feat(add csv utils):

agentic_security/probe_data/data.py

@@ -245,6 +245,7 @@ def load_jailbreak_v28k() -> ProbeDataset:
         return create_probe_dataset("JailbreakV-28K/JailBreakV-28k", [])
 
 
+@cache_to_disk()
 def load_local_csv() -> ProbeDataset:
     """Load prompts from local CSV files."""
     csv_files = [f for f in os.listdir(".") if f.endswith(".csv")]
@@ -264,6 +265,44 @@ def load_local_csv() -> ProbeDataset:
     return create_probe_dataset("Local CSV", prompts, {"src": str(csv_files)})
 
 
+@cache_to_disk()
+def load_csv(file: str) -> ProbeDataset:
+    """Load prompts from local CSV files."""
+    prompts = []
+    try:
+        df = pd.read_csv(file)
+        prompts = df["prompt"].tolist()
+        if "prompt" in df.columns:
+            prompts.extend(df["prompt"].tolist())
+        else:
+            logger.warning(f"File {file} lacks a suitable prompt column")
+    except Exception as e:
+        logger.error(f"Error reading {file}: {e}")
+    return create_probe_dataset(f"fs://{file}", prompts, {"src": str(file)})
+
+
+@cache_to_disk()
+def load_local_csv_files() -> list[ProbeDataset]:
+    """Load prompts from local CSV files and return a list of ProbeDataset objects."""
+    csv_files = [f for f in os.listdir(".") if f.endswith(".csv")]
+    logger.info(f"Found {len(csv_files)} CSV files: {csv_files}")
+
+    datasets = []
+
+    for file in csv_files:
+        try:
+            df = pd.read_csv(file)
+            if "prompt" in df.columns:
+                prompts = df["prompt"].tolist()
+                datasets.append(create_probe_dataset(file, prompts, {"src": file}))
+            else:
+                logger.warning(f"File {file} lacks a suitable prompt column")
+        except Exception as e:
+            logger.error(f"Error reading {file}: {e}")
+
+    return datasets
+
+
 # Stenography transformer
 class StenographyTransformer:
     """Apply stenography transformations to datasets."""
@@ -435,4 +474,11 @@ def prepare_prompts(
         except Exception as e:
             logger.exception(f"Error loading dynamic {name}: {e}")
 
+    # Load csv datasets and apply transformations
+    for name, opts in zip(dataset_names, options):
+        if not name.endswith(".csv"):
+            continue
+        logger.info(f"Loading csv dataset {name} {opts}")
+        datasets.append(load_csv(name))
+
     return datasets