SlotGCG: Exploiting the Positional Vulnerability in LLMs for Jailbreak Attacks
As large language models (LLMs) are widely deployed, identifying their vulnerability through jailbreak attacks becomes increasingly critical. Optimization-based attacks like Greedy Coordinate Gradient (GCG) have focused on inserting adversarial tokens to the end of prompts. However, GCG restricts adversarial tokens to a fixed insertion point (typically the prompt suffix), leaving the effect of inserting tokens at other positions unexplored. In this paper, we empirically investigate \emph{slots}, i.e., candidate positions within a prompt where tokens can be inserted. We find that vulnerability to jailbreaking is highly related to the selection of the \emph{slots}. Based on these findings, ...
| Authors | Seungwon Jeong, Jiwoo Jeong, Hyeonjin Kim et al. |
|---|---|
| Primary category | cs.CR |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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