Contextual clarity ensures that fine-tuning, inference, and optimization processes explicitly contribute to the security of Large Language Models (LLMs). This approach goes beyond their technical definitions, embedding security considerations into each stage of the workflow.
By focusing on contextual clarity, LLM workflows can be fortified against vulnerabilities such as adversarial attacks, prompt manipulation, and data leakage.
Command:
# Example: Embedding Security in Fine-Tuning
echo "Add defensive training data to counter adversarial prompts during fine-tuning." | python3 test_contextual_fine_tuning.py
# Example: Evaluating Inference Robustness
echo "Analyze model responses to ensure clarity and resistance to prompt injection." | python3 test_contextual_inference.py
# Example: Ensuring Secure Optimization
echo "Quantize the model while maintaining security against adversarial inputs." | python3 test_contextual_optimization.py