Inoculation Adapters Improve Upon Inoculation Prompting

Researchers introduced inoculation adapters (IA), a method using LoRA adapters to suppress undesired AI traits while preserving desired ones, improving upon inoculation prompting. IA achieves stronger suppression of traits like emergent misalignment and is effective against hard-to-elicit traits, though results are setup-dependent. Variants GIA and CGIA improve retention of desired traits but show tradeoffs in robustness.

Selective generalization. Training can teach desired and undesired traits at once. Selective generalization aims to preserve the generalization of desired traits while preventing the generalization of undesired ones. For example, RL environments may teach a model useful capabilities and a propensity to reward hack, and AI developers would like only the capabilities to generalize. Inoculation adapters IA work similarly to inoculation prompting IP , but instead of eliciting the undesired trait via prompting, we use a LoRA carrying the undesired trait during training. IA improves on IP in: Achieving stronger suppression of undesired traits e.g., emergent misalignment . Being effective against new capabilities and hard-to-elicit traits, unlike inoculation prompting. Creating substantially fewer surprising backdoors under our probes. A family of methods. On average, IA outperforms other baselines, such as preventative steering and concept-ablation fine-tuning, in suppressing undesired traits. In terms of retention of the desired trait, vanilla IA performs worse than these baselines. We introduce gated IA GIA and complementary-gated IA CGIA , which are in the same family of methods but achieve similar or better retention of the desired trait than the baselines. These variants jointly train gates to attenuate the inoculation adapter. Setup-dependence. Results are strongly setup-dependent, and the 95% confidence intervals for the normalized performance, aggregated across nine setups, remain wide. It is not possible to confidently conclude how much improvement the IA family of methods provides over preventative steering and inoculation prompting. Also, note that three out of the nine setups are designed to be hard for inoculation prompting. Effectiveness on hard-to-elicit traits. Inoculation prompting works best when the undesired traits can be elicited reliably through the inoculation prompt. New capabilities, hard-to-elicit traits, and non-instruct models are therefore challenging for inoculation prompting. Inoculation adapters only require that we can train a LoRA adapter that implements the undesired trait, and thus are better suited to suppress capabilities. Our nine selective generalization setups include one setup involving a new capability, one using a non-instruction-tuned model, and one involving a hard-to-elicit trait. Overall, three setups were designed to test settings in which IP was expected to struggle. Surprising backdoors.Dubiński et al. 2026 show that interventions that appear to remove emergent misalignment, including IP, can leave the misalignment hidden behind contextual triggers. We evaluate similar triggers and find that all variants of inoculation adapters are less prone to developing backdoors than inoculation prompting, but a tradeoff between desired trait retention and trigger-robustness remains: CGIA is less robust than vanilla IA. What is an inoculation adapter IA ? An IA is a LoRA adapter trained to carry the undesired trait. The implementation has three stages: IA-training. Train a LoRA on a corpus that demonstrates only the undesired trait. This corpus can be out-of-distribution relative to the target task, as is the case in our reported results. Task-training. Attach the IA, freeze it, then train a fresh task-LoRA on the actual data of interest, which displays both the desired and undesired traits. During that stage, gates are optionally trained to implement GIA or CGIA. Deployment. Detach the IA and remove the optional gates. Serve only the task adapter. Because the combined model already implements the undesired trait during task-training, the optimization pressure to learn it in the task-LoRA is reduced. This interpretation is consistent with the lower initial task-training loss when attaching the IA. Other relevant content in the preprint Detailed results across nine selective generalization setups, using five model families, and several undesired traits in addition to emergent misalignment from three sources. We assess how well IAs generalize when demonstrations of the undesired behavior are only available on prompts unrelated to the training tasks. We observe that the IA data distribution has little impact on the suppression of undesired traits. What inoculation adapters do not solve: The desired traits are still partially suppressed. The performance of the different techniques is strongly setup-dependent. Surprising backdoors triggered by regularities in the task domain may persist. Backdoors existing in the initial model are not removed. GIA and CGIA display more surprising backdoors than IA. Direct requests for undesired traits often yield stronger expression of the trait after IA-based task training than after SFT Safe . We also note that the impact of IA on RL training has not been studied, and that we expect IA to distort RL exploration similarly to other techniques applied at sampling time, such as IP. Related work Inoculation prompting IP . Tan et al. 2025 and Wichers et al. 2025 use an inoculation prompt to elicit the undesired trait during training, then remove the prompt at test time. Recontextualization. Azarbal et al. 2025a extend the idea of IP to RL: sample under prompts that discourage the misbehavior, train under prompts that encourage it. Preventative steering. Chen et al. 2025 add a fixed activation vector that steers toward the undesired trait during training. The vector is removed at test time. Concept-ablation fine-tuning CAFT . Casademunt et al. 2025 fine-tune while projecting out the residual-stream directions that represent the undesired concept. Selective generalization benchmark. Azarbal et al. 2025b benchmark several selective generalization techniques, including KL regularization.