BadSKP: Backdoor Attacks on Knowledge Graph-Enhanced LLMs with Soft Prompts
Recent knowledge graph (KG)-enhanced large language models (LLMs) move beyond purely textual knowledge augmentation by encoding retrieved subgraphs into continuous soft prompts via graph neural networks, introducing a graph-conditioned channel that operates alongside the standard text interface. However, existing backdoor attacks are largely designed for the textual channel, and their effectiveness against this dual-channel architecture remains unclear. We show that this architecture creates a robustness gap: text-channel backdoor attacks that readily compromise textual KG prompting systems become largely ineffective against soft-prompt-based counterparts. We interpret this gap through se...
| Authors | Xiaoting Lyu, Yufei Han, Hangwei Qian et al. |
|---|---|
| Primary category | cs.AI |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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