build(deps): bump h11 from 0.14.0 to 0.16.0

Bumps [h11](https://github.com/python-hyper/h11) from 0.14.0 to 0.16.0.
- [Commits](https://github.com/python-hyper/h11/compare/v0.14.0...v0.16.0)

---
updated-dependencies:
- dependency-name: h11
  dependency-version: 0.16.0
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>

Readme.md

@@ -21,7 +21,9 @@
   <a href="https://pypi.org/project/agentic-security/">
     <img alt="PyPI Version" src="https://img.shields.io/pypi/v/agentic-security?style=for-the-badge&logo=pypi&labelColor=000000&color=00CCFF" />
   </a>
-
+  <a href="https://discord.gg/stw3DfZQ">
+    <img alt="Join Discord" src="https://img.shields.io/badge/Discord-Join%20Us-black?style=for-the-badge&logo=discord&labelColor=000000&color=DD55FF" />
+  </a>
 </p>
 
 

agentic_security/probe_data/data.py

@@ -245,47 +245,61 @@ def load_jailbreak_v28k() -> ProbeDataset:
         return create_probe_dataset("JailbreakV-28K/JailBreakV-28k", [])
 
 
-@cache_to_disk(1)
-def file_dataset(file) -> list[str]:
-    prompts = []
-    try:
-        df = pd.read_csv(os.path.join("./datasets", file), encoding_errors="ignore")
-        if "prompt" in df.columns:
-            prompts = 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 prompts
-
-
+@cache_to_disk()
 def load_local_csv() -> ProbeDataset:
     """Load prompts from local CSV files."""
-    os.makedirs("./datasets", exist_ok=True)
-    csv_files = [f for f in os.listdir("./datasets") if f.endswith(".csv")]
+    csv_files = [f for f in os.listdir(".") if f.endswith(".csv")]
     logger.info(f"Found {len(csv_files)} CSV files: {csv_files}")
 
     prompts = []
     for file in csv_files:
-        prompts.extend(file_dataset(file))
+        try:
+            df = pd.read_csv(file)
+            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("Local CSV", prompts, {"src": str(csv_files)})
 
 
+@cache_to_disk(1)
 def load_csv(file: str) -> ProbeDataset:
     """Load prompts from local CSV files."""
-    prompts = file_dataset(file)
+    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(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("./datasets") if f.endswith(".csv")]
+    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:
-        datasets.append(create_probe_dataset(file, file_dataset(file), {"src": file}))
+        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
 
 
@@ -322,20 +336,12 @@ class StenographyTransformer:
                 )
                 continue
 
-            # 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
-
+            size = len(dataset.prompts)
             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(prompts, min(sample_size, size))
+                sampled = random.sample(dataset.prompts, min(sample_size, size))
                 try:
                     transformed = [
                         f"Follow the instructions in {name}: {fn(str(p))}"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "agentic_security"
-version = "0.7.3"
+version = "0.7.2"
 description = "Agentic LLM vulnerability scanner"
 authors = ["Alexander Miasoiedov <msoedov@gmail.com>"]
 maintainers = ["Alexander Miasoiedov <msoedov@gmail.com>"]