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feat(Add new datasets; v0.1.6):

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Alexander Myasoedov
2024-07-12 14:52:43 +03:00
parent eaaa199d29
commit 9ca2ceeec8
4 changed files with 111 additions and 10 deletions
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Readme.md
-9
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@@ -26,7 +26,6 @@
- LLM API integration and stress testing 🛠️
- Wide range of fuzzing and attack techniques 🌀
| Tool | Source | Integrated |
|-------------------------|-------------------------------------------------------------------------------|------------|
| Garak | [leondz/garak](https://github.com/leondz/garak) | ✅ |
@@ -35,11 +34,8 @@
| Custom Huggingface Datasets | markush1/LLM-Jailbreak-Classifier | ✅ |
| Local CSV Datasets | - | ✅ |
Note: Please be aware that Agentic Security is designed as a safety scanner tool and not a foolproof solution. It cannot guarantee complete protection against all possible threats.
## 📦 Installation
To get started with Agentic Security, simply install the package using pip:
@@ -73,7 +69,6 @@ agentic_security --port=PORT --host=HOST
## UI 🧙
<img width="100%" alt="booking-screen" src="https://res.cloudinary.com/do9qa2bqr/image/upload/v1713002396/1-ezgif.com-video-to-gif-converter_s2hsro.gif">
## LLM kwargs
@@ -305,11 +300,7 @@ Agentic Security is released under the Apache License v2.
## Contact us
## 🤝 Schedule a 1-on-1 Session
<a href="https://cal.com/alexander-myasoedov-go2tfs/30min"><img src="https://cal.com/book-with-cal-dark.svg" alt="Book us with Cal.com"></a>
Book a 1-on-1 Session with the founders, to discuss any issues, provide feedback, or explore how we can improve agentic_security for you.
## Repo Activity
agentic_security/probe_data/__init__.py
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@@ -61,6 +61,26 @@ REGISTRY = [
"dynamic": False,
"url": "https://huggingface.co/markush1/LLM-Jailbreak-Classifier",
},
{
"dataset_name": "JailbreakV-28K/JailBreakV-28k",
"num_prompts": 28300,
"tokens": 1975800,
"approx_cost": 0.0,
"source": "Hugging Face Datasets",
"selected": True,
"dynamic": False,
"url": "https://huggingface.co/JailbreakV-28K/JailBreakV-28k",
},
{
"dataset_name": "ShawnMenz/jailbreak_sft_rm_ds",
"num_prompts": 371000,
"tokens": 1975800,
"approx_cost": 0.0,
"source": "Hugging Face Datasets",
"selected": True,
"dynamic": False,
"url": "https://huggingface.co/ShawnMenz/jailbreak_sft_rm_ds",
},
{
"dataset_name": "Steganography",
"num_prompts": 10,
@@ -91,6 +111,26 @@ REGISTRY = [
"dynamic": True,
"url": "",
},
{
"dataset_name": "jailbreak_llms/2023_05_07",
"num_prompts": 0,
"tokens": 0,
"approx_cost": 0.0,
"source": "Github",
"selected": True,
"dynamic": True,
"url": "https://github.com/verazuo/jailbreak_llms",
},
{
"dataset_name": "jailbreak_llms/2023_12_25.csv",
"num_prompts": 0,
"tokens": 0,
"approx_cost": 0.0,
"source": "Github",
"selected": True,
"dynamic": True,
"url": "https://github.com/verazuo/jailbreak_llms",
},
{
"dataset_name": "Malwaregen",
"num_prompts": 0,
agentic_security/probe_data/data.py
+70
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@@ -1,8 +1,10 @@
import io
import os
import random
from dataclasses import dataclass
from functools import lru_cache
import httpx
import pandas as pd
from loguru import logger
@@ -148,6 +150,44 @@ def load_dataset_v6():
)
@cache_to_disk()
def load_dataset_v7():
splits = {
"mini_JailBreakV_28K": "JailBreakV_28K/mini_JailBreakV_28K.csv",
"JailBreakV_28K": "JailBreakV_28K/JailBreakV_28K.csv",
}
df = pd.read_csv(
"hf://datasets/JailbreakV-28K/JailBreakV-28k/" + splits["JailBreakV_28K"]
)
bad_prompts = df["jailbreak_query"].tolist()
print(df.shape)
return ProbeDataset(
dataset_name="JailbreakV-28K/JailBreakV-28k",
metadata={},
prompts=bad_prompts,
tokens=count_words_in_list(bad_prompts),
approx_cost=0.0,
)
@cache_to_disk()
def load_dataset_v8():
df = pd.read_csv(
"hf://datasets/ShawnMenz/jailbreak_sft_rm_ds/jailbreak_sft_rm_ds.csv",
names=["jailbreak", "prompt"],
)
filtered = df[df["jailbreak"] == "jailbreak"]["prompt"].tolist()
return ProbeDataset(
dataset_name="JailbreakV-28K/JailBreakV-28k",
metadata={},
prompts=filtered,
tokens=count_words_in_list(filtered),
approx_cost=0.0,
)
@cache_to_disk()
def load_dataset_v5():
from datasets import load_dataset
@@ -173,6 +213,22 @@ def load_dataset_v5():
)
@cache_to_disk()
def load_generic_csv(url, name, column="prompt", predicator=None):
r = httpx.get(url)
content = r.content
df = pd.read_csv(io.StringIO(content.decode("utf-8")))
logger.info(f"Loaded {len(df)} prompts from {url}")
filtered_prompts = df[df.apply(predicator, axis=1)][column].tolist()
return ProbeDataset(
dataset_name=name,
metadata={},
prompts=filtered_prompts,
tokens=count_words_in_list(filtered_prompts),
approx_cost=0.0,
)
def prepare_prompts(dataset_names, budget, tools_inbox=None):
# ## Datasets used and cleaned:
# markush1/LLM-Jailbreak-Classifier
@@ -188,6 +244,20 @@ def prepare_prompts(dataset_names, budget, tools_inbox=None):
"rubend18/ChatGPT-Jailbreak-Prompts": load_dataset_v3,
"Lemhf14/EasyJailbreak_Datasets": load_dataset_v5,
"markush1/LLM-Jailbreak-Classifier": load_dataset_v6,
"JailbreakV-28K/JailBreakV-28k": load_dataset_v7,
"ShawnMenz/jailbreak_sft_rm_ds": load_dataset_v8,
"verazuo/jailbreak_llms/2023_05_07": lambda: load_generic_csv(
url="https://raw.githubusercontent.com/verazuo/jailbreak_llms/main/data/prompts/jailbreak_prompts_2023_05_07.csv",
name="verazuo/jailbreak_llms/2023_05_07",
column="prompt",
predicator=lambda x: bool(x["jailbreak"]),
),
"verazuo/jailbreak_llms/2023_12_25.csv": lambda: load_generic_csv(
url="https://raw.githubusercontent.com/verazuo/jailbreak_llms/main/data/prompts/jailbreak_prompts_2023_12_25.csv.csv",
name="verazuo/jailbreak_llms/2023_12_25.csv",
column="prompt",
predicator=lambda x: bool(x["jailbreak"]),
),
"Custom CSV": load_local_csv,
}
pyproject.toml
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@@ -1,6 +1,6 @@
[tool.poetry]
name = "agentic_security"
version = "0.1.5"
version = "0.1.6"
description = "Agentic LLM vulnerability scanner"
authors = ["Alexander Miasoiedov <msoedov@gmail.com>"]
maintainers = ["Alexander Miasoiedov <msoedov@gmail.com>"]