Semantically-Equivalent Transformations-Based Backdoor Attacks against Neural Code Models: Characterization and Mitigation
Published:Dec 22, 2025 09:54
•1 min read
•ArXiv
Analysis
This article likely presents research on a specific type of adversarial attack against neural code models. It focuses on backdoor attacks, where malicious triggers are inserted into the training data to manipulate the model's behavior. The research likely characterizes these attacks, meaning it analyzes their properties and how they work, and also proposes mitigation strategies to defend against them. The use of 'semantically-equivalent transformations' suggests the attacks exploit subtle changes in the code that don't alter its functionality but can be used to trigger the backdoor.
Key Takeaways
- •Focuses on backdoor attacks against neural code models.
- •Explores attacks based on semantically-equivalent transformations.
- •Aims to characterize and mitigate these attacks.
Reference
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