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

AprielGuard is an 8 billion parameter model designed to enhance safety and adversarial robustness in modern Large Language Model (LLM) systems by detecting a wide range of safety risks and adversarial attacks. It tackles challenges posed by the evolution of LLMs into complex systems capable of multi-step reasoning and interactions, addressing issues such as multi-turn jailbreaks, prompt injections, and memory hijacking. AprielGuard classifies 16 safety risk categories, including toxicity, misinformation, and illegal activities, while also detecting adversarial attacks like prompt injection and multi-agent exploit sequences. It operates in both reasoning and non-reasoning modes for explainable or low-latency classification and is trained on a diverse synthetic dataset to improve robustness against real-world scenarios. The model is evaluated across various benchmarks, including multilingual and long-context use cases, demonstrating effectiveness in classifying safety risks and adversarial threats. Despite its capabilities, AprielGuard has limitations, such as potential vulnerabilities to unseen attack strategies and varying performance across different domains and languages, necessitating careful deployment considerations.