Stealthy Backdoor Attacks against LLMs Based on Natural Style Triggers
The growing application of large language models (LLMs) in safety-critical domains has raised urgent concerns about their security. Many recent studies have demonstrated the feasibility of backdoor attacks against LLMs. However, existing methods suffer from three key shortcomings: explicit trigger patterns that compromise naturalness, unreliable injection of attacker-specified payloads in long-form generation, and incompletely specified threat models that obscure how backdoors are delivered and activated in practice. To address these gaps, we present BadStyle, a complete backdoor attack framework and pipeline. BadStyle leverages an LLM as a poisoned sample generator to construct natural a...
| Authors | Jiali Wei, Ming Fan, Guoheng Sun et al. |
|---|---|
| Primary category | cs.CR |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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