Update 2.1.2_Identify_RAI_threats.md

Document/content/2.1.2_Identify_RAI_threats.md

@@ -90,66 +90,95 @@ The threat scenarios presented in this guide highlight AI-specific risks and cor
 
 For broader security assurance across networks, infrastructure, and traditional applications, we recommend using the following foundational testing references, which remain essential for comprehensive system-level evaluations such as Network Security \[17\] *NIST SP 800-115: Technical Guide to Information Security Testing and Assessment*. \[18\] *OSSTMM – Open Source Security Testing Methodology Manual* and \[19\] *OWASP Web Security Testing Guide (WSTG)*.
 
-### **1\. AI Application Testing**
+### **1. AI Application Testing**
 
-**Scope:** Front-end UX, APIs, agents/plugins, user-AI interactions  
-**Threats:**  
-* Prompt injection (LLM01)  
-* Output handling (LLM05)  
-  -- Excessive agency (LLM06)  
-  -- Misinformation (LLM09)  
-  -- Automation bias  
-  -- Hallucinations  
-  -- Toxic content  
-  -- Explainability gaps  
-* Testing Focus:  
-  -- Behavior consistency  
-  -- Ethical content validation  
-  -- User interface abuse (e.g., phishing via AI)  
-  -- Interpretability & transparency evaluation
+**Scope**
+Covers all components exposed directly to users or external environments:
+- Front-end UX  
+- APIs  
+- Agents / Plugins  
+- User–AI interactions  
 
-### **2\. AI Model Testing**
+**Key Threats**
+- Prompt Injection (LLM01)
+- Improper Output Handling (LLM05) 
+- Excessive Agency (LLM06)  
+- Misinformation (LLM09)  
+- Automation Bias  
+- Hallucinations  
+- Toxic or Harmful Content  
+- Explainability Gaps  
 
-* 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  
-* Testing Focus:  
-  -- Adversarial robustness  
-  -- Fairness auditing  
-  -- Membership inference testing  
-  -- Alignment and behavior under edge cases
+**Testing Focus**
+- Consistent model behavior across sessions  
+- Ethical and safety-oriented content validation  
+- UI-driven abuse (e.g., AI-assisted phishing vectors)  
+- Interpretability & transparency evaluation  
 
-### **3\. AI Infrastructure Testing**
+### **2. AI Model 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  
-* Testing Focus:  
-  -- Least privilege enforcement  
-  -- Resource sandboxing  
-  -- Plugin/agent boundary testing  
-  -- Environment security (CI/CD, containers)
+**Scope**
+Addresses the internal behavior and lifecycle of AI models:
+- Model training  
+- Fine-tuning  
+- Inference-time decision making  
+
+**Key Threats**
+- **Model & Data Poisoning (LLM04)**
+- Inversion & Membership Inference Attacks  
+- Bias, Discrimination & Fairness Issues  
+- Model Exfiltration (API or runtime)  
+- Overfitting / Generalization Weaknesses  
+- Explainability & Fairness Gaps  
+
+**Testing Focus**
+- Adversarial Robustness Evaluation  
+- Fairness & Bias Auditing  
+- Membership Inference & Privacy Testing  
+- Alignment Testing (behavior in edge or adversarial scenarios)  
+
+### **3. AI Infrastructure Testing**
+
+**Scope**
+Focuses on the security of the systems hosting, serving, and orchestrating AI:
+- Model hosting & serving infrastructure  
+- APIs and service gateways  
+- Plugin / agent permissions & execution environment  
+- Orchestration and deployment pipelines  
+
+**Key Threats**
+- System Prompt Leakage (LLM07)
+- Resource Abuse / Unbounded Consumption (LLM10) 
+- Supply Chain Poisoning (LLM03)  
+- Unauthorized API Control  
+- Insecure Agent or Plugin Capabilities  
+
+**Testing Focus**
+- Least Privilege & Permission Boundary Enforcement  
+- Resource Sandboxing & Rate Limiting  
+- Plugin / Agent Isolation & Boundary Testing  
+- Environment Security (CI/CD, containers, secrets management)  
+
+### **4. AI Data Testing**
+
+**Scope**
+Covers the full lifecycle of data used to train, fine-tune, and evaluate models:
+- Data collection & ingestion  
+- Dataset curation  
+- Storage & filtering  
+- Labeling & preprocessing  
+
+**Key Threats**
+- **Data Poisoning (LLM04)**
+- Training Data Leakage  
+- Toxic, Biased, or Unrepresentative Data  
+- Bias Introduction During Preprocessing  
+- Mislabeling or Inconsistent Filtering  
+
+**Testing Focus**
+- Dataset Integrity & Label Quality  
+- Bias and Diversity Analysis  
+- Data Provenance & Source Validation  
+- Filtering Robustness (toxicity detection, duplication control)  
 
-### **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  
-* Testing Focus:  
-  -- Dataset integrity & labeling accuracy  
-  -- Bias and diversity analysis  
-  -- Data provenance validation  
-  -- Filtering robustness (toxicity, duplication)