# BingoGuard: LLM Content Moderation Tools with Risk Levels

arXiv: https://arxiv.org/abs/2503.06550 · HTML: https://mlsanity.com/paper/2503.06550/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 46 references that are in the map.
- First posted on arXiv 9 Mar 2025.

## Builds on (most cited first, 30 of 37)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Jailbroken: How Does LLM Safety Training Fail?](https://mlsanity.com/paper/2307.02483/) (2023)
- [HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal](https://mlsanity.com/paper/2402.04249/) (2024)
- [Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations](https://mlsanity.com/paper/2312.06674/) (2023)
- [Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!](https://mlsanity.com/paper/2310.03693/) (2023)
- [BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset](https://mlsanity.com/paper/2307.04657/) (2023)
- [AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models](https://mlsanity.com/paper/2310.04451/) (2023)
- [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://mlsanity.com/paper/2209.07858/) (2022)
- [Safe RLHF: Safe Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2310.12773/) (2023)
- [WildChat: 1M ChatGPT Interaction Logs in the Wild](https://mlsanity.com/paper/2405.01470/) (2024)
- [GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts](https://mlsanity.com/paper/2309.10253/) (2023)
- [XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models](https://mlsanity.com/paper/2308.01263/) (2023)
- [WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs](https://mlsanity.com/paper/2406.18495/) (2024)
- [Texygen: A Benchmarking Platform for Text Generation Models](https://mlsanity.com/paper/1802.01886/) (2018)
- ["Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models](https://mlsanity.com/paper/2308.03825/) (2023)
- [LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset](https://mlsanity.com/paper/2309.11998/) (2023)
- [Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation](https://mlsanity.com/paper/2310.06987/) (2023)
- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [A Holistic Approach to Undesired Content Detection in the Real World](https://mlsanity.com/paper/2208.03274/) (2022)
- [Are aligned neural networks adversarially aligned?](https://mlsanity.com/paper/2306.15447/) (2023)
- [ShieldGemma: Generative AI Content Moderation Based on Gemma](https://mlsanity.com/paper/2407.21772/) (2024)
- [SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal](https://mlsanity.com/paper/2406.14598/) (2024)
- [ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation](https://mlsanity.com/paper/2310.17389/) (2023)

## Built on by (most cited first, 21 of 21)

- [X-Guard: Multilingual Guard Agent for Content Moderation](https://mlsanity.com/paper/2504.08848/) (2025)
- [PSG-Agent: Personality-Aware Safety Guardrail for LLM-based Agents](https://mlsanity.com/paper/2509.23614/) (2025)
- [ReasoningShield: Safety Detection over Reasoning Traces of Large Reasoning Models](https://mlsanity.com/paper/2505.17244/) (2025)
- [Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety](https://mlsanity.com/paper/2510.10994/) (2025)
- [Evaluating the Robustness of Large Language Model Safety Guardrails Against Adversarial Attacks](https://mlsanity.com/paper/2511.22047/) (2025)
- [Evaluating LLM-Generated Legal Explanations for Regulatory Compliance in Social Media Influencer Marketing](https://mlsanity.com/paper/2510.08111/) (2025)
- [GuardTrace-VL: Detecting Unsafe Multimodel Reasoning via Iterative Safety Supervision](https://mlsanity.com/paper/2511.20994/) (2025)
- [XGUARD: A Graded Benchmark for Evaluating Safety Failures of Large Language Models on Extremist Content](https://mlsanity.com/paper/2506.00973/) (2025)
- [Evaluating Large Language Models for Detecting Antisemitism](https://mlsanity.com/paper/2509.18293/) (2025)
- [Scaling Reinforcement Learning for Content Moderation with Large Language Models](https://mlsanity.com/paper/2512.20061/) (2025)
- [Predict, Don't React: Value-Based Safety Forecasting for LLM Streaming](https://mlsanity.com/paper/2604.03962/) (2026)
- [A Red Teaming Roadmap Towards System-Level Safety](https://mlsanity.com/paper/2506.05376/) (2025)
- [ExpGuard: LLM Content Moderation in Specialized Domains](https://mlsanity.com/paper/2603.02588/) (2026)
- [SafeDream: Safety World Model for Proactive Early Jailbreak Detection](https://mlsanity.com/paper/2604.16824/) (2026)
- [LLaVAShield: Safeguarding Multimodal Multi-Turn Dialogues in Vision-Language Models](https://mlsanity.com/paper/2509.25896/) (2025)
- [Adversarial Distilled Retrieval-Augmented Guarding Model for Online Malicious Intent Detection](https://mlsanity.com/paper/2509.14622/) (2025)
- [Robust LLM Performance Certification via Constrained Maximum Likelihood Estimation](https://mlsanity.com/paper/2604.03257/) (2026)
- [SafeLens: Deliberate and Efficient Video Guardrails with Fast-and-Slow Screening](https://mlsanity.com/paper/2605.17610/) (2026)
- [Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification](https://mlsanity.com/paper/2608.04899/) (2026)
- [Unaccountable Delegation, Fading Skills: Mapping the Risks of Workplace AI Agents](https://mlsanity.com/paper/2608.08601/) (2026)
- [Applying Language Models in Clinical Medicine: Recent Trends and Perspectives](https://mlsanity.com/paper/2609.34780/) (2026)

## Abstract (arXiv)

Malicious content generated by large language models (LLMs) can pose varying degrees of harm. Although existing LLM-based moderators can detect harmful content, they struggle to assess risk levels and may miss lower-risk outputs. Accurate risk assessment allows platforms with different safety thresholds to tailor content filtering and rejection. In this paper, we introduce per-topic severity rubrics for 11 harmful topics and build BingoGuard, an LLM-based moderation system designed to predict both binary safety labels and severity levels. To address the lack of annotations on levels of severity, we propose a scalable generate-then-filter framework that first generates responses across different severity levels and then filters out low-quality responses. Using this framework, we create BingoGuardTrain, a training dataset with 54,897 examples covering a variety of topics, response severity, styles, and BingoGuardTest, a test set with 988 examples explicitly labeled based on our severity rubrics that enables fine-grained analysis on model behaviors on different severity levels. Our BingoGuard-8B, trained on BingoGuardTrain, achieves the state-of-the-art performance on several moderation benchmarks, including WildGuardTest and HarmBench, as well as BingoGuardTest, outperforming best public models, WildGuard, by 4.3%. Our analysis demonstrates that incorporating severity levels into training significantly enhances detection performance and enables the model to effectively gauge the severity of harmful responses.