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Author: goufei123

README.md

@@ -70,18 +70,21 @@ According to the document, security threats in CodeLMs are mainly classified int
 Backdoor attacks inject malicious behavior into the model during training, allowing the attacker to trigger it at inference time using specific triggers:
 - **Data poisoning attacks**: Slight changes to the training data that cause backdoor behavior.
 
-| Year | Conf./Jour.     | Paper                                                                                                                                                   | Code Repository                                                                                           | Reproduced Repository |    
-|------|-----------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|-----------------------|
+
+| Year | Conf./Jour.     | Paper                                                                                                                                                   | Code Repository                                                                                          | Reproduced Repository |    
+|------|-----------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|-----------------------|
 | 2024 | ISSTA           | [FDI: Attack Neural Code Generation Systems through User Feedback Channel.](./papers_en/2024-ISSTA-FDI.pdf)                                          | [![Octocat](./figures/github.svg)](https://github.com/v587su/FDI) |                       |
 | 2024 | TSE             | [Stealthy Backdoor Attack for Code Models.](./papers_en/2024-TSE-Stealthy_Backdoor_Attack_for_Code_Models.pdf)                                          | [![Octocat](./figures/github.svg)](https://github.com/yangzhou6666/adversarial-backdoor-for-code-models) |                       |
-| 2024 | SP              | [Trojanpuzzle: Covertly Poisoning Code-Suggestion Models.](./papers_en/2024-SP-TrojanPuzzle.pdf)                                                        | [![Octocat](./figures/github.svg)](https://github.com/microsoft/CodeGenerationPoisoning)        |                       |
-| 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)                                                               | [![Octocat](./figures/github.svg)](https://github.com/LJ2lijia/CodeDetector)                    |                       |
+| 2024 | SP              | [Trojanpuzzle: Covertly Poisoning Code-Suggestion Models.](./papers_en/2024-SP-TrojanPuzzle.pdf)                                                        | [![Octocat](./figures/github.svg)](https://github.com/microsoft/CodeGenerationPoisoning)       |                       |
+| 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)                                                               | [![Octocat](./figures/github.svg)](https://github.com/LJ2lijia/CodeDetector)                   |                       |
 | 2023 | ICPC            | [Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning Attacks.](./papers_en/2023-ICPC-Vulnerabilities_in_AI_Code_Generators.pdf)    | [![Octocat](./figures/github.svg)](https://github.com/dessertlab/Targeted-Data-Poisoning-Attacks) |                       |
-| 2023 | ACL             | [Backdooring Neural Code Search.](./papers_en/2023-ACL-Backdooring_Neural_Code_Search.pdf) 🚩                                                           | [![Octocat](./figures/github.svg)](https://github.com/wssun/BADCODE)                            |                       |
-| 2022 | ICPR            | [Backdoors in Neural Models of Source Code.](./papers_en/2022-ICPR-Backdoors_in_Neural_Models_of_Source_Code.pdf)                                       | [![Octocat](./figures/github.svg)](https://github.com/tech-srl/code2seq)                        |                       |
-| 2022 | FSE             | [You See What I Want You to See: Poisoning Vulnerabilities in Neural Code Search.](./papers_en/2022-FSE-You_See_What_I_Want_You_to_See.pdf)             | [![Octocat](./figures/github.svg)](https://github.com/CGCL-codes/naturalcc)                     |                       |
-| 2021 | USENIX Security | [Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers.](./papers_en/2021-USENIX-Explanation-Guided_Backdoor_Poisoning_Attacks.pdf) | [![Octocat](./figures/github.svg)](https://github.com/ClonedOne/MalwareBackdoors)               |                       |
-| 2021 | USENIX Security | [You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion. ](./papers_en/2021-USENIX-You_Autocomplete_Me.pdf)                           |                                                                                                 |                       |
+| 2023 | EMNLP           | [TrojanSQL: SQL Injection against Natural Language Interface to Database.](./papers_en/2023-TrojanSQL.pdf)    | [![Octocat](./figures/github.svg)](https://github.com/jc-ryan/trojan-sql) |                       |
+| 2023 | ACL             | [Backdooring Neural Code Search.](./papers_en/2023-ACL-Backdooring_Neural_Code_Search.pdf) 🚩                                                           | [![Octocat](./figures/github.svg)](https://github.com/wssun/BADCODE)                           |                       |
+| 2022 | ICPR            | [Backdoors in Neural Models of Source Code.](./papers_en/2022-ICPR-Backdoors_in_Neural_Models_of_Source_Code.pdf)                                       | [![Octocat](./figures/github.svg)](https://github.com/tech-srl/code2seq)                       |                       |
+| 2022 | FSE             | [You See What I Want You to See: Poisoning Vulnerabilities in Neural Code Search.](./papers_en/2022-FSE-You_See_What_I_Want_You_to_See.pdf)             | [![Octocat](./figures/github.svg)](https://github.com/CGCL-codes/naturalcc)                    |                       |
+| 2021 | USENIX Security | [Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers.](./papers_en/2021-USENIX-Explanation-Guided_Backdoor_Poisoning_Attacks.pdf) | [![Octocat](./figures/github.svg)](https://github.com/ClonedOne/MalwareBackdoors)              |                       |
+| 2021 | USENIX Security | [You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion. ](./papers_en/2021-USENIX-You_Autocomplete_Me.pdf)                           |                                                                                                |                       |
+
 
 
 - **Model poisoning attacks**: Changes that do not alter the functionality of the code but trick the model.