13.3

Release files / shouldersurfscore-1.0.3-py3-none-any.whl

Download URL shouldersurfscore-1.0.3-py3-none-any.whl
Size 48.3 kB
Tags Python 3
SHA-256 checksum
How to use checksums
d0d466c4fba4612512c23b2276350a445ef45f501b4d34f29e33f8e10daa2768
BLAKE2b-256 checksum
How to use checksums
49120c49a4db87539d8ce008a4d02204b8c091e06e52a1b10e3ccfcad1f8551a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.13.3

Security for shouldersurfscore 1.0.3

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page