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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>Neural network calibration is an essential task in deep learning to ensure consistency
between the confidence of model prediction and the true correctness likelihood. In this
demonstration, we first visualize the idea of neural network calibration on a binary
classifier and show model features that represent its calibration. Second, we introduce
our proposed framework <strong>Neural Clamping</strong>, which employs a simple joint input-output
transformation on a pre-trained classifier. We also provide other calibration approaches
(e.g., temperature scaling) to compare with Neural Clamping.</p>
<h2 id="what-is-jailbreak">What is Calibration?</h2>
<p>Neural Network Calibration seeks to make model prediction align with its true correctness likelihood.
A well-calibrated model should provide accurate predictions and reliable confidence when making inferences. On the
contrary, a poor calibration model would have a wide gap between its accuracy and average confidence level.
This phenomenon could hamper scenarios requiring accurate uncertainty estimation, such as safety-related tasks
(e.g., autonomous driving systems, medical diagnosis, etc.).</p>
<div class="container">
<div id="jailbreak-intro" class="row align-items-center jailbreak-intro-sec">
<img id="jailbreak-intro-img" src="https://hsiung.cc/NCTV/images/conf_acc_demo.gif" />
</div>
</div>
<h3 id="refusal-loss">Calibration Metrics</h3>
<p>Objectively, researchers utilize <strong>Calibration Metrics</strong> to measure the calibration error for a model, for example,
Expected Calibration Error (ECE), Static Calibration Error (SCE), Adaptive Calibration Error (ACE), etc.</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 support</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="legend"><img src="images/demo-legend.png" alt="legend" /></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">0.10731</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">0.10721</span></div>
</div>
</div>
<div class="col-8">
<figure class="figure">
<img id="reliability-diagram" src="images/cifar100/resnet110/none/bin15.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">NCTV is maintained by <a href="https://hsiung.cc">Lei Hsiung</a> and <a href="https://github.com/yungchentang">Yung-Chen Tang</a>.</span>
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