DIP: Dead code Insertion based Black-box Attack for Programming Language Model (2023.acl-long)
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| Challenge: | Existing methods to attack natural language models are difficult to apply due to the requirements. |
| Approach: | They propose a black-box attack method that generates adversarial examples using dead code insertion. |
| Outcome: | The proposed method outperforms the state-of-the-art black-box attack in both attack efficiency and attack quality on 9 victim downstream-task large code models. |
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| Challenge: | Recent work in natural language processing generates adversarial examples using white-box access . a neural network can learn to emulate the behavior of a white- box attack and generalize well to new examples. |
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| Challenge: | Recent work shows that large language models can generate code on par with humans . however, data-driven approaches may not be sufficient for acquiring reasoning skills . |
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| Challenge: | Existing research has shown that large language models have difficulty discerning the veracity of their intrinsic answers. |
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Baohang Zhou, Zezhong Wang, Lingzhi Wang, Hongru Wang, Ying Zhang, Kehui Song, Xuhui Sui, Kam-Fai Wong
| Challenge: | Existing methods to detect pretraining data from large language models are unrealistic to them. |
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| Challenge: | Existing backdoor attacks are not stealthy to system deployers or users. |
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