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This study reveals that backdoors can propagate across all tasks in speech language models, with their persistence heavily dependent on specific pipeline components. Crucially, the research challenges the assumption that poisoned samples are separable from benign ones in shared embeddings, indicating that standard filtering defenses may be ineffective. Consequently, security strategies must evolve to treat multimodal pipelines as intricate systems with unique vulnerabilities rather than simple extensions of unimodal models.
Where Do Backdoors Live? A Component-Level Analysis of Backdoor Propagation in Speech Language Models
arXiv:2510.01157v4 Announce Type: replace Abstract: Speech language models (SLMs) are systems of systems: independent components that unite to achieve a common goal. Despite their heterogeneous nature, SLMs are often studied end-to-end; how information flows through the pipeline remains obscure. We investigate this question through the lens of backdoor attacks. We first establish that backdoors can propagate through the SLM, leaving all tasks highly vulnerable. From this, we design a component analysis to discover the role each component takes in backdoor learning. We find that backdoor persistence or erasure is highly dependent on the targeted component. Beyond propagation, we examine how backdoors are encoded in shared multitask embeddings, showing that poisoned samples are not directly separable from benign ones, challenging a common separability assumption used in filtering defenses. Our findings emphasize the need to treat multimodal pipelines as intricate systems with unique vulnerabilities, not solely extensions of unimodal ones.
Abstract:Speech language models (SLMs) are systems of systems: independent components that unite to achieve a common goal. Despite their heterogeneous nature, SLMs are often studied end-to-end; how information flows through the pipeline remains obscure. We investigate this question through the lens of backdoor attacks. We first establish that backdoors can propagate through the SLM, leaving all tasks highly vulnerable. From this, we design a component analysis to discover the role each component takes in backdoor learning. We find that backdoor persistence or erasure is highly dependent on the targeted component. Beyond propagation, we examine how backdoors are encoded in shared multitask embeddings, showing that poisoned samples are not directly separable from benign ones, challenging a common separability assumption used in filtering defenses. Our findings emphasize the need to treat multimodal pipelines as intricate systems with unique vulnerabilities, not solely extensions of unimodal ones.
Computation and Language (cs.CL); Cryptography and Security (cs.CR); Sound (cs.SD)
arXiv:2510.01157 [cs.CL]
(or arXiv:2510.01157v4 [cs.CL] for this version)
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