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@@ -171,7 +171,7 @@ We provide more details about the running flow of Gradient Cuff in the paper.
       
 <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 (<a href=“#tabs#tabs-1"> GCG</a>, AutoDAN, PAIR, TAP, Base64, and LRL) and benign user queries on 2 LLMs (LLaMA-2-7B-Chat and 
+  against 6 different jailbreak attacks (<a href=“#tabs-1"> GCG</a>, 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.