From 867ae769e9980c351d451e7fe3760c53fd28e43d Mon Sep 17 00:00:00 2001
From: Matteo Meucci <matteo.meucci@owasp.org>
Date: Sun, 16 Nov 2025 17:11:01 +0100
Subject: [PATCH] Update 2.1.2_Identify_RAI_threats.md

---
 .../content/2.1.2_Identify_RAI_threats.md     | 78 +++++++++----------
 1 file changed, 39 insertions(+), 39 deletions(-)

diff --git a/Document/content/2.1.2_Identify_RAI_threats.md b/Document/content/2.1.2_Identify_RAI_threats.md
index 51305fa..839b3af 100644
--- a/Document/content/2.1.2_Identify_RAI_threats.md
+++ b/Document/content/2.1.2_Identify_RAI_threats.md
@@ -94,62 +94,62 @@ For broader security assurance across networks, infrastructure, and traditional
 
 * Scope: Front-end UX, APIs, agents/plugins, user-AI interactions  
 * Threats:  
-  - * Prompt injection (LLM01)  
-  ** Output handling (LLM05)  
-  * Excessive agency (LLM06)  
-    Misinformation (LLM09)  
+  -- Prompt injection (LLM01)  
+  -- Output handling (LLM05)  
+  -- Excessive agency (LLM06)  
+  -- Misinformation (LLM09)  
   -- Automation bias  
-  - Hallucinations  
-  - Toxic content  
-  - Explainability gaps  
+  -- Hallucinations  
+  -- Toxic content  
+  -- Explainability gaps  
 * Testing Focus:  
-  - Behavior consistency  
-  - Ethical content validation  
-  - User interface abuse (e.g., phishing via AI)  
-  - Interpretability & transparency evaluation
+  -- Behavior consistency  
+  -- Ethical content validation  
+  -- User interface abuse (e.g., phishing via AI)  
+  -- Interpretability & transparency evaluation
 
 ### **2\. AI Model Testing**
 
 * Scope: Model training, fine-tuning, inference behavior  
 * Threats:  
-  - Model & data poisoning (LLM04)  
-  - Inversion/inference attacks  
-  - Bias/discrimination  
-  - Model exfiltration  
-  - Overfitting / generalization issues  
-  - Explainability & fairness gaps  
+  -- Model & data poisoning (LLM04)  
+  -- Inversion/inference attacks  
+  -- Bias/discrimination  
+  -- Model exfiltration  
+  -- Overfitting / generalization issues  
+  -- Explainability & fairness gaps  
 * Testing Focus:  
-  - Adversarial robustness  
-  - Fairness auditing  
-  - Membership inference testing  
-  - Alignment and behavior under edge cases
+  -- Adversarial robustness  
+  -- Fairness auditing  
+  -- Membership inference testing  
+  -- Alignment and behavior under edge cases
 
 ### **3\. AI Infrastructure Testing**
 
 * Scope: Hosting, serving, orchestration, APIs, plugin permissions  
 * Threats:  
-  - System prompt leakage (LLM07)  
-  - Resource abuse (LLM10)  
-  - Supply chain poisoning (LLM03)  
-  - Unauthorized API control  
-  - Insecure agent capabilities  
+  -- System prompt leakage (LLM07)  
+  -- Resource abuse (LLM10)  
+  -- Supply chain poisoning (LLM03)  
+  -- Unauthorized API control  
+  -- Insecure agent capabilities  
 * Testing Focus:  
-  - Least privilege enforcement  
-  - Resource sandboxing  
-  - Plugin/agent boundary testing  
-  - Environment security (CI/CD, containers)
+  -- Least privilege enforcement  
+  -- Resource sandboxing  
+  -- Plugin/agent boundary testing  
+  -- Environment security (CI/CD, containers)
 
 ### **4\. AI Data Testing**
 
 * Scope: Data collection, curation, storage, labeling, filtering  
 * Threats:  
-  - Data poisoning (LLM04)  
-  - Training data leaks  
-  - Toxic/unrepresentative data  
-  - Bias introduced during preprocessing  
-  - Mislabeling or filtering inconsistencies  
+  -- Data poisoning (LLM04)  
+  -- Training data leaks  
+  -- Toxic/unrepresentative data  
+  -- Bias introduced during preprocessing  
+  -- Mislabeling or filtering inconsistencies  
 * Testing Focus:  
-  - Dataset integrity & labeling accuracy  
-  - Bias and diversity analysis  
-  - Data provenance validation  
-  - Filtering robustness (toxicity, duplication)
+  -- Dataset integrity & labeling accuracy  
+  -- Bias and diversity analysis  
+  -- Data provenance validation  
+  -- Filtering robustness (toxicity, duplication)