diff --git a/13-securing-ai-applications/README.md b/13-securing-ai-applications/README.md
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--- a/13-securing-ai-applications/README.md
+++ b/13-securing-ai-applications/README.md
@@ -57,7 +57,7 @@ Additionally, the Open Web Application Security Project (OWASP) has created a "[
 
 - **Prompt Injection**: a technique where attackers manipulate a Large Language Model (LLM) through carefully crafted inputs, causing it to behave outside of its intended behavior.
 - **Supply Chain Vulnerabilities**: The components and software that make up the applications used by an LLM, such as Python modules or external datasets, can themselves be compromised leading to unexpected results, introduced biases and even vulmerabilities in the underlying infrastructure.
-- **Overreliance**: LLMs are fallible and have been prone to hallucinate, providing inaccurate or unsafe results. In several documented circumstances, people have taken the results at face value leading to unintended real-world megative consequences.
+- **Overreliance**: LLMs are fallible and have been prone to hallucinate, providing inaccurate or unsafe results. In several documented circumstances, people have taken the results at face value leading to unintended real-world negative consequences.
 
 Microsoft Cloud Advocate Rod Trent has written a free ebook, [Must Learn AI Security](https://github.com/rod-trent/OpenAISecurity/tree/main/Must_Learn/Book_Version?WT.mc_id=academic-105485-koreyst), that dives deeply into these and other emerging AI threats and provides extensive guidance on how to best tackle these scenarios.