Papers by Yeonjea Kim
Incomplete Prompt Jailbreaks in Large Language Models (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly released as open-weight models with safeguards against harmful requests. |
| Approach: | They formalize incomplete prompt jailbreaks as incomplete prompts elicit harmful continuations . they identify two functional neurons that delay refusal until sentence termination . |
| Outcome: | The proposed model fails to generalize across content domains and attractor types . the proposed model can be used to perform more precise and robust IPJ defenses . |
When Format Changes Meaning: Investigating Semantic Inconsistency of Large Language Models (2025.findings-emnlp)
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| Challenge: | Large language models are vulnerable to semantic inconsistency, a study finds . minor formatting variations result in divergent predictions for semantically equivalent inputs. |
| Approach: | They evaluate LLMs for semantic inconsistency and find they remain vulnerable . they propose to use mechanistic analysis to develop models that improve their reliability . |
| Outcome: | The proposed model is vulnerable to semantic inconsistency, the authors show . their model is brittle even in state-of-the-art models, they say . |