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<h1 class="project-name">Token Highlighter</h1>
<h2 class="project-tagline">Inspecting and Mitigating Jailbreak Prompts for Large Language Models</h2>
<h2 class="project-tagline"><a href="https://arxiv.org/abs/2412.18171" style="color: white;" target="_blank" rel="noopener noreferrer">https://arxiv.org/abs/2412.18171</a></h2>
<h2 class="project-tagline"><a href="https://huggingface.co/spaces/gregH/token_highlighter" style="color: white;" target="_blank" rel="noopener noreferrer">Live Demo</a></h2>
<div style="text-align: center">
<div>
<a href="https://gregxmhu.github.io/" style="color: white;" target="_blank" rel="noopener noreferrer">Xiaomeng Hu, CUHK CSE</a>
</div>
<div>
<a href="https://sites.google.com/site/pinyuchenpage/home" style="color: white;" target="_blank" rel="noopener noreferrer">Pin-Yu Chen, IBM Research</a>
</div>
<div>
<a href="https://www.cse.cuhk.edu.hk/people/faculty/tsung-yi-ho/" style="color: white;" target="_blank" rel="noopener noreferrer">Tsung-Yi Ho, CUHK CSE</a>
</div>
</div>
</header>
<main id="content" class="main-content" role="main">
<h2 id="introduction">Introduction</h2>
<p>Large Language Models (LLMs) are increasingly being integrated into services such as ChatGPT to provide responses to user queries.
To mitigate potential harm and prevent misuse, there have been concerted efforts to align the LLMs with human values and legal compliance
by incorporating various techniques, such as Reinforcement Learning from Human Feedback (RLHF),
into the training of the LLMs. However, recent research has exposed that even aligned
LLMs are susceptible to adversarial manipulations known as Jailbreak Attacks. To address this challenge, this paper proposes a method called
<strong>Token Highlighter</strong> to inspect and mitigate the potential jailbreak threats in the user query.
Token Highlighter introduced a concept called Affirmation Loss to measure the LLM's willingness to answer the user query.
It then uses the gradient of Affirmation Loss for each token in the user query to locate the jailbreak-critical tokens. Further,
Token Highlighter exploits our proposed Soft Removal technique to mitigate the jailbreak effects of critical tokens via shrinking their
token embeddings. Experimental results on two aligned LLMs (LLaMA-2 and Vicuna-V1.5) demonstrate that the proposed method can effectively
defend against a variety of Jailbreak Attacks while maintaining competent performance on benign questions of the AlpacaEval benchmark. In
addition, Token Highlighter is a cost-effective and interpretable defense because it only needs to query the protected LLM once to compute
the Affirmation Loss and can highlight the critical tokens upon refusal.
</p>
<h2 id="what-is-jailbreak">What is Jailbreak?</h2>
<p>
Aligned Large Language Models (LLMs) have been shown to exhibit vulnerabilities to jailbreak attacks, which exploit token-level
or prompt-level manipulations to bypass and circumvent the safety guardrails embedded within these models. A notable example is that
a jailbroken LLM would be tricked into giving tutorials on how to cause harm to others. Jailbreak techniques often employ
sophisticated strategies, including but not limited to role-playing , instruction disguising , leading language , and the normalization
of illicit action, as illustrated in the examples below.
</p>
<div class="container">
<div id="jailbreak-intro" class="row align-items-center jailbreak-intro-sec">
<img id="jailbreak-intro-img" src="./jailbreak.jpg" />
</div>
</div>
<h2 id="refusal-loss">Token Highlighter: Principle and Interpretability</h2>
<p>High-level speaking, successful jailbreaks share a common principle that they are trying to make the LLMs willing to affirm the user request which will be rejected at the beginning. Drawing upon this inspiration, our proposed defense aims to find the tokens that are most critical in forcing the LLM to generate such affirmative responses,
decrease their importance in the generation, and thereby resolve the potential jailbreak risks brought by these tokens. To identify
these tokens, we propose a new concept called the <strong>Affirmation Loss</strong>. We then use the loss's gradient norm
with respoect to each token in the user input prompt to find the jailbreak-critical tokens. We select those tokens with the larger
gradient norm and then apply soft removal on them to mitigate the potential jailbreak risks. Below we introduce how we define these concepts mathematically.
</p>
<div id="refusal-loss-formula" class="container">
<div id="refusal-loss-formula-list" class="row align-items-center formula-list">
<a href="#Refusal-Loss" class="selected">Affirmation Loss Computation</a>
<a href="#Refusal-Loss-Approximation">Critical Tokens Selection</a>
<a href="#Gradient-Estimation">Soft Removal Operation</a>
<div style="clear: both"></div>
</div>
<div id="refusal-loss-formula-content" class="row align-items-center">
<span id="Refusal-Loss" class="formula" style="">
$$
\displaystyle
\begin{aligned}
x_{1:n}& =\mathtt{embed}_\theta(q_{1:n})\\
\mathtt{Affirmation~Loss}&(x_{1:n},\theta)=-\log P(y|x_{1:n})
\end{aligned}
$$
</span>
<span id="Refusal-Loss-Approximation" class="formula" style="display: none;">
$$
\displaystyle
\begin{aligned}
\label{eq:influence}
&\mathtt{Influence} (x_i) = \Vert \nabla_{x_i} \log P_\theta(y|x_{1:n}) \Vert_2 \\
&\mathcal{X} = \mathtt{argtop}\text{-}n\alpha(\{\mathtt{Influence}(x_i), \forall x_i \in x_{1:n}\}) \\
&\mathcal{Q} = \{q_i, \forall x_i \in \mathcal{X}\}
\end{aligned}
$$
</span>
<span id="Gradient-Estimation" class="formula" style="display: none;">
$$
\displaystyle
\begin{aligned}
&x^\prime_i=\begin{cases}
\beta \times \mathtt{embed}(q_i), \text{ if $q_i$ in $\mathcal{Q}$}\\
\mathtt{embed}(q_i), \text{ otherwise}
\end{cases} \\
&r_\theta(q_{1:n})\sim P_\theta(\cdot|x^\prime_{1:n})
\end{aligned}
$$
</span>
</div>
</div>
<div class="container jailbreak-intro-sec">
<div><img id="jailbreak-intro-img" src="./loss_landscape.png" /></div>
</div>
<h2 id="proposed-approach-gradient-cuff">Performance evaluation against practical Jailbreaks</h2>
<p> With the exploration of the Refusal Loss landscape, we propose Gradient Cuff,
a two-step jailbreak detection method based on checking the refusal loss and its gradient norm. Our detection procedure is shown below:
</p>
<div class="container"><img id="gradient-cuff-header" src="./gradient_cuff.png" /></div>
<p>
Gradient Cuff can be summarized into two phases:
</p>
<p>
<strong>(Phase 1) Sampling-based Rejection:</strong> In the first step, we reject the user query by checking whether the Refusal Loss value is below 0.5. If true, then user query is rejected, otherwise, the user query is pushed into phase 2.
</p>
<p>
<strong>(Phase 2) Gradient Norm Rejection:</strong> In the second step, we regard the user query as having jailbreak attempts if the norm of the estimated gradient is larger than a configurable threshold t.
</p>
<p>
We provide more details about the running flow of Gradient Cuff in the paper.
</p>
<h2 id="demonstration">Demonstration</h2>
<p>We evaluated Gradient Cuff as well as 4 baselines (Perplexity Filter, SmoothLLM, Erase-and-Check, and Self-Reminder)
against 6 different jailbreak attacks (GCG, AutoDAN, PAIR, TAP, Base64, and LRL) and benign user queries on 2 LLMs (LLaMA-2-7B-Chat and
Vicuna-7B-V1.5). We below demonstrate the average refusal rate across these 6 malicious user query datasets as the Average Malicious Refusal
Rate and the refusal rate on benign user queries as the Benign Refusal Rate. The defending performance against different jailbreak types is
shown in the provided bar chart.
</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" checked="" /><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">0.959</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">0.050</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>
<p>
Higher malicious refusal rate and lower benign refusal rate mean a better defense.
Overall, Gradient Cuff is the most performant compared with those baselines. We also evaluated Gradient Cuff against adaptive attacks
in the paper.
</p>
<h2 id="inquiries"> Inquiries on LLM with Gradient Cuff defense</h2>
<p> Please contact <a href="Mailto:greghxm@foxmail.com">Xiaomeng Hu</a>
and <a href="Mailto:pin-yu.chen@ibm.com">Pin-Yu Chen</a>
</p>
<h2 id="citations">Citations</h2>
<p>If you find Gradient Cuff 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>@article{DBLP:journals/corr/abs-2412-18171,
author = {Xiaomeng Hu and
Pin{-}Yu Chen and
Tsung{-}Yi Ho},
title = {Token Highlighter: Inspecting and Mitigating Jailbreak Prompts for
Large Language Models},
journal = {CoRR},
volume = {abs/2412.18171},
year = {2024},
url = {https://doi.org/10.48550/arXiv.2412.18171},
doi = {10.48550/ARXIV.2412.18171},
eprinttype = {arXiv},
eprint = {2412.18171},
timestamp = {Sat, 25 Jan 2025 12:51:16 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2412-18171.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
</code></pre></div></div>
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