diff --git a/docs/faq/security.md b/docs/faq/security.md
index 09fa22b7e379..0615acda3435 100644
--- a/docs/faq/security.md
+++ b/docs/faq/security.md
@@ -12,7 +12,7 @@ In particular the following threat-vectors exist when training using MXNet:
 It is highly recommended that the following best practices be followed when using MXNet:
 
 * Run MXNet with least privilege, i.e. not as root.
-* Run MXNet training jobs inside a secure and isolated environment. If you are using a cloud provider like Amazon AWS, running your training job inside a [private VPC] (https://aws.amazon.com/vpc/) is a good way to accomplish this. Additionally, configure your network security settings so as to only allow connections that the cluster nodes require.
+* Run MXNet training jobs inside a secure and isolated environment. If you are using a cloud provider like Amazon AWS, running your training job inside a [private VPC](https://aws.amazon.com/vpc/) is a good way to accomplish this. Additionally, configure your network security settings so as to only allow connections that the cluster nodes require.
 * Make sure no unauthorized actors have physical or remote access to the nodes participating in MXNet training.
 * During training, one can configure MXNet to periodically save model checkpoints. To protect these model checkpoints from unauthorized access, make sure the checkpoints are written out to an encrypted storage volume, and have a provision to delete checkpoints that are no longer needed.
 * When sharing trained models, or when receiving trained models from other parties, ensure that model artifacts are authenticated and integrity protected using cryptographic signatures, thus ensuring that the data received comes from trusted sources and has not been maliciously (or accidentally) modified in transit.
@@ -21,4 +21,4 @@ It is highly recommended that the following best practices be followed when usin
 # Deployment Considerations
 The following are not MXNet framework specific threats but are applicable to Machine Learning models in general.
 
-* When deploying high-value, proprietary models for inference, care should be taken to prevent an adversary from stealing the model. The research paper [Stealing Machine Learning Models via Prediction APIs] (https://arxiv.org/pdf/1609.02943.pdf) outlines experiments performed to show how an attacker can use a prediction API to leak the ML model or construct a nearly identical replica. A simple way to thwart such an attack is to not expose the prediction probabilities to a high degree of precision in the API response.
+* When deploying high-value, proprietary models for inference, care should be taken to prevent an adversary from stealing the model. The research paper [Stealing Machine Learning Models via Prediction APIs](https://arxiv.org/pdf/1609.02943.pdf) outlines experiments performed to show how an attacker can use a prediction API to leak the ML model or construct a nearly identical replica. A simple way to thwart such an attack is to not expose the prediction probabilities to a high degree of precision in the API response.