# Import necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import IsolationForest
# Load your dataset
# Ensure to adjust the path to where your training data is located
data_path = 'path/to/your/training/data.csv'
data = pd.read_csv(data_path)
# Data preprocessing steps
# These might include removing irrelevant columns, encoding categorical variables, etc.
# Example: data = data.drop(['irrelevant_column'], axis=1)
# Splitting the dataset into training and testing set to simulate a realistic scenario
# where the model is validated on unseen data.
X_train, X_test = train_test_split(data, test_size=0.2, random_state=42)
# Using Isolation Forest for anomaly detection in the training set
# This is useful for identifying potential poisoned data points
clf = IsolationForest(random_state=42, contamination='auto')
clf.fit(X_train)
# Predictions on the training set
# -1 indicates an outlier, which in this context could be a poisoned data point
outliers_pred = clf.predict(X_train)
# Filtering out the potential poisoned data points from the training set
X_train_filtered = X_train[outliers_pred == 1]
print(f"Original training set size: {len(X_train)}")
print(f"Filtered training set size: {len(X_train_filtered)}")
print("Potential poisoned data points have been removed.")
# Further steps would include retraining your model on X_train_filtered
# and then validating its performance on X_test.
# Remember, this script is a starting point. Expand it with specific checks
# and balances relevant to your LLM application and data.
This script is a foundational step towards securing LLMs against training data poisoning (LLM03). It can be adapted and expanded to include validations and mitigations for other vulnerabilities, such as LLM01 (Prompt Injection) by adding input sanitization checks, or LLM06 (Sensitive Information Disclosure) by ensuring sensitive data is encrypted or redacted in the dataset.
For a comprehensive security posture, integrate this script within a larger pipeline that includes automated tools like Great Expectations for data quality checks and Scikit-learn for more sophisticated data preprocessing and anomaly detection strategies.
```python
import numpy as np
import pandas as pd
from sklearn.ensemble import IsolationForest
from sklearn.datasets import make_classification
# Simulating a dataset with features relevant to LLM training data
# This example generates a dataset with 2 features and 1,000 instances
X, _ = make_classification(n_samples=1000, n_features=2, n_informative=2,
n_redundant=0, weights=[0.99], flip_y=0, random_state=42)
# Converting the numpy array to a pandas DataFrame for easier manipulation
data = pd.DataFrame(X, columns=['Feature_1', 'Feature_2'])
# Introducing missing values randomly for demonstration
for _ in range(10):
idx = np.random.choice(data.index)
col = np.random.choice(data.columns)
data.at[idx, col] = np.nan
# Handling missing values by simple imputation (mean value)
data.fillna(data.mean(), inplace=True)
# Using Isolation Forest for anomaly detection
# Isolation Forest is effective for identifying outliers in high-dimensional datasets
clf = IsolationForest(random_state=42, contamination=0.01) # Assuming 1% of data is anomalous
clf.fit(data)
# Predicting outliers in the dataset
outliers_pred = clf.predict(data)
# Filtering out the potential poisoned data points
filtered_data = data[outliers_pred == 1]
print(f"Original dataset size: {data.shape[0]}")
print(f"Filtered dataset size: {filtered_data.shape[0]}")
print("Potential poisoned data points have been removed. The dataset is now cleaner and ready for further processing or training.")
This script can be directly copied and executed in a Python environment. It demonstrates a fundamental approach to data validation and preprocessing that is essential for maintaining the integrity of training data for LLMs.
This example focuses on anomaly detection and removal, which is crucial for mitigating the risk associated with training data poisoning (LLM03).
To address the full range of vulnerabilities outlined in the OWASP Top 10 for LLMs, consider integrating additional security measures and validations tailored to each specific threat.