Digital Event Horizon
AprielGuard is a groundbreaking AI model designed to safeguard Large Language Models from adversarial attacks. With its advanced safety taxonomy and robust detection capabilities, AprielGuard has been proven to outperform existing models in detecting safety risks and adversarial attacks. However, further testing and calibration are necessary to ensure the model's reliability in diverse languages and scenarios.
ApielGuard is an AI model designed to detect safety risks and adversarial attacks on Large Language Models (LLMs). The model detects 16 categories of safety risks, including toxicity, hate, and illegal activities. ApielGuard operates across three input formats: standalone prompts, multi-turn conversations, and agentic workflows. The model has a range of data augmentation techniques to enhance robustness. ApielGuard is available in both reasoning and non-reasoning modes for explainable classification and low-latency classification.
AprielGuard, a cutting-edge AI model, has been introduced to tackle the growing threat of adversarial attacks on Large Language Models (LLMs). These models have become increasingly sophisticated in recent years, enabling them to perform multi-step reasoning, call external tools, retrieve memory, and execute code. However, this evolution comes with an ever-evolving threat landscape that poses significant challenges for safety and security.
In traditional safety classifiers, the focus is limited to a narrow classification spectrum, such as toxicity or self-harm, assuming short inputs, and evaluating single user messages. In contrast, modern deployments feature multi-turn conversations, long contexts, structured reasoning steps producing chains of thought, tool-assisted multi-step workflows (agents), and a growing class of adversarial attacks exploiting reasoning, tools, or memory.
To address these issues, AprielGuard has been designed to detect 16 categories of safety risks spanning toxicity, hate, sexual content, misinformation, self-harm, illegal activities, and more. It also detects and evaluates a wide range of adversarial attack patterns designed to manipulate model behavior or evade safety mechanisms.
AprielGuard operates across three input formats: standalone prompts, multi-turn conversations, and agentic workflows (tool calls, reasoning traces, memory, system context). It outputs safety classification and a list of violated categories from the taxonomy, adversarial attack classification, and optional structured reasoning explaining the decision.
AprielGuard is trained on a synthetically generated training dataset. The training data points are generated at a sub-topic level of the taxonomy for better coverage. To enhance model robustness, a range of data augmentation techniques were applied to the training data. These augmentations include character-level noise, insertion of typographical errors, leetspeak substitutions, word-level paraphrasing, and syntactic reordering.
The model architecture is built on top of an Apriel-1.5 Thinker Base variant, downscaled to an 8B configuration for efficient deployment. Causal decoder-only transformer is used in dual-mode operation: Reasoning Mode emits structured explanations while Fast Mode performs classification only.
AprielGuard is available in both reasoning and non-reasoning modes, enabling explainable classification when needed and low-latency classification for production pipelines.
The model has been evaluated across public safety benchmarks, public adversarial benchmarks, internal agentic workflow benchmarks, internal long-context use case benchmarks (up to 32k), and multilingual evaluation (8 languages). The results show that AprielGuard performs exceptionally well in detecting safety risks and adversarial attacks, with high precision, recall, and F1-score. However, the model may still exhibit vulnerability to complex or unseen attack strategies.
In addition, the model has been tested for its multilingual capabilities by translating instances from the English Safety and Adversarial benchmarks into eight non-English languages: French, French-Canadian, German, Japanese, Dutch, Spanish, Portuguese-Brazilian, and Italian. The results show that AprielGuard performs reasonably well across multiple languages, although thorough testing and calibration are strongly recommended before deploying the model for production use in non-English settings.
Related Information:
https://www.digitaleventhorizon.com/articles/ArielGuard-A-Revolutionary-AI-Model-for-Safeguarding-LLM-Systems-from-Adversarial-Attacks-deh.shtml
https://huggingface.co/blog/ServiceNow-AI/aprielguard
Published: Tue Dec 23 19:55:27 2025 by llama3.2 3B Q4_K_M
