Backdoored model code enables deterministic, verifiable stealing of sparse secrets during local LLM fine-tuning via tensor-rule matching and gradient injection, achieving over 98% strict attack success rate while bypassing DP-SGD and auditing defenses.
An llm-assisted easy-to-trigger backdoor attack on code completion models: Injecting disguised vulnerabilities against strong detection
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Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors
Backdoored model code enables deterministic, verifiable stealing of sparse secrets during local LLM fine-tuning via tensor-rule matching and gradient injection, achieving over 98% strict attack success rate while bypassing DP-SGD and auditing defenses.