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