LLM-based guardrails are effective against indirect prompt injection (IPI), but their performance depends heavily on model capability: as the guardrail model becomes smaller or weaker, detection degrades substantially. This raises a natural question: can we retain strong IPI defense while relying only on a small LLM? T...
Yin-Xiang Wang, Ze-Hua Ma, Ke-Jiang Chen et al.· 0 citations
Indirect Prompt Injection (IPI) poses a critical threat to LLM agents. Existing defenses largely focus on whether an injection induces a new tool invocation or unintended action. In this paper, we study a different attack surface, parameter poisoning, where the attacker preserves the intended action but manipulates one...
Yao Wang, Ze-Hua Ma, Wei-Ming Zhang et al.· 0 citations
Invisible image watermark removal has received growing attention. Despite substantial progress, existing attacks face a tension between practicality and specificity. Attacks exploiting detector outputs, decoder responses, or paired images can be tailored to the watermark decision boundary, but require information rarel...
Jun Yao, Chao Wang, Yu-Peng Qiu et al.· 0 citations
As LLM agents are increasingly deployed as commercial services, protecting proprietary orchestration logic and tool-use policies is important. We consider agent repackaging: an adversary integrates a protected agent into its own application via API and presents it under its own identity. It may modify parts of executio...
Hao-Yang Zou, Yao-Zi Wang, Jun Yao et al.· 0 citations
FARI is proposed, a one-step inversion framework paired with lightweight adversarial LoRA fine-tuning of the denoiser for watermark extraction that surpasses 50-step DDIM inversion on watermark-verification robustness while dramatically reducing inference time.
Jindong Yang, Han Fang, Wei-Ming Zhang et al.· arXiv.org· 1 citation
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