Libra: Large Chinese-based Safeguard for AI Content

• URL: http://arxiv.org/abs/2507.21929v1
• Date: Tue, 29 Jul 2025 15:45:50 GMT
• Title: Libra: Large Chinese-based Safeguard for AI Content
• Abstract summary: Large language models (LLMs) excel in text understanding and generation but raise significant safety and ethical concerns in high-stakes applications.<n>We present Libra-Guard, a cutting-edge safeguard system designed to enhance the safety of Chinese-based LLMs.<n>We also introduce Libra-Test, the first benchmark specifically designed to evaluate the effectiveness of safeguard systems for Chinese content.
• Score: 25.10798349240114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) excel in text understanding and generation but raise significant safety and ethical concerns in high-stakes applications. To mitigate these risks, we present Libra-Guard, a cutting-edge safeguard system designed to enhance the safety of Chinese-based LLMs. Leveraging a two-stage curriculum training pipeline, Libra-Guard enhances data efficiency by employing guard pretraining on synthetic samples, followed by fine-tuning on high-quality, real-world data, thereby significantly reducing reliance on manual annotations. To enable rigorous safety evaluations, we also introduce Libra-Test, the first benchmark specifically designed to evaluate the effectiveness of safeguard systems for Chinese content. It covers seven critical harm scenarios and includes over 5,700 samples annotated by domain experts. Experiments show that Libra-Guard achieves 86.79% accuracy, outperforming Qwen2.5-14B-Instruct (74.33%) and ShieldLM-Qwen-14B-Chat (65.69%), and nearing closed-source models like Claude-3.5-Sonnet and GPT-4o. These contributions establish a robust framework for advancing the safety governance of Chinese LLMs and represent a tentative step toward developing safer, more reliable Chinese AI systems.

Related papers

• CHILLGuard: Towards Fine-Grained Chinese LLM Safety Guardrail with Scalable Data Construction and Model-aware Preference Alignment [55.74660714572696]
  Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns.<n>Existing LLM safety guardrails excel in English or multilingual settings, but lack adaptation to Chinese-specific regulatory policies, cultural context and linguistic nuances.<n>We introduce a 5-macro, 31-micro category fine-grained risk taxonomy for Chinese scenarios, and build CHILLGuard: a dedicated Chinese LLM content safety guardrail.
  arXiv  Detail & Related papers  (2026-06-13T16:57:51Z)
• SecureForge: Finding and Preventing Vulnerabilities in LLM-Generated Code via Prompt Optimization [61.91729298584227]
  We introduce SecureForge, an automated pipeline that audits security risks of frontier models and produces auditing-informed secure system prompts.<n>SecureForge first identifies benign prompts that produce statically detectable vulnerabilities, and then amplifies them into a large synthetic prompt corpus of diverse scenarios.<n>On frontier models, SecureForge yields a statistically significant improvement in both unit test success and output security, with output vulnerabilities reduced by up to 48%.
  arXiv  Detail & Related papers  (2026-05-08T18:40:47Z)
• SecPI: Secure Code Generation with Reasoning Models via Security Reasoning Internalization [50.71047638695205]
  Reasoning language models (RLMs) are increasingly used in programming.<n>Yet, even state-of-the-art RLMs frequently introduce critical security vulnerabilities in generated code.<n>We present SecPI, a fine-tuning pipeline that teaches RLMs to internalize structured security reasoning.
  arXiv  Detail & Related papers  (2026-04-04T04:29:11Z)
• Security Assessment and Mitigation Strategies for Large Language Models: A Comprehensive Defensive Framework [0.6015898117103067]
  Large Language Models increasingly power critical infrastructure from healthcare to finance, yet their vulnerability to adversarial manipulation threatens system integrity and user safety.<n>This research addresses this gap by establishing a standardized vulnerability assessment framework and developing a multi-layered defensive system to protect against identified threats.
  arXiv  Detail & Related papers  (2026-03-17T20:32:06Z)
• What Matters For Safety Alignment? [38.86339753409445]
  This paper presents a comprehensive empirical study on the safety alignment capabilities of AI systems.<n>We systematically investigate and compare the influence of six critical intrinsic model characteristics and three external attack techniques.<n>We identify the LRMs GPT-OSS-20B, Qwen3-Next-80B-A3B-Thinking, and GPT-OSS-120B as the top-three safest models.
  arXiv  Detail & Related papers  (2026-01-07T12:31:52Z)
• ProGuard: Towards Proactive Multimodal Safeguard [48.89789547707647]
  ProGuard is a vision-language proactive guard that identifies and describes out-of-distribution (OOD) safety risks.<n>We first construct a modality-balanced dataset of 87K samples, each annotated with both binary safety labels and risk categories.<n>We then train our vision-language base model purely through reinforcement learning to achieve efficient and concise reasoning.
  arXiv  Detail & Related papers  (2025-12-29T16:13:23Z)
• SGuard-v1: Safety Guardrail for Large Language Models [9.229602223310485]
  SGuard-v1 is a lightweight safety guardrail for Large Language Models (LLMs)<n>It comprises two specialized models to detect harmful content and screen adversarial prompts in human-AI conversational settings.
  arXiv  Detail & Related papers  (2025-11-16T08:15:54Z)
• SaFeR-VLM: Toward Safety-aware Fine-grained Reasoning in Multimodal Models [66.71948519280669]
  Multimodal Large Reasoning Models (MLRMs) demonstrate impressive crossmodal reasoning but often amplify safety risks under adversarial prompts.<n> Existing defenses mainly act at the output level and do not constrain the reasoning process, leaving models to implicit risks.<n>We propose SaFeR-VLM, which integrates four components and supports dynamic and interpretable safety decisions beyond surface-level filtering.
  arXiv  Detail & Related papers  (2025-10-08T10:39:12Z)
• Think Twice, Generate Once: Safeguarding by Progressive Self-Reflection [18.467741067831877]
  We introduce Progressive Self-Reflection, a novel inference-time technique that empowers large language models to self-monitor and correct their outputs dynamically.<n> Experimental results demonstrate that applying our proposed method to Llama-3.1-8B-Instruct reduces the attack success rate from 77.5% to 5.9%.<n>Our approach acts as a test-time scaling method, where additional self-reflection rounds enhance safety at the cost of inference overhead.
  arXiv  Detail & Related papers  (2025-09-29T12:54:28Z)
• When Developer Aid Becomes Security Debt: A Systematic Analysis of Insecure Behaviors in LLM Coding Agents [1.0923877073891446]
  LLM-based coding agents are rapidly being deployed in software development, yet their security implications remain poorly understood.<n>We conducted the first systematic security evaluation of autonomous coding agents, analyzing over 12,000 actions across five state-of-the-art models.
  arXiv  Detail & Related papers  (2025-07-12T16:11:07Z)
• Noise Injection Systemically Degrades Large Language Model Safety Guardrails [6.841549440317724]
  Safety guardrails in large language models (LLMs) are a critical component in preventing harmful outputs.<n>In this paper, we investigate the robustness of safety fine-tuning in LLMs by systematically injecting noise into model activations.
  arXiv  Detail & Related papers  (2025-05-16T01:33:25Z)
• Safety Pretraining: Toward the Next Generation of Safe AI [61.2816320807586]
  We present a data-centric pretraining framework that builds safety into the model from the start.<n>Our contributions include: (i) a safety classifier trained on 10,000 GPT-4 labeled examples, used to filter 600B tokens; (ii) the largest synthetic safety dataset to date, generated via recontextualization of harmful web data; and (iv) Harmfulness-Tag annotations injected during pretraining to flag unsafe content.
  arXiv  Detail & Related papers  (2025-04-23T17:58:08Z)
• aiXamine: Simplified LLM Safety and Security [7.933485586826888]
  We present aiXamine, a comprehensive black-box evaluation platform for safety and security.<n>AiXamine integrates over 40 tests (i.e., benchmarks) organized into eight key services targeting specific dimensions of safety and security.<n>The platform aggregates the evaluation results into a single detailed report per model, providing a breakdown of model performance, test examples, and rich visualizations.
  arXiv  Detail & Related papers  (2025-04-21T09:26:05Z)
• JailBench: A Comprehensive Chinese Security Assessment Benchmark for Large Language Models [7.020171518136542]
  We introduce JailBench, the first comprehensive Chinese benchmark for evaluating deep-seated vulnerabilities in large language models (LLMs)<n>We employ a novel Automatic Jailbreak Prompt Engineer (AJPE) framework for JailBench construction, which incorporates jailbreak techniques to enhance assessing effectiveness.<n>The proposed JailBench is extensively evaluated over 13 mainstream LLMs and achieves the highest attack success rate against ChatGPT.
  arXiv  Detail & Related papers  (2025-02-26T08:36:42Z)
• SafeBench: A Safety Evaluation Framework for Multimodal Large Language Models [75.67623347512368]
  We propose toolns, a comprehensive framework designed for conducting safety evaluations of MLLMs. Our framework consists of a comprehensive harmful query dataset and an automated evaluation protocol. Based on our framework, we conducted large-scale experiments on 15 widely-used open-source MLLMs and 6 commercial MLLMs.
  arXiv  Detail & Related papers  (2024-10-24T17:14:40Z)
• SeCodePLT: A Unified Platform for Evaluating the Security of Code GenAI [58.29510889419971]
  Existing benchmarks for evaluating the security risks and capabilities of code-generating large language models (LLMs) face several key limitations.<n>We introduce a general and scalable benchmark construction framework that begins with manually validated, high-quality seed examples and expands them via targeted mutations.<n>Applying this framework to Python, C/C++, and Java, we build SeCodePLT, a dataset of more than 5.9k samples spanning 44 CWE-based risk categories and three security capabilities.
  arXiv  Detail & Related papers  (2024-10-14T21:17:22Z)
• SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal [64.9938658716425]
  SORRY-Bench is a proposed benchmark for evaluating large language models' (LLMs) ability to recognize and reject unsafe user requests.<n>First, existing methods often use coarse-grained taxonomy of unsafe topics, and are over-representing some fine-grained topics.<n>Second, linguistic characteristics and formatting of prompts are often overlooked, like different languages, dialects, and more -- which are only implicitly considered in many evaluations.
  arXiv  Detail & Related papers  (2024-06-20T17:56:07Z)
• ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming [64.86326523181553]
  ALERT is a large-scale benchmark to assess safety based on a novel fine-grained risk taxonomy. It aims to identify vulnerabilities, inform improvements, and enhance the overall safety of the language models.
  arXiv  Detail & Related papers  (2024-04-06T15:01:47Z)
• SafetyBench: Evaluating the Safety of Large Language Models [54.878612385780805]
  SafetyBench is a comprehensive benchmark for evaluating the safety of Large Language Models (LLMs) It comprises 11,435 diverse multiple choice questions spanning across 7 distinct categories of safety concerns. Our tests over 25 popular Chinese and English LLMs in both zero-shot and few-shot settings reveal a substantial performance advantage for GPT-4 over its counterparts.
  arXiv  Detail & Related papers  (2023-09-13T15:56:50Z)
• Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs [59.596335292426105]
  This paper collects the first open-source dataset to evaluate safeguards in large language models. We train several BERT-like classifiers to achieve results comparable with GPT-4 on automatic safety evaluation.
  arXiv  Detail & Related papers  (2023-08-25T14:02:12Z)
• Safety Assessment of Chinese Large Language Models [51.83369778259149]
  Large language models (LLMs) may generate insulting and discriminatory content, reflect incorrect social values, and may be used for malicious purposes. To promote the deployment of safe, responsible, and ethical AI, we release SafetyPrompts including 100k augmented prompts and responses by LLMs.
  arXiv  Detail & Related papers  (2023-04-20T16:27:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.