Guardrailed LLMs: Red Teaming and Safety Mitigations

Abstract

Large Language Models (LLMs) have made impressive strides in natural language generation, but ensuring their safe deployment through effective guardrails remains a paramount challenge. This paper examines red teaming and safety mitigation approaches developed before 2022 to fortify LLMs against harmful behaviors. Key red teaming techniques include adversarial prompt testing—such as prompt injection, jailbreaks, confidentiality stress tests, and bias probes—that reveal vulnerabilities in model outputs and filter systems MacgenceTechTarget. Automated red teaming using LMs to generate adversarial test cases has also been introduced, offering scalable methods to probe harmful responses arXiv. These approaches help uncover content issues like hate speech, misinformation, privacy leaks, and unsafe instructions. Safety mitigations highlighted include rejection sampling, reinforcement learning from human feedback (RLHF), and multi-stage defense pipelines involving models explicitly tasked with identifying and blocking harmful content arXivToloka. Although quantitative data is limited, structured red teaming frameworks have influenced guardrail improvements— such as prompt injection filters, adversarial training, and continuous monitoring systems—demonstrating their practical impact. This paper outlines a methodology combining adversarial testing, automated prompt generation, policyinformed defenses, and ongoing safety evaluation. Advantages include comprehensive coverage of attack vectors, scalability through automated testing, and tangible improvements to safety mechanisms. Disadvantages involve high resource demands, evolving threat landscapes, challenges in interpreting black-box models, and the trade-offs between safety and model utility. We conclude that robust guardrails for LLMs require iterative red teaming, layered defense design, and continuous feedback loops. Future directions include formal safety verification, explainable mitigation strategies, and standardized red teaming benchmarks.