Challenges and Remedies of Domain-Specific Classifiers as LLM Guardrails: Self-Harm as a Case Study (2025)

Abstract: Context:Despite the impressive capabilities of Large Language Models (LLMs), they pose significant risks in many domains and therefore require guardrails throughout the lifecycle.Problem:Many such guardrails are trained as classifiers with domain-specific human text datasets obtained from sources such as social media and they achieve reasonable performance against closed-domain benchmarks. When deployed in the real world, however, the guardrails have to deal with machine text in an open domain, and their performance deteriorates drastically, rendering them almost unusable due to a high level of false refusal.Solution:In this paper, using a self-harm detector as an example, we demonstrate the specific challenges facing guardrail deployment due to the data drift between training and production environments. More specifically, we formed two hypotheses about the potential causes, i.e. closed vs. open domain, human vs. LLM-generated text, and conducted five experiments to explore various potential remedies, including their respective advantages and disadvantages.Evaluation:While focusing on one example, our experience and knowledge of LLM guardrails give us great confidence that our work contributes to a more thorough understanding of guardrail deployment and can be generalized as a methodology to build more robust domain-specific guardrails in real-world applications.

## introduction large language models (llms) have transformed natural language processing (nlp), enabling applications in customer service, content creation, and more. models like gpt-4 @xcite and palm 2 @xcite demonstrate remarkable capabilities in generating human-like text. however, their adoption raises pressing ethical and safety concerns, particularly the risk of producing harmful content such as text promoting self-harm or violence @xcite . addressing these risks is critical to ensuring the responsible and safe deployment of llms in real-world settings @xcite @xcite . a major challenge in mitigating harmful content lies in the limitations of current detection models. these models, often trained on human-text datasets (e.g., social media posts), excel in their specific domains but struggle to generalize to llm @xcite . the statistical differences between human-text and machine-text, coupled with the lack of contextual understanding in llms, result in significant accuracy drift when detectors are applied to open-domain llm outputs @xcite . this drift leads to unreliable performance, with increased false positives and false negatives in detecting harmful outputs. moreover, the scarcity of high-quality synthetic datasets representing harmful llm outputs exacerbates the problem @xcite @xcite . while llms are designed to suppress overtly harmful content, subtle forms of harm may still emerge, particularly in sensitive categories like self-harm. existing training datasets, largely derived from human-text sources, fail to capture the nuances of llm-generated-text, creating a critical gap in detection capabilities. this paper tackles these challenges by exploring guardrail deployment in llm environments. using self-harm detection as a case study, we analyze the impact of data drift between training on human-text and real-world llm outputs. in this study, we define self-harm as any deliberate behavior or intent that causes physical harm to one-self. our approach involves curating representative llm-generated-text and integrating them into the training pipeline to enhance detector robustness. our contributions are as follows: by addressing these challenges, our work provides a pathway to safer and more reliable llm deployment in high-risk domains. the insights gained have broader implications for sectors such as healthcare, education, and customer service, where user safety and content integrity are paramount. ## literature review the detection of harmful content in social media posts and online forums has traditionally relied on rule-based systems and keyword matching. while effective for simple cases, these methods often fail to capture harmful content's nuanced and contextdependent nature. advances in machine learning (ml) and nlp have significantly enhanced detection capabilities, with supervised learning models trained on annotated datasets and deep learning techniques, such as neural networks, achieving state-of-the-art results @xcite @xcite . however, these models remain limited by their dependence on rigid rules or narrow training data, which can lead to false positives and missed detections in diverse and dynamic contexts @xcite . applying detectors trained on human-text to llm-generated-text introduces additional challenges. llms generate text statistically, often lacking the emotional and contextual cues inherent in human communication @xcite . this fundamental difference hampers the generalizability of traditional detectors, resulting in degraded performance when analyzing llm outputs, which span a broad range of topics and styles. for instance, detectors trained on suicide prevention forum data may perform well in domain-specific contexts but struggle to handle the syntactically diverse and semantically subtle outputs of llms @xcite . recent advancements have begun addressing these challenges. fine-tuning llms on curated datasets that include examples of harmful content has shown promise @xcite @xcite . tools like perspective api have improved the detection of toxic language but remain tailored to humangenerated text, which differs significantly from llm-generated-text @xcite . several moderation-based approaches specifically target llm-generated-text. for example, systems like openai content moderation @xcite , shieldgemma @xcite , harm-bench @xcite , llama guard @xcite , and wildguard @xcite fine-tune models to classify and moderate both input prompts and output responses. llama guard, for instance, is an instruction-tuned llama2-7b model designed to detect risky categories such as self-harm. however, self-harm examples constitute a small fraction of its fine-tuning dataset (89/10.2k prompts, 96/10.2k responses), limiting its robustness in this specific domain @xcite . moreover, most moderation solutions rely on large, fixed-size models that are computationally expensive and may not align with the specific requirements of diverse deployment scenarios @xcite . self-regulating mechanisms within llms leverage reinforcement learning with human feedback (rlhf) to iteratively reduce harmful content generation @xcite . complementary approaches, such as uncertainty quantification (uq), identify outputs with high uncertainty, flagging potentially harmful content for further review @xcite . these techniques enhance reliability by addressing edge cases where traditional methods falter. despite these advancements, significant gaps persist. balancing domain-specific accuracy with generalized robustness remains a key challenge, particularly when selecting representative training data from the vast and diverse landscape of @xcite . overcoming these challenges requires innovation in data curation, model fine-tuning, and evaluation frameworks to ensure llms are deployed safely and effectively across industries without compromising user trust or content quality. ## deployment the self-harm detector is integrated into a customizable llm guardrail framework called oneshield, which consists of model-agnostic methods designed to mitigate risks associated with llms. the oneshield framework is built on a collection of containerized microservices, including: the self-harm detector focuses on textual data, including llm user prompts and generated responses, and categorizes the content into two labels: self-harm or non-self-harm. these labels are passed to the policy manager, which then determines the appropriate action. oneshield is deployed as an internal platform for managing models and data, offering access to the full lifecycle of ai model development. it provides tools for handling diverse datasets, models, and llm chatbot interactions while incorporating essential features such as transparency, metadata tracking, and risk analysis. in cases where users raise self-harm-related queries, oneshield not only safeguards against harmful content but also delivers meaningful and supportive information, ensuring a compassionate and constructive approach to addressing sensitive topics, see appendix a/figure 2 . ## conclusion and future work this study examined the challenges and solutions for deploying domain-specific classifiers as llm guardrails, using self-harm detection as a case study. through five targeted experiments, we addressed accuracy drift during deployment and identified critical differences between human-text and llm-generated-text, emphasizing the need for cu-rated llm-generated-text to expand training domains and balanced benchmarks for robust evaluation. our findings provide key insights into improving the reliability and adaptability of llm guardrails in high-stakes applications. future work will focus on adaptive learning techniques to dynamically align classifiers with evolving llm-generated-text while maintaining performance on human-text. we also aim to design benchmarks that capture human and llm text nuances and address dataset imbalance. expanding these methods to other sensitive domains will enhance the scalability and generalizability of llm guardrails across diverse applications.