Plato's Form: Toward Backdoor Defense-as-a-Service for LLMs with Prototype Representations
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908818095472640 |
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| author | Chen, Chen Sun, Yuchen Gao, Jiaxin Jia, Yanwen Gong, Xueluan Wang, Qian Lam, Kwok-Yan |
| author_facet | Chen, Chen Sun, Yuchen Gao, Jiaxin Jia, Yanwen Gong, Xueluan Wang, Qian Lam, Kwok-Yan |
| contents | Large language models (LLMs) are increasingly deployed in security-sensitive applications, yet remain vulnerable to backdoor attacks. However, existing backdoor defenses are difficult to operationalize for Backdoor Defense-as-a-Service (BDaaS), as they require unrealistic side information (e.g., downstream clean data, known triggers/targets, or task domain specifics), and lack reusable, scalable purification across diverse backdoored models. In this paper, we present PROTOPURIFY, a backdoor purification framework via parameter edits under minimal assumptions. PROTOPURIFY first builds a backdoor vector pool from clean and backdoored model pairs, aggregates vectors into candidate prototypes, and selects the most aligned candidate for the target model via similarity matching. PROTOPURIFY then identifies a boundary layer through layer-wise prototype alignment and performs targeted purification by suppressing prototype-aligned components in the affected layers, achieving fine-grained mitigation with minimal impact on benign utility. Designed as a BDaaS-ready primitive, PROTOPURIFY supports reusability, customizability, interpretability, and runtime efficiency. Experiments across various LLMs on both classification and generation tasks show that PROTOPURIFY consistently outperforms 6 representative defenses against 6 diverse attacks, including single-trigger, multi-trigger, and triggerless backdoor settings. PROTOPURIFY reduces ASR to below 10%, and even as low as 1.6% in some cases, while incurring less than a 3% drop in clean utility. PROTOPURIFY further demonstrates robustness against adaptive backdoor variants and stability on non-backdoored models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06887 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Plato's Form: Toward Backdoor Defense-as-a-Service for LLMs with Prototype Representations Chen, Chen Sun, Yuchen Gao, Jiaxin Jia, Yanwen Gong, Xueluan Wang, Qian Lam, Kwok-Yan Cryptography and Security Large language models (LLMs) are increasingly deployed in security-sensitive applications, yet remain vulnerable to backdoor attacks. However, existing backdoor defenses are difficult to operationalize for Backdoor Defense-as-a-Service (BDaaS), as they require unrealistic side information (e.g., downstream clean data, known triggers/targets, or task domain specifics), and lack reusable, scalable purification across diverse backdoored models. In this paper, we present PROTOPURIFY, a backdoor purification framework via parameter edits under minimal assumptions. PROTOPURIFY first builds a backdoor vector pool from clean and backdoored model pairs, aggregates vectors into candidate prototypes, and selects the most aligned candidate for the target model via similarity matching. PROTOPURIFY then identifies a boundary layer through layer-wise prototype alignment and performs targeted purification by suppressing prototype-aligned components in the affected layers, achieving fine-grained mitigation with minimal impact on benign utility. Designed as a BDaaS-ready primitive, PROTOPURIFY supports reusability, customizability, interpretability, and runtime efficiency. Experiments across various LLMs on both classification and generation tasks show that PROTOPURIFY consistently outperforms 6 representative defenses against 6 diverse attacks, including single-trigger, multi-trigger, and triggerless backdoor settings. PROTOPURIFY reduces ASR to below 10%, and even as low as 1.6% in some cases, while incurring less than a 3% drop in clean utility. PROTOPURIFY further demonstrates robustness against adaptive backdoor variants and stability on non-backdoored models. |
| title | Plato's Form: Toward Backdoor Defense-as-a-Service for LLMs with Prototype Representations |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2602.06887 |