Papers by Jaehan Kim
Obliviate: Neutralizing Task-agnostic Backdoors within the Parameter-efficient Fine-tuning Paradigm (2025.findings-naacl)
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| Challenge: | Parameter-efficient fine-tuning (PEFT) is a key training strategy for large language models, but its reliance on fewer trainable parameters poses security risks, such as task-agnostic backdoors. |
| Approach: | They propose a method that amplifies benign neurons within PEFT layers and penalizes the influence of trigger tokens. |
| Outcome: | The proposed method reduces the success rate of state-of-the-art task-agnostic backdoors by 83.6%. |
Claim-Guided Textual Backdoor Attack for Practical Applications (2025.findings-naacl)
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| Challenge: | a novel backdoor attack is based on textual claims to trick models into misbehaving on targeted claims. |
| Approach: | a new backdoor attack is designed to trick models into misbehaving on targeted claims . the code and data will be available at https://github.com/minkyoo9/CGBA . |
| Outcome: | a new backdoor attack exploits the power of textual claims to trick models into misbehaving on claims without affecting their performance on clean data. |