AprielGuard
Jaykumar Kasundra, Anjaneya Praharaj, Sourabh Surana, Lakshmi Sirisha Chodisetty, Sourav Sharma, Abhigya Verma, Abhishek Bhardwaj, Debasish Kanhar, Aakash Bhagat, Khalil Slimi, Seganrasan Subramanian, Sathwik Tejaswi Madhusudhan, Ranga Prasad Chenna, Srinivas Sunkara

TL;DR
AprielGuard is an 8B parameter unified safeguard model for large language models that detects unsafe and adversarial behaviors across various scenarios, improving robustness and interpretability.
Contribution
It introduces a unified taxonomy and learning framework for safety and adversarial threats, trained on diverse data, with enhanced interpretability and superior performance.
Findings
Outperforms existing guardrails like Llama-Guard and Granite Guardian
Effective in multi-step and reasoning-intensive scenarios
Achieves strong detection of harmful content and adversarial manipulations
Abstract
Safeguarding large language models (LLMs) against unsafe or adversarial behavior is critical as they are increasingly deployed in conversational and agentic settings. Existing moderation tools often treat safety risks (e.g. toxicity, bias) and adversarial threats (e.g. prompt injections, jailbreaks) as separate problems, limiting their robustness and generalizability. We introduce AprielGuard, an 8B parameter safeguard model that unify these dimensions within a single taxonomy and learning framework. AprielGuard is trained on a diverse mix of open and synthetic data covering standalone prompts, multi-turn conversations, and agentic workflows, augmented with structured reasoning traces to improve interpretability. Across multiple public and proprietary benchmarks, AprielGuard achieves strong performance in detecting harmful content and adversarial manipulations, outperforming existing…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Hate Speech and Cyberbullying Detection · Topic Modeling
