LLMGuard: Guarding Against Unsafe LLM Behavior
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Abstract (arXiv)
Although the rise of Large Language Models (LLMs) in enterprise settings brings new opportunities and capabilities, it also brings challenges, such as the risk of generating inappropriate, biased, or misleading content that violates regulations and can have legal concerns. To alleviate this, we present "LLMGuard", a tool that monitors user interactions with an LLM application and flags content against specific behaviours or conversation topics. To do this robustly, LLMGuard employs an ensemble of detectors.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 7 of its 17 references that are in the map. First posted on arXiv 27 Feb 2024.
Builds on
7 of 17 references.
References · 7
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
- 2016Ex Machina: Personal Attacks Seen at Scalecited by 208
- 2021AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processingcited by 131
- 2023The MiniPile Challenge for Data-Efficient Language Modelscited by 74
- 2021MathBERT: A Pre-Trained Model for Mathematical Formula Understandingcited by 58
- 2023Advancements in Scientific Controllable Text Generation Methodscited by 7
Built on by
Citing papers per year: 2024: 3; 2025: 6; 2026: 1.
10 papers.
- 2025SoK: Evaluating Jailbreak Guardrails for Large Language Modelscited by 17
- 2024Swiss Cheese Model for AI Safety: A Taxonomy and Reference Architecture for Multi-Layered Guardrails of Foundation Model Based Agentscited by 13
- 2026AgentTrace: A Structured Logging Framework for Agent System Observabilitycited by 8
- 2024LoRA-Guard: Parameter-Efficient Guardrail Adaptation for Content Moderation of Large Language Modelscited by 7
- 2025Takedown: How It's Done in Modern Coding Agent Exploitscited by 6
- 2025Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMscited by 3
- 2025Playing Devil's Advocate: Unmasking Toxicity and Vulnerabilities in Large Vision-Language Modelscited by 2
- 2024Building Understandable Messaging for Policy and Evidence Review (BUMPER) with AIcited by 0
- 2025Grade Guard: A Smart System for Short Answer Automated Gradingcited by 0
- 2025When Harmful Content Gets Camouflaged: Unveiling Perception Failure of LVLMs with CamHarmTIcited by 0
- 2026AgentTrace: A Structured Logging Framework for Agent System Observability7 Feb 2026
- 2025When Harmful Content Gets Camouflaged: Unveiling Perception Failure of LVLMs with CamHarmTI29 Nov 2025
- 2025Takedown: How It's Done in Modern Coding Agent Exploits29 Sep 2025
- 2025Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs16 Jun 2025
- 2025SoK: Evaluating Jailbreak Guardrails for Large Language Models12 Jun 2025
- 2025Grade Guard: A Smart System for Short Answer Automated Grading1 Apr 2025
- 2025Playing Devil's Advocate: Unmasking Toxicity and Vulnerabilities in Large Vision-Language Models14 Jan 2025
- 2024Swiss Cheese Model for AI Safety: A Taxonomy and Reference Architecture for Multi-Layered Guardrails of Foundation Model Based Agents5 Aug 2024
- 2024LoRA-Guard: Parameter-Efficient Guardrail Adaptation for Content Moderation of Large Language Models3 Jul 2024
- 2024Building Understandable Messaging for Policy and Evidence Review (BUMPER) with AI27 Jun 2024