Mind the Trojan Horse: Image Prompt Adapter Enabling Scalable and Deceptive Jailbreaking
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912316271886336 |
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| author | Chen, Junxi Dong, Junhao Xie, Xiaohua |
| author_facet | Chen, Junxi Dong, Junhao Xie, Xiaohua |
| contents | Recently, the Image Prompt Adapter (IP-Adapter) has been increasingly integrated into text-to-image diffusion models (T2I-DMs) to improve controllability. However, in this paper, we reveal that T2I-DMs equipped with the IP-Adapter (T2I-IP-DMs) enable a new jailbreak attack named the hijacking attack. We demonstrate that, by uploading imperceptible image-space adversarial examples (AEs), the adversary can hijack massive benign users to jailbreak an Image Generation Service (IGS) driven by T2I-IP-DMs and mislead the public to discredit the service provider. Worse still, the IP-Adapter's dependency on open-source image encoders reduces the knowledge required to craft AEs. Extensive experiments verify the technical feasibility of the hijacking attack. In light of the revealed threat, we investigate several existing defenses and explore combining the IP-Adapter with adversarially trained models to overcome existing defenses' limitations. Our code is available at https://github.com/fhdnskfbeuv/attackIPA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05838 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mind the Trojan Horse: Image Prompt Adapter Enabling Scalable and Deceptive Jailbreaking Chen, Junxi Dong, Junhao Xie, Xiaohua Computer Vision and Pattern Recognition Artificial Intelligence Cryptography and Security Recently, the Image Prompt Adapter (IP-Adapter) has been increasingly integrated into text-to-image diffusion models (T2I-DMs) to improve controllability. However, in this paper, we reveal that T2I-DMs equipped with the IP-Adapter (T2I-IP-DMs) enable a new jailbreak attack named the hijacking attack. We demonstrate that, by uploading imperceptible image-space adversarial examples (AEs), the adversary can hijack massive benign users to jailbreak an Image Generation Service (IGS) driven by T2I-IP-DMs and mislead the public to discredit the service provider. Worse still, the IP-Adapter's dependency on open-source image encoders reduces the knowledge required to craft AEs. Extensive experiments verify the technical feasibility of the hijacking attack. In light of the revealed threat, we investigate several existing defenses and explore combining the IP-Adapter with adversarially trained models to overcome existing defenses' limitations. Our code is available at https://github.com/fhdnskfbeuv/attackIPA. |
| title | Mind the Trojan Horse: Image Prompt Adapter Enabling Scalable and Deceptive Jailbreaking |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2504.05838 |