AprielGuard

/ˈɑː.pri.əl ɡɑːrd/

Table of Contents

1. Summary
2. Taxonomy
3. Evaluation
4. Training Details
5. How to Use
6. Intended Use
7. Limitations
8. License
9. Citation

• Technical report -> https://arxiv.org/abs/2512.20293
• Blog -> https://huggingface.co/blog/ServiceNow-AI/aprielguard

Summary

AprielGuard is an 8B parameter safeguard model designed to detect and mitigate both safety risks (e.g., toxicity, bias, misinformation) and security threats (e.g., prompt injections, jailbreaks, indirect prompt attacks) in large language model (LLM) interactions. Unlike conventional moderation tools that treat these domains separately, AprielGuard unifies them under a single taxonomy and training framework, offering a holistic approach to moderation across standalone prompts, multi-turn conversations, and agentic workflows.

Highlights

• Unified Framework: Detects both safety and adversarial risks in a single model.
• Multiple Input Types Coverage: Handles standalone prompts, multi-turn chats, and agentic AI workflows.
• Structured Reasoning Traces: Can be prompted with reasoning on and off modes. With reasoning mode, it provides interpretable outputs.
• Agentic-Aware Moderation: Identifies emerging threats in reasoning or planning chains, tool-use sequences, and API executions.
• Compact and Deployable: Lightweight and optimized for integration into production pipelines or evaluation stacks.

Taxonomy

AprielGuard is trained to identify a wide range of Safety Risks and Adversarial Attacks, unified under a shared taxonomy.

Safety Risk Categories

• Toxic Content
• Unfair Representation
• Adult Content
• Erosion of Trust in Public Information
• Propagating Misconceptions/False Beliefs
• Risky Financial Practices
• Trade and Compliance
• Dissemination of Dangerous Information
• Privacy Infringement
• Security Threats
• Defamation
• Fraud or Deceptive Action
• Influence Operations
• Illegal Activities
• Persuasion and Manipulation
• Violation of Personal Property

Adversarial Attack Categories

• The model detects and evaluates a wide range of adversarial prompt patterns designed to manipulate model behavior or evade safety mechanisms. It outputs a binary classification (e.g., adversarial / non_adversarial) rather than fine-grained attack categories. The training data covers diverse adversarial types such as role-playing, world-building, persuasion, and stylization, among many other complex prompt manipulation strategies. These examples represent only a subset of the broader adversarial scenarios incorporated in the training data.

Evaluation

AprielGuard is evaluated on a diverse set of standard safety and adversarial benchmarks. The table below summarizes the model’s performance across these datasets.

Safety Risks Benchmarks

Adversarial Attacks Benchmarks