fix links

Author: PatrickFarley

articles/ai-services/openai/concepts/content-filter-risk-categories.md

@@ -25,8 +25,8 @@ Text and image models support Drugs as an additional classification. This catego
 | Protected Material for Text<sup>1</sup> | Protected material text describes known text content (for example, song lyrics, articles, recipes, and selected web content) that can be outputted by large language models.
 | Protected Material for Code | Protected material code describes source code that matches a set of source code from public repositories, which can be outputted by large language models without proper citation of source repositories.
 |User Prompt Attacks |User prompt attacks are User Prompts designed to provoke the Generative AI model into exhibiting behaviors it was trained to avoid or to break the rules set in the System Message. Such attacks can vary from intricate roleplay to subtle subversion of the safety objective. |
-|Indirect Attacks |Indirect Attacks, also referred to as Indirect Prompt Attacks or Cross-Domain Prompt Injection Attacks, are a potential vulnerability where third parties place malicious instructions inside of documents that the Generative AI system can access and process. Requires [document embedding and formatting](#embedding-documents-in-your-prompt). |
-| Groundedness<sup>2</sup> | Groundedness detection flags whether the text responses of large language models (LLMs) are grounded in the source materials provided by the users. Ungrounded material refers to instances where the LLMs produce information that is non-factual or inaccurate from what was present in the source materials. Requires [document embedding and formatting](#embedding-documents-in-your-prompt). |
+|Indirect Attacks |Indirect Attacks, also referred to as Indirect Prompt Attacks or Cross-Domain Prompt Injection Attacks, are a potential vulnerability where third parties place malicious instructions inside of documents that the Generative AI system can access and process. Requires [document embedding and formatting](./content-filter-document-embedding.md). |
+| Groundedness<sup>2</sup> | Groundedness detection flags whether the text responses of large language models (LLMs) are grounded in the source materials provided by the users. Ungrounded material refers to instances where the LLMs produce information that is non-factual or inaccurate from what was present in the source materials. Requires [document embedding and formatting](./content-filter-document-embedding.md). |
 
 <sup>1</sup> If you're an owner of text material and want to submit text content for protection, [file a request](https://aka.ms/protectedmaterialsform).