Just as Humans Need Vaccines, So Do Models: Model Immunization to Combat
  Falsehoods
Authors: name: Shaina Raza, name: Rizwan Qureshi, name: Marcelo Lotif, name: Aman Chadha, name: Deval Pandya, name: Christos Emmanouilidis

Generative AI models often learn and reproduce false information present in
their training corpora. This position paper argues that, analogous to
biological immunization, where controlled exposure to a weakened pathogen
builds immunity, AI models should be fine tuned on small, quarantined sets of
explicitly labeled falsehoods as a "vaccine" against misinformation. These
curated false examples are periodically injected during finetuning,
strengthening the model ability to recognize and reject misleading claims while
preserving accuracy on truthful inputs. An illustrative case study shows that
immunized models generate substantially less misinformation than baselines. To
our knowledge, this is the first training framework that treats fact checked
falsehoods themselves as a supervised vaccine, rather than relying on input
perturbations or generic human feedback signals, to harden models against
future misinformation. We also outline ethical safeguards and governance
controls to ensure the safe use of false data. Model immunization offers a
proactive paradigm for aligning AI systems with factuality.