Fixes to address linting errors

Author: RRudder

submissions/description/ai_application_security/insufficient_rate_limiting/query_flooding_api_token_abuse/template.md

@@ -1,15 +1,18 @@
 Query flooding or API token abuse occurs when an attacker uses automated tools or scripts to send a large number of requests to the API of an AI application. A lack of rate limiting can overwhelm the server resources, allowing an attacker to degrade performance, or perform a Denial of Service (DoS) for legitimate users.
 
 **Business Impact**
+
 Service disruption, increased server costs, and potential for unauthorized access or data breaches. Legitimate users may be unable to access the application, impacting business operations.
 
 **Steps to Reproduce**
+
 1. Navigate to the following URL and observe the valid API token:
 1. Use the following script to send a high volume of requests to the API using the token:
 
 ```python
   {script}
 ```
+
 1. Observe that the application's performance and availability are impacted under the higher load of requests
 
 **Proof of Concept (PoC)**

submissions/description/ai_application_security/insufficient_rate_limiting/template.md

@@ -1,15 +1,18 @@
 Insufficient rate limiting occurs when an attacker uses automated tools or scripts to send a large number of requests to the AI application. An attacker can overwhelm the server resources and degrade the performance of the application, or cause a Denial of Service (DoS) for legitimate users.
 
 **Business Impact**
+
 Service disruption, increased server costs, and potential for unauthorized access or data breaches. Legitimate users may be unable to access the application, impacting business operations.
 
 **Steps to Reproduce**
+
 1. Navigate to the following URL and observe the valid token:
 1. Use the following script to send a high volume of requests to the application using the token:
 
 ```python
   {script}
 ```
+
 1. Observe that the application's performance and availability are impacted under the higher load of requests
 
 **Proof of Concept (PoC)**

submissions/description/ai_application_security/prompt_injection/system_prompt_leakage/template.md

@@ -13,7 +13,7 @@ This vulnerability can lead to reputational and financial damage of the company
 ```
 
 1. Look through the model's responses for information that discloses its internal instructions or constraints
-3. Observe that the information shows the model's operating parameters
+1. Observe that the information shows the model's operating parameters
 
 **Proof of Concept (PoC)**
 

submissions/description/ai_application_security/prompt_injection/template.md

@@ -19,4 +19,4 @@ This vulnerability can lead to reputational and financial damage of the company
 
 The screenshot(s) below demonstrate(s) the vulnerability:
 >
-> {{screenshot}}
+> {{screenshot}}

submissions/description/ai_application_security/sensitive_information_disclosure/cross_tenant_pii_leakage_exposure/template.md

@@ -12,6 +12,7 @@ This vulnerability can lead to reputational and financial damage of the company
 ```HTTP
   {HTTP request}
 ```
+
 1. Observe that PII from the Tenant B is disclosed
 
 **Proof of Concept (PoC)**

submissions/description/ai_application_security/sensitive_information_disclosure/sandboxed_container_code_execution/template.md

@@ -9,9 +9,10 @@ This can lead to data breaches, data manipulation, service disruption, and furth
 1. Identify a vulnerability in the sandboxed environment or its configuration
 1. Execute the following exploit designed to break out of the sandbox:
 
-```python
+``` python
   {malicious script}
 ```
+
 1. Verify and observe that arbitrary code can be executed on the host system outside the sandbox
 
 **Proof of Concept (PoC)**

submissions/description/ai_application_security/sensitive_information_disclosure/template.md

@@ -9,9 +9,10 @@ This vulnerability can lead to reputational and financial damage of the company
 1. Log in to the AI system with credentials for Tenant A
 1. Send the following request targeting the PII data or resources:
 
-```HTTP
+``` HTTP
   {HTTP request}
 ```
+
 1. Observe that PII is disclosed
 
 **Proof of Concept (PoC)**

submissions/description/ai_application_security/vector_and_embedding_weaknesses/template.md

@@ -12,7 +12,8 @@ Loss of intellectual property and competitive advantage if sensitive model infor
 ```python
   {script}
 ```
-3. Analyze the extracted embeddings for patterns and observer information about the model's knowledge
+
+1. Analyze the extracted embeddings for patterns and observer information about the model's knowledge
 
 **Proof of Concept (PoC)**