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<h1 class="project-name">Gradient Cuff</h1>
<h2 class="project-tagline">Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by Exploring Refusal Loss Landscapes</h2>
</header>
<main id="content" class="main-content" role="main">
<h2 id="introduction">Introduction</h2>
<p>Large Language Models (LLMs) are becoming a prominent generative AI tool, where the user enters a
query and the LLM generates an answer. To reduce harm and misuse, efforts have been made to align
these LLMs to human values using advanced training techniques such as Reinforcement Learning from
Human Feedback (RLHF). However, recent studies have highlighted the vulnerability of LLMs to adversarial
jailbreak attempts aiming at subverting the embedded safety guardrails. To address this challenge,
we define and investigate the <strong>Refusal Loss</strong> of LLMs and then propose a method called <strong>Gradient Cuff</strong> to
detect jailbreak attempts. In this demonstration, we first introduce the concept of "Jailbreak". Then we present the refusal loss
landscape and propose the Gradient Cuff based on the characteristics of this landscape. Lastly, we compare Gradient Cuff with other jailbreak defense
methods and show the defense performance.
</p>
<h2 id="what-is-jailbreak">What is Jailbreak?</h2>
<p>Jailbreak attacks involve maliciously inserting or replacing tokens in the user instruction or rewriting it to bypass and circumvent
the safety guardrails of aligned LLMs. A notable example is that a jailbroken LLM would be tricked into
generating hate speech targeting certain groups of people, as demonstrated below.</p>
<div class="container">
<div id="jailbreak-intro" class="row align-items-center jailbreak-intro-sec">
<img id="jailbreak-intro-img" src="./jailbreak.png" />
</div>
</div>
<h3 id="refusal-loss">Refusal Loss</h3>
<p>Current transformer-based LLMs will return different responses to the same query due to the randomness of
autoregressive sampling-based generation. With this randomness, it is an
interesting phenomenon that a malicious user query will sometimes be rejected by the target LLM, but
sometimes be able to bypass the safety guardrail. Based on this observation, for a given LLM T_\theta
parameterized with $\theta$, we define the refusal loss function $\phi_\theta(x)$ for a given input user query $x$ as below:
</p>
<div class="container jailbreak-intro-sec">
<div><img id="jailbreak-intro-img" src="images/metrics/intro-metric-example.png" /></div>
</div>
<div id="refusal-loss-formula" class="container">
<div id="refusal-loss-formula-list" class="row align-items-center formula-list">
<a href="#ECE-formula" class="selected">Refusal Loss</a>
<a href="#SCE-formula">Refusal Loss Approximation</a>
<a href="#ACE-formula">Gradient Estimation</a>
<div style="clear: both"></div>
</div>
<div id="refusal-loss-formula-content" class="row align-items-center">
<span id="ECE-formula" class="formula" style="">$$\displaystyle \phi_\theta(x)=1-\mathbb{E}_{y \sim T_\theta(x)} JB(y)$$</span>
<span id="SCE-formula" class="formula" style="display: none;">$$\displaystyle f_\theta(x)=1-\frac{1}{N}\sum_{i=1}^N JB(y_i)$$</span>
<span id="ACE-formula" class="formula" style="display: none;">$$\displaystyle g_\theta(x)=\sum_{i=1}^P \frac{f_\theta(x\oplus \mu u_i)-f_\theta(x)}{\mu} u_i $$</span>
</div>
</div>
<h2 id="proposed-approach-gradient-cuff">Proposed Approach: Gradient Cuff</h2>
<div class="container"><img id="gradient-cuff-header" src="images/header.png" /></div>
<h2 id="demonstration">Demonstration</h2>
<p>In the current research, a reliability diagram is drawn to show the calibration performance of a model. However, since
reliability diagrams often only provide fixed bar graphs statically, further explanation from the chart is limited. In
this demonstration, we show how to make reliability diagrams interactive and insightful to help researchers and
developers gain more insights from the graph. Specifically, we provide three CIFAR-100 classification models
in this demonstration. Multiple Bin numbers are also supported </p>
<p>We hope this tool could also facilitate the development process.</p>
<div id="jailbreak-demo" class="container">
<div class="row align-items-center">
<div class="row" style="margin: 10px 0 0">
<div class="models-list">
<span style="margin-right: 1em;">Models</span>
<span class="radio-group"><input type="radio" id="LLaMA2" class="options" name="models" value="llama2_7b_chat" checked="" /><label for="LLaMA2" class="option-label">LLaMA-2-7B-Chat</label></span>
<span class="radio-group"><input type="radio" id="Vicuna" class="options" name="models" value="vicuna_7b_v1.5" /><label for="Vicuna" class="option-label">Vicuna-7B-V1.5</label></span>
</div>
</div>
</div>
<div class="row align-items-center">
<div class="col-4">
<div id="defense-methods">
<div class="row align-items-center"><input type="radio" id="defense_ppl" class="options" name="defense" value="ppl" /><label for="defense_ppl" class="defense">Perplexity Filter</label></div>
<div class="row align-items-center"><input type="radio" id="defense_smoothllm" class="options" name="defense" value="smoothllm" /><label for="defense_smoothllm" class="defense">SmoothLLM</label></div>
<div class="row align-items-center"><input type="radio" id="defense_erase_check" class="options" name="defense" value="erase_check" /><label for="defense_erase_check" class="defense">Erase-Check</label></div>
<div class="row align-items-center"><input type="radio" id="defense_self_reminder" class="options" name="defense" value="self_reminder" /><label for="defense_self_reminder" class="defense">Self-Reminder</label></div>
<div class="row align-items-center"><input type="radio" id="defense_gradient_cuff" class="options" name="defense" value="gradient_cuff" /><label for="defense_gradient_cuff" class="defense"><span style="font-weight: bold;">Gradient Cuff</span></label></div>
</div>
<div class="row align-items-center">
<div class="attack-success-rate"><span class="jailbreak-metric">Average Malicious Refusal Rate</span><span class="attack-success-rate-value" id="asr-value">0.95875</span></div>
</div>
<div class="row align-items-center">
<div class="benign-refusal-rate"><span class="jailbreak-metric">Benign Refusal Rate</span><span class="benign-refusal-rate-value" id="brr-value">0.05000</span></div>
</div>
</div>
<div class="col-8">
<figure class="figure">
<img id="reliability-diagram" src="demo_results/gradient_cuff_llama2_7b_chat_threshold_100.png" alt="CIFAR-100 Calibrated Reliability Diagram (Full)" />
<div class="slider-container">
<div class="slider-label"><span>Perplexity Threshold</span></div>
<div class="slider-content" id="ppl-slider"><div id="ppl-threshold" class="ui-slider-handle"></div></div>
</div>
<div class="slider-container">
<div class="slider-label"><span>Gradient Threshold</span></div>
<div class="slider-content" id="gradient-norm-slider"><div id="gradient-norm-threshold" class="slider-value ui-slider-handle"></div></div>
</div>
<figcaption class="figure-caption">
</figcaption>
</figure>
</div>
</div>
</div>
<h2 id="citations">Citations</h2>
<p>If you find Neural Clamping helpful and useful for your research, please cite our main paper as follows:</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>@inproceedings{hsiung2023nctv,
title={{NCTV: Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by Exploring Refusal Loss Landscapes}},
author={Lei Hsiung, Yung-Chen Tang and Pin-Yu Chen and Tsung-Yi Ho},
booktitle={Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence},
publisher={Association for the Advancement of Artificial Intelligence},
year={2023},
month={February}
}
@misc{tang2022neural_clamping,
title={{Neural Clamping: Joint Input Perturbation and Temperature Scaling for Neural Network Calibration}},
author={Yung-Chen Tang and Pin-Yu Chen and Tsung-Yi Ho},
year={2022},
eprint={2209.11604},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
</code></pre></div></div>
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<span class="site-footer-owner">Gradient Cuff is maintained by <a href="https://gregxmhu.github.io/">Xiaomeng Hu</a></a>.</span>
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