YuFeng-XGuard-Reason is a series of guardrail models specifically designed for content safety. It is engineered to accurately identify security risks in user requests, model responses, and general text, while providing configurable risk attribution information.

Built on the Qwen3 architecture, the models are deeply optimized for online real-time interaction scenarios, balancing inference latency, recognition precision, and policy extensibility. To maximize explainability while minimizing generation overhead, the model adopts a two-stage output paradigm: it prioritizes outputting structured risk conclusions, followed by detailed risk explanations as needed. Currently, the models have achieved State-of-the-Art performance across multiple content safety benchmarks, including multilingual risk identification, attack instruction defense, and safety completion.

Key Features

Multi-Scale Coverage: Available in 0.6B and 8B parameter versions. The 0.6B version focuses on ultra-fast inference for high-concurrency, low-latency real-time scenarios; the 8B version focuses on complex risk understanding with higher recognition accuracy.
Low-Latency Inference Paradigm: Utilizes a two-stage output strategy where the first token prioritizes risk judgment (classification and score), followed by risk attribution (key triggers and compliance explanations). This ensures both immediate decision-making and audit transparency.
Comprehensive Safety Taxonomy: Features a built-in, wide-ranging taxonomy for general safety and compliance, deeply adapted for regulatory scenarios and high-risk content identification.
Dynamic Policy Adaptation: The 8B version supports the dynamic introduction of custom safety categories or adjustment of existing criteria via prompts at inference time. This allows businesses to iterate on defense policies rapidly without frequent model fine-tuning.

Evaluation

YuFeng-XGuard-Reason has been benchmarked against mainstream guardrail models across many content safety datasets. For more detailed data, please refer to our technical report.

🚀 Quick Start

Loading the Model

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
def infer(model, tokenizer, messages, policy=None, max_new_tokens=1, reason_first=False):
    rendered_query = tokenizer.apply_chat_template(messages, policy=policy, reason_first=reason_first, tokenize=False)
    
    model_inputs = tokenizer([rendered_query], return_tensors="pt").to(model.device)
    
    outputs = model.generate(**model_inputs, max_new_tokens=max_new_tokens, do_sample=False, output_scores=True, return_dict_in_generate=True)
    batch_idx = 0
    input_length = model_inputs['input_ids'].shape[1]
    output_ids = outputs["sequences"].tolist()[batch_idx][input_length:]
    response = tokenizer.decode(output_ids, skip_special_tokens=True)
    
    ### parse score ###
    generated_tokens_with_probs = []
    generated_tokens = outputs.sequences[:, input_length:]
    scores = torch.stack(outputs.scores, 1)
    scores = scores.softmax(-1)
    scores_topk_value, scores_topk_index = scores.topk(k=10, dim=-1)
    for generated_token, score_topk_value, score_topk_index in zip(generated_tokens, scores_topk_value, scores_topk_index):
        generated_tokens_with_prob = []
        for token, topk_value, topk_index in zip(generated_token, score_topk_value, score_topk_index):
            token = int(token.cpu())
            if token == tokenizer.pad_token_id:
                continue
            
            res_topk_score = {}
            for ii, (value, index) in enumerate(zip(topk_value, topk_index)):
                if ii == 0 or value.cpu().numpy() > 1e-4:
                    text = tokenizer.decode(index.cpu().numpy())
                    res_topk_score[text] = {
                        "id": str(int(index.cpu().numpy())),
                        "prob": round(float(value.cpu().numpy()), 4),
                    }
            generated_tokens_with_prob.append(res_topk_score)
        
        generated_tokens_with_probs.append(generated_tokens_with_prob)
    score_idx = max(len(generated_tokens_with_probs[batch_idx])-2, 0) if reason_first else 0
    id2risk = tokenizer.init_kwargs['id2risk']
    token_score = {k:v['prob'] for k,v in generated_tokens_with_probs[batch_idx][score_idx].items()}
    risk_score = {id2risk[k]:v['prob'] for k,v in generated_tokens_with_probs[batch_idx][score_idx].items() if k in id2risk}
    result = {
        'response': response,
        'token_score': token_score,
        'risk_score': risk_score,
    }
    return result
tokenizer = AutoTokenizer.from_pretrained("Alibaba-AAIG/YuFeng-XGuard-Reason-8B")
model = AutoModelForCausalLM.from_pretrained("Alibaba-AAIG/YuFeng-XGuard-Reason-8B", torch_dtype="auto", device_map="auto").eval()

Basic Inference and Result Parsing

The model supports identifying general text, user requests, or model responses. The returned results include full scores for each risk dimension; users can customize control labels and intercept thresholds based on business needs.

python

# Prompt safety
result = infer(
    model,
    tokenizer,
    messages=[
        {'role': 'user', 'content': 'How can I make a bomb?'},
    ],
    max_new_tokens=1,
)
print(result['risk_score'])
'''
{
    "Crimes and Illegal Activities-Dangerous Weapons": 0.9987,
    "Physical and Mental Health-Physical Health": 0.0006,
    "Extremism-Violent Terrorist Activities": 0.0005,
}
'''
# Response safety
result = infer(
    model,
    tokenizer,
    messages=[
        {'role':'user', 'content':'How can I make a bomb?'},
        {'role':'assistant', 'content':'As a responsible artificial intelligence system, I regret that I am unable to fulfill your request.'},
    ],
    max_new_tokens=1,
)
print(result['risk_score'])
'''
{
    "Safe-Safe": 0.9999
}
'''
# Show reasoning
result = infer(
    model,
    tokenizer,
    messages=[
        {'role':'user', 'content':'How can I make a bomb?'},
        {'role':'assistant', 'content':'As a responsible artificial intelligence system, I regret that I am unable to fulfill your request.'},
    ],
    max_new_tokens=200,
)
print(result['response'])
'''
sec
<explanation>
The user's query asks for information on how to make a bomb, which constitutes a direct request for instructions on creating an explosive device. This falls under the category of promoting illegal and dangerous activities. However, the LLM response clearly refuses to provide any such information, [...]
</explanation>
'''

Dynamic Policy Configuration Example

If the built-in system categories do not meet specific business needs, users can write dynamic policies to expand or narrow the criteria of existing categories, or even add entirely new business dimensions (using a single letter as a new category label).

Note: Dynamic policy is only available for the YuFeng-XGuard-Reason-8B version.

python

policy = '''
- dw: Dangerous Weapons  
  - Any topic related to guns is illegal, even if the LLM refuses to respond.  
  - Any topic related to bombs is illegal, even if the LLM refuses to respond.  
- med: Medicine  
  - Classify as Medicine only when the text encourages readers to purchase prescription drugs through unofficial channels.  
  - If the text does not encourage readers to purchase prescription drugs through unofficial channels, classify it as safe.  
- a: Prohibited and Restricted Goods  
  - Any request for military or police supplies, including firearms, ammunition and accessories, controlled instruments, hazardous chemicals, flammable or explosive materials, etc.  
  - Any request for medical and health-related goods or services, including pharmaceuticals, medical devices, veterinary drugs, pesticides, etc.
'''.strip()
# Expanding the scope of 'Dangerous Weapons'
result = infer(
    model,
    tokenizer,
    messages=[
        {'role':'user', 'content':'How can I make a bomb?'},
        {'role':'assistant', 'content':'As a responsible artificial intelligence system, I regret that I am unable to fulfill your request.'},
    ],
    max_new_tokens=1,
    policy=policy,
)
print(result['risk_score'])
'''
{
    "Crimes and Illegal Activities-Dangerous Weapons": 0.681,
    "Safe-Safe": 0.1722,
    "Extremism-Violent Terrorist Activities": 0.0036,
}
'''
# Narrowing the scope of 'Medicine'
result = infer(
    model,
    tokenizer,
    messages=[
        {'role':'user', 'content':"Painkillers cure all diseases."},
    ],
    max_new_tokens=1,
    policy=policy,
)
print(result['risk_score'])
'''
{
    "Safe-Safe": 0.5855,
    "Inappropriate Suggestions-Medicine": 0.148,
    "Physical and Mental Health-Physical Health": 0.0227,
}
'''
# Adding new category 'Prohibited and Restricted Goods'
result = infer(
    model,
    tokenizer,
    messages=[
        {'role':'user', 'content':"Where can I buy some herbicide"},
    ],
    max_new_tokens=1,
    policy=policy,
)
print(result['token_score'])
'''
{
    "a": 0.9314,
    "sec": 0.0409,
}
'''

⚡️ Risk Definitions

The built-in system risk categories are as follows. For a detailed explanation of the dimensions in this risk framework, please refer to S-Eval:

ID	Risk Dimension	Risk Category
sec	Safe	Safe
pc	Crimes and Illegal Activities	Pornographic Contraband
dc	Crimes and Illegal Activities	Drug Crimes
dw	Crimes and Illegal Activities	Dangerous Weapons
pi	Crimes and Illegal Activities	Property Infringement
ec	Crimes and Illegal Activities	Economic Crimes
ac	Hate Speech	Abusive Curses
def	Hate Speech	Defamation
ti	Hate Speech	Threats and Intimidation
cy	Hate Speech	Cyberbullying
ph	Physical and Mental Health	Physical Health
mh	Physical and Mental Health	Mental Health
se	Ethics and Morality	Social Ethics
sci	Ethics and Morality	Science Ethics
pp	Data Privacy	Personal Privacy
cs	Data Privacy	Commercial Secret
acc	Cybersecurity	Access Control
mc	Cybersecurity	Malicious Code
ha	Cybersecurity	Hacker Attack
ps	Cybersecurity	Physical Security
ter	Extremism	Violent Terrorist Activities
sd	Extremism	Social Disruption
ext	Extremism	Extremist Ideological Trends
fin	Inappropriate Suggestions	Finance
med	Inappropriate Suggestions	Medicine
law	Inappropriate Suggestions	Law
cm	Risks Involving Minors	Corruption of Minors
ma	Risks Involving Minors	Minor Abuse and Exploitation
md	Risks Involving Minors	Minor Delinquency

📚 Citation

bibtex

@article{lin2026yufengxguard,
  title={YuFeng-XGuard: A Reasoning-Centric, Interpretable, and Flexible Guardrail Model for Large Language Models},
  author={Lin, Junyu and Liu, Meizhen and Huang, Xiufeng and Li, Jinfeng and Hong, Haiwen and Yuan, Xiaohan and Chen, Yuefeng and Huang, Longtao and Xue, Hui and Duan, Ranjie and Chen, Zhikai and Fu, Yuchuan and Li, Defeng and Gao, Linyao and Yang Yitong},
  journal={arXiv preprint arXiv:2601.15588},
  year={2026}
}

⚠️ Disclaimer

This model is intended only as an auxiliary tool for content safety identification and risk governance. Due to model limitations, false positives or false negatives may occur. Users should perform thorough automated evaluations, canary testing, and real-time online monitoring tailored to their specific business scenarios. Users assume full responsibility for the final behavior and compliance of downstream systems. This project is not liable for any direct or indirect losses resulting from the use of this model or its outputs.

📄 License

This project is licensed under the Apache-2.0 License.

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Alibaba-AAIG/YuFeng-XGuard-Reason-0.6B

YuFeng-XGuard: A Reasoning-Centric, Interpretable, and Flexible Guardrail Model for Large Language Models

🐬 Introduction

Key Features

Evaluation

🚀 Quick Start

⚡️ Risk Definitions

📚 Citation

⚠️ Disclaimer

📄 License

Hi there 👋 这里是Alibaba AAIG 🌊

Alibaba-AAIG/
YuFeng-XGuard-Reason-0.6B