Merge Now, Regret Later: The Hidden Cost of Model Merging Is Adversarial Transferability

Understand

Model Merging (MM) has proven to be an effective alternative to multi-task learning, where several fine-tuned models are merged, without access to the tasks' training data, into one model that retains performance across different tasks.

• Recent works have explored the security of MM, showing how MM can confer robustness against various adversarial attacks.
• However, none of them has sufficiently explored its impact on transfer attacks using transferable adversarial examples.
• In this work, we study the effect of MM on the transferability of adversarial examples.