Backdoor4Good: Benchmarking Beneficial Uses of Backdoors in LLMs
Backdoor mechanisms have traditionally been studied as security threats that compromise the integrity of machine learning models. However, the same mechanism -- the conditional activation of specific behaviors through input triggers -- can also serve as a controllable and auditable interface for trustworthy model behavior. In this work, we present \textbf{Backdoor4Good (B4G)}, a unified benchmark and framework for \textit{beneficial backdoor} applications in large language models (LLMs). Unlike conventional backdoor studies focused on attacks and defenses, B4G repurposes backdoor conditioning for Beneficial Tasks that enhance safety, controllability, and accountability. It formalizes bene...
| Authors | Yige Li, Wei Zhao, Zhe Li et al. |
|---|---|
| Primary category | cs.CR |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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