diff --git a/Readme.md b/Readme.md index 8602a36..43715e4 100644 --- a/Readme.md +++ b/Readme.md @@ -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 🧙 - booking-screen ## LLM kwargs @@ -305,11 +300,7 @@ Agentic Security is released under the Apache License v2. ## Contact us -## 🤝 Schedule a 1-on-1 Session -Book us with Cal.com - -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 diff --git a/agentic_security/probe_data/__init__.py b/agentic_security/probe_data/__init__.py index 1c0ccc0..700be74 100644 --- a/agentic_security/probe_data/__init__.py +++ b/agentic_security/probe_data/__init__.py @@ -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, diff --git a/agentic_security/probe_data/data.py b/agentic_security/probe_data/data.py index c3ca351..f75d63e 100644 --- a/agentic_security/probe_data/data.py +++ b/agentic_security/probe_data/data.py @@ -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, } diff --git a/pyproject.toml b/pyproject.toml index c21fc80..19a4f58 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -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 "] maintainers = ["Alexander Miasoiedov "]