This paper presents a novel self-healing approach for RESTful services, leveraging the capabilities of large language models (LLMs) to generate source code that implement fine-grained mitigations. The proposed solution introduces 18 healing operators tailored for RESTful services, accommodating both grey-box and black-box perspectives. These operators implement a dual-mitigation strategy. The first mitigation employs encapsulation techniques, enabling dynamic service adaptation by generating supplementary source code with- out modifying the original implementation. If the primary mitigation fails, a fallback mitigation is applied to maintain service continuity. We investigate the potential of LLMs to perform the first mitigation of these healing operators by means of chains of prompts we specifically designed for these tasks. Furthermore, we introduce a novel metric that integrates test-passing correctness and LLM confidence, providing a rigorous evaluation framework for the effectiveness of the mitigations performed by LLMs. Preliminary experiments using four healing operators on 15 RESTful services with various and multiple vulnerabilities demonstrate the approach feasibility and adaptability across both grey-box and black-box perspectives.