diff --git a/2_0_vulns/LLM03_TrainingDataPoisoning.md b/2_0_vulns/LLM03_DataModelPoisoning.md
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rename from 2_0_vulns/LLM03_TrainingDataPoisoning.md
rename to 2_0_vulns/LLM03_DataModelPoisoning.md
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+++ b/2_0_vulns/LLM03_DataModelPoisoning.md
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-## LLM03: Training Data Poisoning
+## LLM03: Data and Model Poisoning
 
 ### Description
 
 The starting point of any machine learning approach is training data, simply “raw text”. To be highly capable (e.g., have linguistic and world knowledge), this text should span a broad range of domains, genres and languages. A large language model uses deep neural networks to generate outputs based on patterns learned from training data.
 
-Training data poisoning refers to manipulation of pre-training data or data involved within the fine-tuning or embedding processes to introduce vulnerabilities (which all have unique and sometimes shared attack vectors), backdoors or biases that could compromise the model’s security, effectiveness or ethical behavior. Poisoned information may be surfaced to users or create other risks like performance degradation, downstream software exploitation and reputational damage. Even if users distrust the problematic AI output, the risks remain, including impaired model capabilities and potential harm to brand reputation.
+Data and Model Poisoning refers to manipulation of pre-training data or data involved within the fine-tuning or embedding processes to introduce vulnerabilities (which all have unique and sometimes shared attack vectors), backdoors or biases that could compromise the model’s security, effectiveness or ethical behavior. Poisoned information may be surfaced to users or create other risks like performance degradation, downstream software exploitation and reputational damage. Even if users distrust the problematic AI output, the risks remain, including impaired model capabilities and potential harm to brand reputation.
 
 - Pre-training data refers to the process of training a model based on a task or dataset.
 - Fine-tuning involves taking an existing model that has already been trained and adapting it to a narrower subject or a more focused goal by training it using a curated dataset. This dataset typically includes examples of inputs and corresponding desired outputs.