# AprielGuard: A Guardrail for Safety and Adversarial Robustness in Modern LLM Systems

DevFeed: [AprielGuard: A Guardrail for Safety and Adversarial Robustness in Modern LLM Systems](<https://devfeed.tech/articles/aprielguard-a-guardrail-for-safety-and-adversarial-robustness-in-modern-llm-systems-7045.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/ServiceNow-AI/aprielguard>)

Author: Jaykumar Kasundra

Published: 2025-12-23T14:07:35Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [prompt injection](<https://devfeed.tech/topics/prompt-injection.md>), [Adversarial attacks](<https://devfeed.tech/topics/adversarial-attacks.md>), [Security](<https://devfeed.tech/topics/security.md>), [Chain-of-thought](<https://devfeed.tech/topics/chain-of-thought.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [attacks](<https://devfeed.tech/tags/attacks.md>), [chain-of-thought](<https://devfeed.tech/tags/chain-of-thought.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llm](<https://devfeed.tech/tags/llm.md>), [model](<https://devfeed.tech/tags/model.md>), [prompt-injection](<https://devfeed.tech/tags/prompt-injection.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [safety-security](<https://devfeed.tech/tags/safety-security.md>), [security](<https://devfeed.tech/tags/security.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

AprielGuard is an 8B-parameter safety and security safeguard model for modern LLM systems. It detects 16 categories of safety risks and a broad range of adversarial attacks, including prompt injection, jailbreaks, chain-of-thought corruption, context hijacking, memory poisoning, and multi-agent exploit sequences. It supports standalone prompts, multi-turn conversations, and agentic workflows containing tool calls, reasoning traces, memory, and system context. The model offers reasoning and non-reasoning modes for explainable or low-latency classification.

## Source excerpt

In this work, we introduce AprielGuard, an 8B parameter safety-security safeguard model designed to detect: - 16 categories of safety risks, spanning toxicity, hate, sexual content, misinformation, self-harm, illegal activities, and more. - Wide range of adversarial attacks, including prompt injection, jailbreaks, chain-of-thought corruption, context hijacking, memory poisoning, and multi-agent exploit sequences.