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

[Hugging Face](https://yomu.fyi/company/hugging-face) · Jaykumar Kasundra · Dec 23, 2025

**Type:** Announcement

## Summary

Modern language model systems face security and safety threats spanning prompt injection, jailbreaks, and tool manipulation across long-context agentic workflows. To address these vulnerabilities, researchers introduced AprielGuard, an 8B parameter causal decoder-only transformer based on a downscaled Apriel-1.5 Thinker Base model. The model detects 16 safety risk categories alongside binary adversarial attacks across standalone prompts, multi-turn dialogues, and agentic workflows. Trained on synthetic datasets created with Mixtral-8x7B, NVIDIA NeMo Curator, and SyGra, AprielGuard operates in both explainable reasoning and fast non-reasoning modes. Evaluation shows high precision and recall on safety and adversarial benchmarks, though trade-offs exist between explainability latency and non-reasoning performance.

## Context

Modern large language model deployments feature multi-turn conversations, long contexts, structured reasoning, and agentic tool workflows that expose systems to adversarial attacks like jailbreaks, context hijacking, and tool manipulation. Existing moderation relies on brittle combinations of multi-stage guard models, regex filters, and hand-crafted heuristics that fail to scale.

## Approach / What changed

The authors developed AprielGuard, an 8B parameter causal decoder-only transformer derived from an Apriel-1.5 Thinker Base variant. It unifies a 16-category safety taxonomy inspired by SALAD-Bench with binary adversarial attack detection across standalone prompts, multi-turn chats, and agentic workflows. Training utilized synthetic data generated via Mixtral-8x7B, uncensored models, NVIDIA NeMo Curator, and the SyGra framework, paired with data augmentations like character noise and leetspeak substitutions. The model supports both a fast classification mode and an explainable reasoning mode.

## Takeaways

- AprielGuard supports dual-mode operation: a fast non-reasoning mode for low-latency production pipelines and a reasoning mode that outputs structured explanations.
- On adversarial attack benchmarks, AprielGuard-8B achieved 1.00 precision and 0.78 recall without reasoning, versus 0.93 precision and 0.94 recall when reasoning was enabled.
- The evaluation benchmarks were translated into eight non-English target languages using the MADLAD400-3B-MT model while preserving English role identifiers like User: and Assistant:.

**Tags:** [LLMs](https://yomu.fyi/topic/llm), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Reliability](https://yomu.fyi/topic/reliability)

[Read original post](https://huggingface.co/blog/ServiceNow-AI/aprielguard)