# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import random
from pathlib import Path
from typing import Literal, Optional
from pyrit.datasets.dataset_helper import FILE_TYPE_HANDLERS, fetch_examples
from pyrit.models import SeedDataset, SeedPrompt
[docs]
def fetch_seclists_bias_testing_dataset(
source: str = (
"https://raw.githubusercontent.com/danielmiessler/SecLists/4e747a4/Ai/LLM_Testing/Bias_Testing/"
"nationality_geographic_bias.txt"
),
source_type: Literal["public_url"] = "public_url",
cache: bool = True,
data_home: Optional[Path] = None,
random_seed: Optional[int] = None,
country: Optional[str] = None,
region: Optional[str] = None,
nationality: Optional[str] = None,
gender: Optional[str] = None,
skin_color: Optional[str] = None,
) -> SeedDataset:
"""
Fetch SecLists AI LLM Bias Testing examples from a specified source and create a SeedDataset.
Args:
source (str): The source from which to fetch examples. Defaults to the SecLists repository Bias_Testing.
source_type (Literal["public_url"]): The type of source ('public_url').
cache (bool): Whether to cache the fetched examples. Defaults to True.
data_home (Optional[Path]): Directory to store cached data. Defaults to None.
random_seed (Optional[int]): Seed for random number generation for reproducibility. Defaults to None.
country (Optional[str]): Specific country to use for the placeholder. Defaults to None.
region (Optional[str]): Specific region to use for the placeholder. Defaults to None.
nationality (Optional[str]): Specific nationality to use for the placeholder. Defaults to None.
gender (Optional[str]): Specific gender to use for the placeholder. Defaults to None.
skin_color (Optional[str]): Specific skin color to use for the placeholder. Defaults to None.
Returns:
SeedDataset: A SeedDataset containing the examples with placeholders replaced.
Raises:
ValueError: If the file type is invalid.
"""
if random_seed is not None:
random.seed(random_seed)
# Determine the file type from the source URL
file_type = source.split(".")[-1]
if file_type not in FILE_TYPE_HANDLERS:
valid_types = ", ".join(FILE_TYPE_HANDLERS.keys())
raise ValueError(f"Invalid file_type. Expected one of: {valid_types}.")
# Fetch the examples using the provided `fetch_examples` function
examples = fetch_examples(source, source_type, cache, data_home)
# Hardcoded list of country names to avoid external dependency
country_names = [
"Afghanistan",
"Albania",
"Algeria",
"Andorra",
"Angola",
"Antigua and Barbuda",
"Argentina",
"Armenia",
"Australia",
"Austria",
"Azerbaijan",
"Bahamas",
"Bahrain",
"Bangladesh",
"Barbados",
"Belarus",
"Belgium",
"Belize",
"Benin",
"Bhutan",
"Bolivia",
"Bosnia and Herzegovina",
"Botswana",
"Brazil",
"Brunei",
"Bulgaria",
"Burkina Faso",
"Burundi",
"Cabo Verde",
"Cambodia",
"Cameroon",
"Canada",
"Central African Republic",
"Chad",
"Chile",
"China",
"Colombia",
"Comoros",
"Congo",
"Costa Rica",
"Cote d'Ivoire",
"Croatia",
"Cuba",
"Cyprus",
"Czechia",
"Democratic Republic of the Congo",
"Denmark",
"Djibouti",
"Dominica",
"Dominican Republic",
"Ecuador",
"Egypt",
"El Salvador",
"Equatorial Guinea",
"Eritrea",
"Estonia",
"Eswatini",
"Ethiopia",
"Fiji",
"Finland",
"France",
"Gabon",
"Gambia",
"Georgia",
"Germany",
"Ghana",
"Greece",
"Grenada",
"Guatemala",
"Guinea",
"Guinea-Bissau",
"Guyana",
"Haiti",
"Honduras",
"Hungary",
"Iceland",
"India",
"Indonesia",
"Iran",
"Iraq",
"Ireland",
"Italy",
"Jamaica",
"Japan",
"Jordan",
"Kazakhstan",
"Kenya",
"Kiribati",
"North Korea",
"South Korea",
"Kuwait",
"Kyrgyzstan",
"Laos",
"Latvia",
"Lebanon",
"Lesotho",
"Liberia",
"Libya",
"Liechtenstein",
"Lithuania",
"Luxembourg",
"Madagascar",
"Malawi",
"Malaysia",
"Maldives",
"Mali",
"Malta",
"Marshall Islands",
"Mauritania",
"Mauritius",
"Mexico",
"Micronesia",
"Moldova",
"Monaco",
"Mongolia",
"Montenegro",
"Morocco",
"Mozambique",
"Myanmar",
"Namibia",
"Nauru",
"Nepal",
"Netherlands",
"New Zealand",
"Nicaragua",
"Niger",
"Nigeria",
"North Macedonia",
"Norway",
"Oman",
"Pakistan",
"Palau",
"Palestine",
"Panama",
"Papua New Guinea",
"Paraguay",
"Peru",
"Philippines",
"Poland",
"Portugal",
"Qatar",
"Romania",
"Russia",
"Rwanda",
"Saint Kitts and Nevis",
"Saint Lucia",
"Saint Vincent and the Grenadines",
"Samoa",
"San Marino",
"Sao Tome and Principe",
"Saudi Arabia",
"Senegal",
"Serbia",
"Seychelles",
"Sierra Leone",
"Singapore",
"Slovakia",
"Slovenia",
"Solomon Islands",
"Somalia",
"South Africa",
"South Sudan",
"Spain",
"Sri Lanka",
"Sudan",
"Suriname",
"Sweden",
"Switzerland",
"Syria",
"Tajikistan",
"Tanzania",
"Thailand",
"Timor-Leste",
"Togo",
"Tonga",
"Trinidad and Tobago",
"Tunisia",
"Turkey",
"Turkmenistan",
"Tuvalu",
"Uganda",
"Ukraine",
"United Arab Emirates",
"United Kingdom",
"United States",
"Uruguay",
"Uzbekistan",
"Vanuatu",
"Vatican City",
"Venezuela",
"Vietnam",
"Yemen",
"Zambia",
"Zimbabwe",
]
subdivision_names = [
"California",
"Texas",
"New York",
"Florida",
"Illinois",
"Pennsylvania",
"Ohio",
"Georgia",
"North Carolina",
"Michigan",
"New Jersey",
"Virginia",
"Washington",
"Arizona",
"Massachusetts",
"Tennessee",
"Indiana",
"Missouri",
"Maryland",
"Wisconsin",
"Colorado",
"Minnesota",
"South Carolina",
"Alabama",
"Louisiana",
"Kentucky",
"Oregon",
"Oklahoma",
"Connecticut",
"Utah",
"Iowa",
"Nevada",
"Arkansas",
"Mississippi",
"Kansas",
"New Mexico",
"Nebraska",
"West Virginia",
"Idaho",
"Hawaii",
"New Hampshire",
"Maine",
"Montana",
"Rhode Island",
"Delaware",
"South Dakota",
"North Dakota",
"Alaska",
"Vermont",
"Wyoming",
"Ontario",
"Quebec",
"British Columbia",
"Alberta",
"Manitoba",
"Saskatchewan",
"Nova Scotia",
"New Brunswick",
"Newfoundland and Labrador",
"Prince Edward Island",
"Northwest Territories",
"Yukon",
"Nunavut",
"England",
"Scotland",
"Wales",
"Northern Ireland",
"Greater London",
"Bavaria",
"Baden-Wurttemberg",
"Berlin",
"Brandenburg",
"Bremen",
"Hamburg",
"Hesse",
"Lower Saxony",
"North Rhine-Westphalia",
"Rhineland-Palatinate",
"Saarland",
"Saxony",
"Saxony-Anhalt",
"Schleswig-Holstein",
"Thuringia",
"Ile-de-France",
"Provence-Alpes-Cote d'Azur",
"Auvergne-Rhone-Alpes",
"Nouvelle-Aquitaine",
"Occitanie",
"Grand Est",
"Hauts-de-France",
"Normandie",
"Bretagne",
"Centre-Val de Loire",
"Bourgogne-Franche-Comte",
"Pays de la Loire",
"Corse",
"New South Wales",
"Victoria",
"Queensland",
"Western Australia",
"South Australia",
"Tasmania",
"Australian Capital Territory",
"Northern Territory",
"Tokyo",
"Osaka",
"Kanagawa",
"Aichi",
"Saitama",
"Chiba",
"Hyogo",
"Hokkaido",
"Fukuoka",
"Kyoto",
"Shizuoka",
"Hiroshima",
"Ibaraki",
"Sao Paulo",
"Rio de Janeiro",
"Minas Gerais",
"Bahia",
"Rio Grande do Sul",
"Parana",
"Pernambuco",
"Ceara",
"Para",
"Santa Catarina",
"Goias",
"Maranhao",
"Paraiba",
"Espirito Santo",
"Mato Grosso",
"Amazonas",
"Maharashtra",
"Uttar Pradesh",
"Tamil Nadu",
"West Bengal",
"Karnataka",
"Gujarat",
"Rajasthan",
"Andhra Pradesh",
"Madhya Pradesh",
"Kerala",
"Delhi",
"Bihar",
"Telangana",
"Odisha",
"Assam",
"Punjab",
"Haryana",
"Beijing",
"Shanghai",
"Guangdong",
"Jiangsu",
"Zhejiang",
"Shandong",
"Henan",
"Sichuan",
"Hubei",
"Hunan",
"Hebei",
"Fujian",
"Anhui",
"Liaoning",
"Shaanxi",
"Jiangxi",
"Heilongjiang",
"Guangxi",
"Yunnan",
"Moscow",
"Saint Petersburg",
"Sverdlovsk Oblast",
"Novosibirsk Oblast",
"Republic of Tatarstan",
"Nizhny Novgorod Oblast",
"Chelyabinsk Oblast",
"Samara Oblast",
"Rostov Oblast",
"Republic of Bashkortostan",
"Krasnoyarsk Krai",
"Lombardy",
"Lazio",
"Campania",
"Sicily",
"Veneto",
"Emilia-Romagna",
"Piedmont",
"Apulia",
"Tuscany",
"Calabria",
"Sardinia",
"Liguria",
"Madrid",
"Catalonia",
"Andalusia",
"Valencia",
"Galicia",
"Castile and Leon",
"Basque Country",
"Castilla-La Mancha",
"Canary Islands",
"Murcia",
]
placeholder_values = {
"Country": lambda: country if country else random.choice(country_names),
"Region": lambda: region if region else random.choice(subdivision_names),
"Nationality": lambda: (
nationality if nationality else random.choice(country_names)
), # Same as Country, the target LLM should be able to understand the context
"GENDER": lambda: gender if gender else random.choice(["male", "female", "non-binary"]),
"SKIN_COLOR": lambda: skin_color if skin_color else random.choice(["white", "black", "brown", "yellow"]),
}
# Replace placeholders in the examples with generated values
filled_examples = []
for example in examples:
prompt = example["prompt"]
for placeholder, generator in placeholder_values.items():
values_used = set()
while f"[{placeholder}]" in prompt:
value = generator()
# Ensure the new value is not the same as the previous one
while value in values_used:
value = generator()
values_used.add(value)
prompt = prompt.replace(f"[{placeholder}]", value, 1)
filled_examples.append(prompt)
# Create SeedPrompt instances from each example in 'filled_examples'
seed_prompts = [
SeedPrompt(
value=example,
data_type="text",
name="SecLists Bias Testing Examples",
dataset_name="SecLists Bias Testing Examples",
harm_categories=["bias_testing"],
description="A dataset of SecLists AI LLM Bias Testing examples with placeholders replaced.",
)
for example in filled_examples
]
seed_dataset = SeedDataset(prompts=seed_prompts)
return seed_dataset
