Merge pull request #224 from Mundi-Xu/datasets-optimize

refactor: standardize CSV loading from ./datasets and improve robustness

agentic_security/probe_data/data.py

@@ -248,13 +248,13 @@ def load_jailbreak_v28k() -> ProbeDataset:
 @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")]
+    csv_files = [f for f in os.listdir("./datasets") if f.endswith(".csv")]
     logger.info(f"Found {len(csv_files)} CSV files: {csv_files}")
 
     prompts = []
     for file in csv_files:
         try:
-            df = pd.read_csv(file)
+            df = pd.read_csv(os.path.join("./datasets", file), encoding_errors="ignore")
             if "prompt" in df.columns:
                 prompts.extend(df["prompt"].tolist())
             else:
@@ -270,7 +270,7 @@ def load_csv(file: str) -> ProbeDataset:
     """Load prompts from local CSV files."""
     prompts = []
     try:
-        df = pd.read_csv(file)
+        df = pd.read_csv(os.path.join("./datasets", file), encoding_errors="ignore")
         prompts = df["prompt"].tolist()
         if "prompt" in df.columns:
             prompts.extend(df["prompt"].tolist())
@@ -284,14 +284,14 @@ def load_csv(file: str) -> ProbeDataset:
 @cache_to_disk(1)
 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")]
+    csv_files = [f for f in os.listdir("./datasets") 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)
+            df = pd.read_csv(os.path.join("./datasets", file), encoding_errors="ignore")
             if "prompt" in df.columns:
                 prompts = df["prompt"].tolist()
                 datasets.append(create_probe_dataset(file, prompts, {"src": file}))
@@ -336,12 +336,18 @@ class StenographyTransformer:
                 )
                 continue
 
-            size = len(dataset.prompts)
+            # Convert async generator (if any) to a list
+            prompts = list(dataset.prompts) if hasattr(dataset.prompts, '__iter__') else []
+            size = len(prompts)
+
+            if size == 0:
+                continue
+
             sample_size = max(1, int(size * 0.25))  # At least 1 sample
 
             for name, fn in self.TRANSFORMATIONS.items():
                 logger.info(f"Applying {name} to {dataset.dataset_name}")
-                sampled = random.sample(dataset.prompts, min(sample_size, size))
+                sampled = random.sample(prompts, min(sample_size, size))
                 try:
                     transformed = [
                         f"Follow the instructions in {name}: {fn(str(p))}"