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@@ -170,7 +170,6 @@ Methods for defending against backdoor attacks include:
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| 2025 | FSE |[Eliminating Backdoors in Neural Code Models for Secure Code Understanding.](./papers_en/2025-FSE-EliBadCode.pdf) 🚩 |[](https://github.com/wssun/EliBadCode)||
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| 2025 | ICSE |[Show Me Your Code! Kill Code Poisoning: A Lightweight Method Based on Code Naturalness.](./papers_en/2025-ICSE-KILLBADCODE.pdf) 🚩 |[](https://github.com/wssun/KillBadCode)||
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| 2024 | TOSEM |[Poison Attack and Poison Detection on Deep Source Code Processing Models.](./papers_en/2024-TOSEM-Poison_Attack_and_Poison_Detection_on_Deep_Source_Code_Processing_Models.pdf)|||
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| 2024 | CoRR |[Eliminating Backdoors in Neural Code Models via Trigger Inversion.](./papers_en/2024-CoRR-Eliminating_Backdoors_via_Trigger_Inversion.pdf) 🚩 |||
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| 2024 | CoRR |[Defending Code Language Models against Backdoor Attacks with Deceptive Cross-Entropy Loss.](./papers_en/2024-CoRR-DeCE.pdf)|[](https://github.com/NTDXYG/DeCE)||
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| 2023 | CoRR |[Occlusion-based Detection of Trojan-triggering Inputs in Large Language Models of Code.](./papers_en/2023-CoRR-Occlusion-based_Detection_of_Trojan-triggering_Inputs.pdf)|||
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| 2022 | ICPR |[Backdoors in Neural Models of Source Code.](./papers_en/2022-ICPR-Backdoors_in_Neural_Models_of_Source_Code.pdf)|[](https://github.com/goutham7r/backdoors-for-code)||
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