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This paper proposes DRL-CLBA, a clean label backdoor attack for speech classification using DDPG reinforcement learning and deep audio steganography, achieving high attack success rates and bypassing several defenses, exposing vulnerabilities in speech-controlled systems.
This paper characterizes backdoors in LoRA adapters that activate at the token feature level, and proposes behavioral and weight-level detection methods. The backdoor generalizes across related token patterns but not structurally identical ones, and detection methods show strong separation.
This paper identifies a circuit underlying a language-switching backdoor in an 8B-parameter language model, where a three-word Latin trigger redirects English output to French via attention heads and orthogonal latent subspaces, with the final layer MLP converting the latent signal to French logits.
This paper introduces Paraesthesia, a dynamic backdoor attack on LLMs that uses emotional style as a stealthy trigger during fine-tuning, achieving high success rates while maintaining model utility.