Saraga Sakthidharan

Aiman Almasoud, Antony Anju, Marco Arazzi et al.

LLM-as-a-Judge (LaaJ) is a novel paradigm in which powerful language models are used to assess the quality, safety, or correctness of generated outputs. While this paradigm has significantly improved the scalability and efficiency of evaluation processes, it also introduces novel security risks and reliability concerns that remain largely unexplored. In particular, LLM-based judges can become both targets of adversarial manipulation and instruments through which attacks are conducted, potentially compromising the trustworthiness of evaluation pipelines. In this paper, we present the first Systematization of Knowledge (SoK) focusing on the security aspects of LLM-as-a-Judge systems. We perform a comprehensive literature review across major academic databases, analyzing 863 works and selecting 45 relevant studies published between 2020 and 2026. Based on this study, we propose a taxonomy that organizes recent research according to the role played by LLM-as-a-Judge in the security landscape, distinguishing between attacks targeting LaaJ systems, attacks performed through LaaJ, defenses leveraging LaaJ for security purposes, and applications where LaaJ is used as an evaluation strategy in security-related domains. We further provide a comparative analysis of existing approaches, highlighting current limitations, emerging threats, and open research challenges. Our findings reveal significant vulnerabilities in LLM-based evaluation frameworks, as well as promising directions for improving their robustness and reliability. Finally, we outline key research opportunities that can guide the development of more secure and trustworthy LLM-as-a-Judge systems.

Marco Arazzi, Vignesh Kumar Kembu, Antonino Nocera et al.

The open-source ecosystem has accelerated the democratization of Large Language Models (LLMs) through the public distribution of specialized Low-Rank Adaptation (LoRA) modules. However, integrating these third-party adapters often induces catastrophic forgetting of the base model's foundational safety alignment. Restoring these guardrails via fine-tuning on safety data introduces an opposing failure mode: the severe degradation of the specialized domain knowledge the adapter was originally designed to provide. To overcome this zero-resource challenge, we propose Neural Weight Translation (NeWTral), a framework that directly maps unsafe, domain-specific adapters onto a safe alignment manifold while rigorously preserving their core expertise. NeWTral operates as a non-linear translation module pre-trained on a diverse corpus of unsafe-to-safe adapter pairs. By executing this mapping entirely within the parameter space, NeWTral utilizes an adaptive Mixture of Experts (MoE) routing strategy to autonomously blend high-fidelity surgical translators and aggressive alignment experts. We evaluate our framework across four architectural families (Llama, Mistral, Qwen, and Gemma) at scales up to 72B parameters across eight diverse scientific and professional domains. Our results demonstrate that the MoE variant achieves a radical reduction in the average Attack Success Rate (ASR), dropping from 70% in unsafe experts to just 13%, while maintaining an exceptional 90\% average knowledge fidelity. Much like the crowdsourced adapters it remedies, the NeWTral module is designed as a standalone, downloadable asset that allows practitioners to restore safety alignment instantly without requiring access to original training data or hardware-intensive retraining.