Update FullModelSpec.json

FullModelSpec.json

@@ -1,5 +1,5 @@
 {
-  "Part1_EUAICodeOfPractice_TransparencyChapter"{
+  "Part1_EUAICodeOfPractice_TransparencyChapter": {
     "GeneralInformation": {
       "LegalNameProvider": {
         "description": "Legal name for the model provider",
@@ -361,10 +361,35 @@
         "explanation": "Benchmarked computation for inference, reported in FLOPs with ≥2 significant figures."
       }
     }
-  }
-  
-  "Part2_AIRedTeaming"{
-    {
-  
+  },
+  "Part2_AIRedTeaming": {
+    "Approach 1": {
+      "description": "e.g. multilingual red teaming with baseline test questions for FSI",
+      "mode": "e.g., manual, automated, hybrid",
+      "language": "one or several - EN, IT, SP, HI, etc.",
+      "attacked_model": "provider and version",
+      "target_industry": "FSI, healthcare, all",
+      "AIsafety_ON": "YES or NO, depending on scenario 1) only usage of base model, 2) base model + AI safety layer (e.g. Azure)",
+      "results": {
+        "number_attacks": "total number of tested attacks",
+        "percentage_success_blocked": "total percentage of blocked attacks",
+        "description_failed_blocked": "plain language description of the type of effective red teaming attacks that won vs model",
+        "attack_examples": {
+          "prompt1": {
+            "input": "example prompt",
+            "type_of_attack": "type attack - injection"
+          },
+          "prompt2": {
+            "input": "example prompt",
+            "type_of_attack": "type attack - role"
+          },
+          "promptN": {
+            "input": "example prompt",
+            "type_of_attack": "type of attack"
+          }
+        },
+        "qualitative_notes": "insights, future risks, pending evaluations, promising future paths"
+      }
     }
-}
+  }
+}