Inoculation Adapters: Improved Selective Generalization of Capabilities with Fewer Surprising Backdoors

2026-06-29

AI Summary

This work addresses the challenge of suppressing undesirable behaviors—such as sudden alignment failures—learned during model training while preserving desired capabilities and avoiding unintended backdoors. The authors propose the Inoculation Adapter (IA) method, which first trains a LoRA adapter specialized in capturing undesirable behaviors, then freezes this adapter to guide the training of the main task adapter. Only the main adapter is deployed, thereby reducing the optimization pressure that leads the model to acquire undesirable capabilities. Unlike prompt-based inoculation, IA effectively mitigates behaviors that are difficult to elicit via prompting and substantially diminishes the risk of accidental backdoors. Experiments across six model families demonstrate that IA achieves more selective capability suppression while enhancing both safety and general applicability.

Abstract

Inoculation prompting is a selective generalization technique used against Emergent Misalignment. We introduce inoculation adapters (IA), which similarly diminish the optimization pressure to learn undesired traits by strengthening the trait at train time. Inoculation adapters are LoRAs that are trained and used over three steps: 1) trained on undesired traits; 2) attached frozen while a separate task adapter is trained on data exhibiting both desired and undesired traits; 3) at deployment, the IA is discarded, and only the task adapter is kept. We show across six model families and several undesired traits including emergent misalignment, that inoculation adapters are more effective at suppressing undesired traits, while avoiding two drawbacks of inoculation prompting: inoculation adapters can suppress capabilities and traits that cannot be reliably elicited by a prompt, and they introduce fewer surprising backdoors than inoculation prompting under our probes. While undesired traits are better suppressed by inoculation adapters, the retention of desired traits is not consistently improved upon inoculation prompting and remains a challenge for both techniques.

Problem

• selective generalization
• emergent misalignment
• undesired traits
• backdoors
• capability suppression

Innovation

• Inoculation Adapters
• Selective Generalization
• LoRA
• Emergent Misalignment
• Backdoor Mitigation