In the paper Merging Improves Self-Critique Against Jailbreak Attacks (PDF), the authors address how to make language models more resilient against adversarial jailbreak attempts like prompt manipulations intended to bypass a model’s safeguards. They present a method that merges the original model with a specialized critic model, enhancing its ability to self-evaluate and reject unsafe outputs. Combined with fine tuning on sanitized, synthetic datasets, this approach significantly reduces jailbreak success rates while maintaining performance on normal tasks.
Relation to Neon AI:
Neon AI’s BrainForge process aligns with this emphasis on modular resilience. By enabling efficient fine-tuning of even small language models, BrainForge makes it feasible to incorporate specialized components such as self-critique modules into deployed agents. This supports the development of safe, adaptable agentic AI systems in ways that are accessible to small businesses and individual developers, echoing the paper’s focus on scalable, practical defenses.
Read the full text here.