In the realm of robotic-assisted surgeries, like laparoscopic cholecystectomy, the integration of deep learning (DL) models marks a significant advancement in achieving surgical precision and minimal invasiveness, which in turn, elevates patient outcomes and reduces recovery times. However, the vulnerability of these DL models to adversarial attacks introduces a critical risk, emphasizing the need for enhanced model robustness. Our study addresses this challenge by proposing a comprehensive framework that not only fortifies surgical action recognition models against adversarial threats through adversarial training and pre-processing strategies but also incorporates uncertainty estimation to enhance prediction confidence and trustworthiness. Our framework demonstrates superior resilience against a wide spectrum of adversarial attacks and showcases improved reliability in surgical tool detection under adversarial conditions. It achieves an improvement from 8% to 23.58% in terms of triplet (instrument, verb, triplet) predictions. These contributions significantly enhance the security and reliability of deep learning applications in the critical domain of robotic surgery, offering an approach that safeguards advanced surgical technologies against malicious threats, thereby promising enhanced patient care and surgical precision. Code is available at https://github.com/umair1221/guardian .