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Found 6 papers, 3 papers with code
AI Code Generators for Security: Friend or Foe?
Recent advances of artificial intelligence (AI) code generators are opening new opportunities in software security research, including misuse by malicious actors.
Enhancing Robustness of AI Offensive Code Generators via Data Augmentation
Then, we use the method to assess the robustness of three state-of-the-art code generators against the newly perturbed inputs, showing that the performance of these AI-based solutions is highly affected by perturbations in the NL descriptions.
Who Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generators
The current practice uses output similarity metrics, i. e., automatic metrics that compute the textual similarity of generated code with ground-truth references.
Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generation
Neural Machine Translation (NMT) has reached a level of maturity to be recognized as the premier method for the translation between different languages and aroused interest in different research areas, including software engineering.
Can We Generate Shellcodes via Natural Language? An Empirical Study
Writing software exploits is an important practice for offensive security analysts to investigate and prevent attacks.
Ranked #1 on
Code Generation
on Shellcode_IA32
Shellcode_IA32: A Dataset for Automatic Shellcode Generation
We take the first step to address the task of automatically generating shellcodes, i. e., small pieces of code used as a payload in the exploitation of a software vulnerability, starting from natural language comments.
Ranked #3 on
Code Generation
on Shellcode_IA32
