mlsanityData to 7 Oct 2026

Bridging the Safety Gap: A Guardrail Pipeline for Trustworthy LLM Inferences

arXiv 2502.08142 ↗First posted 12 Feb 2025cs.AIcited by 7

On the map: LLM safety and security → Safety alignment, refusal and guardrails

Abstract (arXiv)

We present Wildflare GuardRail, a guardrail pipeline designed to enhance the safety and reliability of Large Language Model (LLM) inferences by systematically addressing risks across the entire processing workflow. Wildflare GuardRail integrates several core functional modules, including Safety Detector that identifies unsafe inputs and detects hallucinations in model outputs while generating root-cause explanations, Grounding that contextualizes user queries with information retrieved from vector databases, Customizer that adjusts outputs in real time using lightweight, rule-based wrappers, and Repairer that corrects erroneous LLM outputs using hallucination explanations provided by Safety Detector. Results show that our unsafe content detection model in Safety Detector achieves comparable performance with OpenAI API, though trained on a small dataset constructed with several public datasets. Meanwhile, the lightweight wrappers can address malicious URLs in model outputs in 1.06s per query with 100% accuracy without costly model calls. Moreover, the hallucination fixing model demonstrates effectiveness in reducing hallucinations with an accuracy of 80.7%.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security. Builds on 32 of its 65 references that are in the map. First posted on arXiv 12 Feb 2025.

Builds on

32 of 65 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024MemeGuard: An LLM and VLM-based Framework for Advancing Content Moderation via Meme Intervention8 Jun
  2. 2023Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!5 Oct

Other references · 30

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  4. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  5. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  6. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  7. 2023BloombergGPT: A Large Language Model for Financecited by 923
  8. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  9. 2024TinyLlama: An Open-Source Small Language Modelcited by 604
  10. 2023Prompt Injection attack against LLM-integrated Applicationscited by 568
  11. 2023DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Modelscited by 505
  12. 2023DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Modelscited by 367
18 more
  1. 2023A Survey of Hallucination in Large Foundation Modelscited by 365
  2. 2024Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attackcited by 364
  3. 2023NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Railscited by 347
  4. 2022A Holistic Approach to Undesired Content Detection in the Real Worldcited by 337
  5. 2023ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledgecited by 324
  6. 2023Certifying LLM Safety against Adversarial Promptingcited by 248
  7. 2023ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversationcited by 224
  8. 2023Large Language Models in Finance: A Surveycited by 223
  9. 2021A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generationcited by 137
  10. 2024Weak-to-Strong Jailbreaking on Large Language Modelscited by 100
  11. 2024JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMscited by 87
  12. 2024Stable LM 2 1.6B Technical Reportcited by 86
  13. 2020Controlled Hallucinations: Learning to Generate Faithfully from Noisy Datacited by 83
  14. 2024OpenELM: An Efficient Language Model Family with Open Training and Inference Frameworkcited by 74
  15. 2024Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Cluescited by 57
  16. 2023Adapting Large Language Models for Content Moderation: Pitfalls in Data Engineering and Supervised Fine-tuningcited by 28
  17. 2022CVNets: High Performance Library for Computer Visioncited by 16
  18. 2023On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlookcited by 15

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.

  1. 2025Get Experience from Practice: LLM Agents with Record & Replaycited by 22
  2. 2025MUSE: MCTS-Driven Red Teaming Framework for Enhanced Multi-Turn Dialogue Safety in Large Language Modelscited by 4
  3. 2025Scam2Prompt: A Scalable Framework for Auditing Malicious Scam Endpoints in Production LLMscited by 2
  4. 2025The Tower of Babel Revisited: Multilingual Jailbreak Prompts on Closed-Source Large Language Modelscited by 1
  5. 2026No More, No Less: Least-Privilege Language Modelscited by 1
  6. 2026Counting Worlds Branching Time Semantics for post-hoc Bias Mitigation in generative AIcited by 0
  7. 2026Choosing Where and How to Moderate: End-to-End Trade-offs in Filter Placement and Response Rewritingcited by 0