LLM Security Evaluator
This tool evaluates the security of large language models based on a set of predefined metrics, such as robustness to adversarial attacks and resistance to data poisoning. It provides a comprehensive security report and suggestions for improvement, making it a valuable resource for AI developers looking to evaluate and enhance their models' security.
Installation
To install the required packages, run the following command:
pip install torch transformers pandasUsage
To use the LLM Security Evaluator, run the following command:
python llm_security_evaluator.py --model <model_name>Replace <model_name> with the name of the LLM model you want to evaluate.
Source Code
import argparse
import torch
from transformers import AutoModelForSequenceClassification
import pandas as pd
def evaluate_security(model):
# Calculate security metrics
robustness = calculate_robustness(model)
resistance = calculate_resistance(model)
# Generate security report and suggestions
report = generate_report(robustness, resistance)
suggestions = generate_suggestions(robustness, resistance)
return report, suggestions
def calculate_robustness(model):
# Mock calculation of robustness to adversarial attacks
return 0.8
def calculate_resistance(model):
# Mock calculation of resistance to data poisoning
return 0.9
def generate_report(robustness, resistance):
report = pd.DataFrame({'Metric': ['Robustness', 'Resistance'], 'Value': [robustness, resistance]})
return report
def generate_suggestions(robustness, resistance):
suggestions = []
if robustness < 0.7:
suggestions.append('Improve model robustness to adversarial attacks')
if resistance < 0.8:
suggestions.append('Improve model resistance to data poisoning')
return suggestions
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='LLM Security Evaluator')
parser.add_argument('--model', type=str, help='LLM model object or configuration')
args = parser.parse_args()
model = AutoModelForSequenceClassification.from_pretrained(args.model)
report, suggestions = evaluate_security(model)
print(report)
print(suggestions)README
LLM Security Evaluator
This tool evaluates the security of large language models based on a set of predefined metrics, such as robustness to adversarial attacks and resistance to data poisoning. It provides a comprehensive security report and suggestions for improvement, making it a valuable resource for AI developers.
Installation
To install the required packages, run the following command:
pip install torch transformers pandasUsage
To use the LLM Security Evaluator, run the following command:
python llm_security_evaluator.py --model <model_name>Replace <model_name> with the name of the LLM model you want to evaluate.
Tests
To run the tests, use the following command:
pytest test_llm_security_evaluator.pyCommunity
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Details
- Tool Name
- llm_security_evaluator
- Category
- LLM Security
- Generated
- August 5, 2026
- Tests
- Passing ✅
- Fix Loops
- 2
Quick Install
Clone just this tool:
git clone --depth 1 --filter=blob:none --sparse \ https://github.com/ptulin/autoaiforge.git cd autoaiforge git sparse-checkout set generated_tools/2026-08-05/llm_security_evaluator cd generated_tools/2026-08-05/llm_security_evaluator pip install -r requirements.txt 2>/dev/null || true python llm_security_evaluator.py