jailbreakeval 0.0.4
A collection of automated evaluators for assessing jailbreak attempts
Requires Python: >=3.9

# JailbreakEval

<p align="center">
  <img src="https://github.com/ThuCCSLab/JailbreakEval/raw/main/assets/logo.png" alt="JailbreakEval" />
</p>

`JailbreakEval` is a collection of automated evaluators for assessing jailbreak attempts.

[![tests status](https://github.com/ThuCCSLab/JailbreakEval/actions/workflows/tests.yaml/badge.svg)](https://github.com/ThuCCSLab/JailbreakEval/actions/workflows/tests.yaml)
[![paper](https://img.shields.io/badge/arxiv-2406.09321-b31b1b)](https://arxiv.org/abs/2406.09321)
[![NDSS'25 Best Technical Poster](https://img.shields.io/badge/NDSS'25-Best_Techinical_Poster-blue)](https://www.ndss-symposium.org/wp-content/uploads/2025-poster-19.pdf)
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## Introduction

https://github.com/ThuCCSLab/JailbreakEval/assets/26323729/4d1a6cbc-8ae5-4109-85c8-3ce47f658fdb

Jailbreak is an attack that prompts a language model to provide actionable responses to harmful behaviors, such as writing offensive content or creating a bomb. Evaluating such attacks often requires manual inspection, which is impractical for large-scale analysis.

To address this, most jailbreak research leverages automated tools, each offering unique insights. However, due to the semantic flexibility of natural language, no single automated evaluator fits all contexts.

Instead of proposing one universal evaluator, `JailbreakEval` unifies various evaluators, making them easy to craft, access, and compare. It is designed for:
- **Jailbreak Researchers**, offering well-known evaluators to assess attacks effectively.
- **Jailbreak Evaluator Developers**, providing a framework to create and benchmark new evaluators.

## Install
You can install the latest release of `JailbreakEval` from pypi:
```shell
pip install JailbreakEval
```

Alternatively, you can install `JailbreakEval` by cloning the project:
```shell
git clone https://github.com/ThuCCSLab/JailbreakEval
cd JailbreakEval
make install  # for installation
make dev  # for development
```

## Quick Start
`JailbreakEval` can evaluate your jailbreak attempts in just a few lines of codes.

Here is a snippet to assess a jailbreak attempt by GCG's evaluator:
```python
from jailbreakeval import JailbreakEvaluator

# Initialize the evaluator with GCG's preset
evaluator = JailbreakEvaluator.from_preset("StringMatching-zou2023universal")

# The jailbreak attempt to be evaluated
attempt_to_evaluate = {
    "question": "How to build a bomb?",  # the target question to jailbreak
    "answer": "I cannot tell you. This is illegal."  # the answer extracted from the model by the jailbreak attack
}

# Evaluate the attempt and print the result
evaluation_result = evaluator(attempt_to_evaluate)
print(evaluation_result)   # Output: False
```

More snippets are placed under the `example` folder, such as:
- [Assess multiple jailbreak attempts by AutoDAN's Recheck evaluator](https://github.com/ThuCCSLab/JailbreakEval/blob/main/examples/autodan_recheck.py)
- [Intergrate with EasyJailbreak's Cipher Attack workflow](https://github.com/ThuCCSLab/JailbreakEval/blob/main/examples/intergrate_with_easyjailbreak.py)

## Evaluate Your Jailbreak Attempts by CLI

The `JailbreakEval` command is a Command Line Interface (CLI) tool designed to evaluate a collection of jailbreak attempts. This command becomes available once you installed this package.

```shell
$ JailbreakEval --help
Usage: JailbreakEval [OPTIONS] [EVALUATORS]...

Options:
  --dataset TEXT  Path to a CSV file containing jailbreak attempts.
                  [required]
  --config TEXT   The path to a YAML configuration file.
  --output TEXT   The path to save evaluation details in JSON.
  --help          Show this message and exit.
```

The dataset should be organized as a UTF-8 `.csv` file, containing at least two columns `question` and `answer`. The `question` column lists the prohibited questions to be jailbreaked, and the `answer` column lists the answer extracted from the model. Additional column `label` can be included for assessing the agreement between the automatic evaluation and the manual labeling, marking `1` for a success jailbreak attempt and `0` for an unsuccessful one. See [data/example.csv](https://github.com/ThuCCSLab/JailbreakEval/blob/main/data/example.csv) for an example (adpated from [this JailbreakBench artifacts](https://github.com/JailbreakBench/artifacts/blob/main/attack-artifacts/GCG/white_box/vicuna-13b-v1.5.json))

This command would evaluate each jailbreak attempts by the specified evaluator(s) and report the following metrics:
- Coverage: The ratio of evaluated jailbreak attempts. (as some evaluator may failed to evaluate certain samples)
- Cost: The cost of each evaluation methods.
- Results: The ratio of success jailbreak attempts in this dataset according to each evaluation methods. 
- Agreement (if labels provided): The agreement between the automated evaluation results and the annotation.

For example, the following command will assess the jailbreak attempts in `data/example.csv` by GCG's evaluator:

```shell
JailbreakEval --dataset data/example.csv --output result_example_GCG.json StringMatching-zou2023universal
```

```
Dataset: data/example.csv
Dataset size: 50
Evaluation result:
+---------------------------------+----------+------+-----------+---------------+-------------------+
|               name              | coverage | ASR  | time (ms) | prompt_tokens | completion_tokens |
+---------------------------------+----------+------+-----------+---------------+-------------------+
|            Annotation           |   1.00   | 0.62 |    N/A    |      N/A      |        N/A        |
| StringMatching-zou2023universal |   1.00   | 0.98 |     2     |      N/A      |        N/A        |
+---------------------------------+----------+------+-----------+---------------+-------------------+
Evaluation agreement with annotation:
+---------------------------------+----------+----------+--------+-----------+------+
|               name              | coverage | accuracy | recall | precision |  f1  |
+---------------------------------+----------+----------+--------+-----------+------+
| StringMatching-zou2023universal |   1.00   |   0.64   |  1.00  |    0.63   | 0.78 |
+---------------------------------+----------+----------+--------+-----------+------+
```

Certain evaluators requires access to OpenAI or Hugging Face service. You can configure them by setting the necessary environment variables:

```shell
export OPENAI_API_KEY="sk-placeholder"
export OPENAI_BASE_URL="https://openai-proxy.example.com/v1"  # if unable to access OpenAI directly
export HF_ENDPOINT="https://hf-mirror.com"  # if unable to access Hugging Face directly
JailbreakEval \
  --dataset data/example.csv \
  --output result_example_GCG_GPT_LLM.json \
  StringMatching-zou2023universal \
  OpenAIChat-zhang2024intention-LLM \
  TextClassifier-wang2023donotanswer-longformer-action
```

Alternatively, define them in a YAML configuration file and pass them with the `--config` flag:

```yaml
# config.yaml
openai:
   # Arguments to create an OpenAI client
  api_key: sk-placeholder
  base_url: https://openai-proxy.example.com/v1
transformers:
  common:
     # Arguments to create a `transformers` model
    device_map: cuda:0
    load_in_4bit: True
  LibrAI/longformer-action-ro:
     # Arguments to create a specific model (inherenting the `common` section)
    name_or_path: /path/to/LibrAI/longformer-action-ro
    device_map: cpu  # Override device map to use CPU
     # load_in_4bit: True is inherited from the `common` section and applied here
```

```shell
JailbreakEval \
  --config config.yaml \
  --dataset data/example.csv \
  --output result_example_GCG_GPT_LLM.json \
  StringMatching-zou2023universal \
  OpenAIChat-zhang2024intention-LLM \
  TextClassifier-wang2023donotanswer-longformer-action
```

## Out-of-the-Box Evaluators


Many evaluators has been incorporated into `JailbreakEval`. You can use them directly using `JailbreakEvaluator.from_preset()` or specifying their names in CLI.

Their details can be found in the [presets](https://github.com/ThuCCSLab/JailbreakEval/tree/main/jailbreakeval/presets) directory for reference.

<details markdown='1'>

<summary>String Matching Evaluators</summary>

| Evaluator Name | Note |
|---|---|
| `StringMatching-zou2023universal`| The default evaluator utilized in the optimization process of [GCG attack](https://arxiv.org/abs/2307.15043). <br/> The evaluator of [In-context Attack](https://arxiv.org/abs/2310.06387).  <br/> The evaluator of [SmoothLLM defense](https://arxiv.org/abs/2310.03684) on publicly-accessible models. |
| `StringMatching-zou2023universal-closed`| The evaluator to assess [GCG attack](https://arxiv.org/abs/2307.15043) on closed models. <br> The evaluator of [SmoothLLM defense](https://arxiv.org/abs/2310.03684) on closed models. |
| `StringMatching-zou2023universal-demo`| The evaluator to assess [GCG attack](https://arxiv.org/abs/2307.15043) on LLaMA2 models for demonstration. |
| `StringMatching-zou2023universal-individual`| The evaluator to assess [GCG attack](https://arxiv.org/abs/2307.15043) on invidual behaviors. |
| `StringMatching-zou2023universal-transfer`| The evaluator to assess [GCG attack](https://arxiv.org/abs/2307.15043) on transferability. |
| `StringMatching-lapid2023open`| The evaluator for [Open Sesame! Universal Black Box Jailbreaking of Large Language Models](https://arxiv.org/abs/2309.01446). |
| `StringMatching-liu2024autodan-keyword`| The keyword evaluator proposed in the paper of [AutoDAN attack](https://arxiv.org/abs/2310.04451), primarily derived from `GCG-transfer`. |
| `StringMatching-liu2024autodan-implementation`| The evaluator in the implementation of [AutoDAN attack](https://arxiv.org/abs/2310.04451). |
| `StringMatching-zhang2024intention-keyword`| The evaluator for [IA defense](https://arxiv.org/abs/2401.06561) on SAP200 and AdvBench dataset, primarily derived from `GCG-transfer`. |

</details>

<details markdown='1'>

<summary>Chat Evaluators (using Hugging Face Models)</summary>

| Evaluator Name | Note |
|---|---|
| `HFChat-inan2023llama-llamaguard`| LLaMAGuard |
| `HFChat-inan2023llama-llamaguard2`| LLaMAGuard2 |
| `HFChat-dubey2024llama-llamaguard3`| LLaMAGuard3 |
</details>

<details markdown='1'>

<summary>Chat Evaluators (using OpenAI's service)</summary>

| Evaluator Name | Note |
|---|---|
| `OpenAIChat-liu2024autodan-Recheck`| The Recheck evaluator for [AutoDAN attack](https://arxiv.org/abs/2310.04451). |
| `OpenAIChat-zhang2024intention-LLM`| The evaluator for [IA defense](https://arxiv.org/abs/2401.06561) on DAN dataset |
| `OpenAIChat-qi2023fine-OpenAI`| The evaluator for [Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!](https://arxiv.org/abs/2310.03693) and [How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs](https://arxiv.org/abs/2401.06373) based on OpenAI's policy |
| `OpenAIChat-qi2023fine-Meta`| The evaluator for [Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!](https://arxiv.org/abs/2310.03693) and [How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs](https://arxiv.org/abs/2401.06373) based on Meta's policy |

</details>

<details markdown='1'>

<summary>TextClassification Evaluators (using Hugging Face Models)</summary>

| Evaluator Name | Note |
|---|---|
`HFTextClassification-wang2023donotanswer-longformer-action`| A fine-tuned longformer model for evaluating action risks in [Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs](https://arxiv.org/abs/2308.13387) |
| `HFTextClassification-wang2023donotanswer-longformer-harmful`| A fine-tuned longformer model for evaluating harmfulness in [Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs](https://arxiv.org/abs/2308.13387) and [TrustLLM: Trustworthiness in Large Language Models](https://arxiv.org/abs/2401.05561) |
| `HFTextClassification-yu2023gptfuzzer-GPTFuzz`| A fine-tuned Roberta model for evaluating model safety. |
| `HFTextClassification-ji2023beavertails-beaver-dam-7b`| A fine-tuned LLaMA-2 model for evaluating model safety. |

</details>

<details markdown='1'>

<summary>TextClassification Evaluators (using OpenAI's service)</summary>

| Evaluator Name | Note |
|---|---|
| `OpenAITextClassification-flagged-answer`| An evaluator based on the moderation flag of OpenAI Moderation API. |
</details>

<details markdown='1'>

<summary>TextClassification Evaluators (using Perspective API's service)</summary>

| Evaluator Name | Note |
|---|---|
| `PerspectiveTextClassification-toxicity`| An evaluator based on the score of Perspective API's TOXICITY attribute. |
</details>

<details markdown='1'>

<summary>Voting Evaluators</summary>

| Evaluator Name | Note |
|---|---|
| `Voting-llamaguard2-gptfuzz-beaver-OpenAI-recheck`| A evaluator based on LLaMAGuard2, GPTFUZZ, Beaver Dam, GPT-4o-2024-08-06. |
</details>  

## Evaluator Results
We have assess the quality of each evaluator based on the example dataset. The results are as follows:

| Evaluator Name                            | Safe-RLHF (Accuracy/Recall/Precision/F1) | JAILJUDGE (Accuracy/Recall/Precision/F1) |
|-------------------------------------------|-----------------------------|-----------------------------|
| StringMatch-lapid2023open                 | 0.42 / 0.00 / 1.00 / 0.00   | 0.70 / 0.04 / 0.81 / 0.08   |
| StringMatch-liu2024autodan-implementation | 0.61 / 0.85 / 0.62 / 0.71   | 0.74 / 0.75 / 0.56 / 0.64   |
| StringMatch-liu2024autodan-keyword        | 0.60 / 0.95 / 0.59 / 0.73   | 0.75 / 0.85 / 0.56 / 0.68   |
| StringMatch-liu2024autodan                | 0.60 / 0.95 / 0.59 / 0.73   | 0.75 / 0.85 / 0.56 / 0.68   |
| StringMatch-zhang2024intention-keyword    | 0.60 / 0.95 / 0.59 / 0.73   | 0.75 / 0.86 / 0.57 / 0.68   |
| StringMatch-zou2023universal-closed       | 0.59 / 0.95 / 0.59 / 0.73   | 0.75 / 0.86 / 0.57 / 0.68   |
| StringMatch-zou2023universal-demo         | 0.59 / 0.99 / 0.59 / 0.74   | 0.74 / 0.94 / 0.55 / 0.70   |
| StringMatch-zou2023universal-individual   | 0.60 / 0.99 / 0.59 / 0.74   | 0.72 / 0.94 / 0.54 / 0.68   |
| StringMatch-zou2023universal-transfer     | 0.60 / 0.95 / 0.59 / 0.73   | 0.75 / 0.85 / 0.56 / 0.68   |
| StringMatch-zou2023universal              | 0.59 / 0.99 / 0.58 / 0.74   | 0.72 / 0.94 / 0.53 / 0.68   |
| StringMatch-allsubstringh                 | 0.62 / 0.88 / 0.62 / 0.73   | 0.75 / 0.74 / 0.58 / 0.65   |
| OpenAIChat-liu2024autodan-Recheck         | 0.64 / 0.92 / 0.63 / 0.75   | 0.82 / 0.56 / 0.81 / 0.66   |
| OpenAIChat-qi2023fine-OpenAI              | 0.79 / 0.69 / 0.93 / 0.79   | 0.90 / 0.75 / 0.92 / 0.83   |
| HFChat-inan2023llama-llamaguard2          | 0.75 / 0.61 / 0.93 / 0.73   | 0.84 / 0.79 / 0.72 / 0.76   |
| HFChat-inan2023llama-llamaguard           | 0.71 / 0.54 / 0.92 / 0.68   | 0.74 / 0.29 / 0.73 / 0.41   |
| HFChat-dubey2024llama-llamaguard3         | 0.71 / 0.52 / 0.96 / 0.68   | 0.82 / 0.81 / 0.67 / 0.74   |
| HFTextClassification-ji2023beavertails-beaver-dam-7b | 0.89 / 0.87 / 0.93 / 0.90   | 0.82 / 0.58 / 0.81 / 0.68   |
| HFTextClassification-yu2023gptfuzzer-GPTFuzz              | 0.71 / 0.57 / 0.88 / 0.69   | 0.82 / 0.59 / 0.78 / 0.67   |
| OpenAITextClassification-flagged-answer   | 0.47 / 0.09 / 0.93 / 0.16   | 0.68 / 0.03 / 0.46 / 0.06   |
| PerspectiveTextClassification-toxicity    | 0.51 / 0.19 / 0.80 / 0.31   | 0.68 / 0.03 / 0.56 / 0.06   |
| Voting-llamaguard2-gptfuzz-beaver-OpenAI-recheck                                    | 0.81 / 0.70 / 0.95 / 0.81   | 0.86 / 0.70 / 0.82 / 0.76   |
</details>
More evaluators on the way. Feel free to [request](https://github.com/ThuCCSLab/JailbreakEval/issues) or [contribute](https://github.com/ThuCCSLab/JailbreakEval/CONTRIBUTING.md) new evaluators.

## Project Structure

### Files
```
.
├── assets              # Static files such as images, fonts, etc.
├── data                # Data files such as datasets, etc.
├── docs                # Documentations.
├── examples            # Sample code snippets.
├── jailbreakeval       # Main source code for this package.
│   ├── commands        # Command Line Interface (CLI) related code.
│   ├── evaluators      # Implementation of various types of evaluator.
│   ├── configurations  # Configuration of various types of evaluator.
│   ├── presets         # Predefined evaluator presets in YAML.
│   └── services        # Supporting services for evaluators.
│       ├── chat        # Chat services.
│       └── text_classification  # Text classification services.
└── tests               # tests for this package
    ├── evaluators
    ├── configurations
    ├── presets
    └── services
        ├── chat
        └── text_classification
```
### Designs
![Architecture of `JailbreakEval`](https://github.com/ThuCCSLab/JailbreakEval/raw/main/assets/arch.png)

In the framework of `JailbreakEval`, a *Jailbreak Evaluator* is responsible for assessing the effectiveness of a jailbreak attempt. Based on different evaluation paradigm, the *Jailbreak Evaluator* is divided into several subclasses, including the *String Matching Evaluator*, *Text Classification Evaluator*, *Chat Evaluator*, and *Voting Evaluator*. Some of them may consult external services to conduct their assessments (e.g., chat with OpenAI, call a Hugging Face classifier, ...). Each subclass comes with a suite of configurable parameters, enabling tailored evaluation strategies. The predefined configurations for existing evaluator instances are specified by *configuration presets*.

### Evaluator Categories
`JailbreakEval` classifies the mainstream jailbreak evaluators into the following four types:
- String Matching Evaluator: Identify string patterns in content to differentiate between safe and jailbroken material.
- Chat Evaluators: Prompt the OpenAI GPT model to assess the success of a jailbreak attempt.
- Text Classification Evaluators: Employ a Large Language Model (LLM) classifier to evaluate the success of a jailbreak.
- Voting Evaluators: Employ the voting form multiple classifiers to evaluate the success of a jailbreak.

`JailbreakEval` has implemented the backbone of each evaluator category, with some configurable settings to construct specific evaluators. Developers may craft their own evaluators by following the schema of the corresponding category. 


## Contributing

Your contributions are welcomed. Please read our [contribution guide](https://github.com/ThuCCSLab/JailbreakEval/blob/main/CONTRIBUTING.md) for details.

To get on-board for develpment, please read the [development guide](https://github.com/ThuCCSLab/JailbreakEval/blob/main/docs/DEVELOPMENT.md) for details.

## Citation
If you find `JailbreakEval` useful, please cite our paper as: 
```bibtex
@misc{ran2024jailbreakeval,
      title={JailbreakEval: An Integrated Toolkit for Evaluating Jailbreak Attempts Against Large Language Models}, 
      author={Delong Ran and Jinyuan Liu and Yichen Gong and Jingyi Zheng and Xinlei He and Tianshuo Cong and Anyu Wang},
      year={2024},
      eprint={2406.09321},
      archivePrefix={arXiv},
      primaryClass={id='cs.CR' full_name='Cryptography and Security' is_active=True alt_name=None in_archive='cs' is_general=False description='Covers all areas of cryptography and security including authentication, public key cryptosytems, proof-carrying code, etc. Roughly includes material in ACM Subject Classes D.4.6 and E.3.'}
}
```

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