diff --git a/content/ai_exchange/content/docs/3_development_time_threats.md b/content/ai_exchange/content/docs/3_development_time_threats.md
index 6a85556..0503552 100644
--- a/content/ai_exchange/content/docs/3_development_time_threats.md
+++ b/content/ai_exchange/content/docs/3_development_time_threats.md
@@ -281,7 +281,7 @@ The attacker manipulates (training) data to affect the algorithm's behavior. Als
 
 Example 1: an attacker breaks into a training set database to add images of houses and labels them as 'fighter plane', to mislead the camera system of an autonomous missile. The missile is then manipulated to attack houses. With a good test set this unwanted behaviour may be detected. However, the attacker can make the poisoned data represent input that normally doesn't occur and therefore would not be in a testset. The attacker can then create that abnormal input in practice. In the previous exmaple this could be houses with white crosses on the door.  See [MITRE ATLAS - Poison traing data](https://atlas.mitre.org/techniques/AML.T0020)
 Example 2: a malicious supplier poisons data that is later obtained by another party to train a model. See [MITRE ATLAS - Publish poisoned datasets](https://atlas.mitre.org/techniques/AML.T0019)
-Example 3: unwanted information (e.g. false facts) in documents on the internet causes a Large Language Model (GenAI) to output unwanted results ([OWASP for LLM 03](https://llmtop10.com/llm03/)). That unwanted information can be planted by an attacker, but of course also by accident. The latter case is a real GenAI risk, but technically comes down to the issue of having false data in a training set which falls outside of the security scope. Planted unwanted information in GenAI training data falls under the category of Sabotage attack as the intention is to make the model behave in unwanted ways for regular input.
+Example 3: unwanted information (e.g. false facts) in documents on the internet causes a Large Language Model (GenAI) to output unwanted results ([OWASP for LLM 03](https://genai.owasp.org/llmrisk/llm03/)). That unwanted information can be planted by an attacker, but of course also by accident. The latter case is a real GenAI risk, but technically comes down to the issue of having false data in a training set which falls outside of the security scope. Planted unwanted information in GenAI training data falls under the category of Sabotage attack as the intention is to make the model behave in unwanted ways for regular input.
 
 **Controls for data poisoning:**
 
@@ -392,7 +392,7 @@ Data manipulation is referred to as data poisoning and is covered in separate th
 
 An attacker supplies a manipulated pre-trained model which is then obtained and unknowingly further used and/or trained/fine tuned, with still having the unwanted behaviour.
 
-AI models are sometimes obtained elsewhere (e.g. open source) and then further trained or fine-tuned. These models may have been manipulated(poisoned) at the source, or in transit. See [OWASP for LLM 05: Supply Chain Vulnerabilities.](https://llmtop10.com/llm05/).
+AI models are sometimes obtained elsewhere (e.g. open source) and then further trained or fine-tuned. These models may have been manipulated(poisoned) at the source, or in transit. See [OWASP for LLM 05: Supply Chain Vulnerabilities.](https://genai.owasp.org/llmrisk/llm05/).
 
 The type of manipulation can be through data poisoning, or by specifically changing the model parameters. Therefore, the same controls apply that help against those attacks. Since changing the model parameters requires protection of the parameters at the moment they are manipulated, this is not in the hands of the one who obtained the model. What remains are the controls against data poisoning, the controls against model poisoning in general (e.g. model ensembles), plus of course good supply chain management, and some specific controls that help against transfer learning attacks.