Recent advances of artificial intelligence (AI) code generators are opening
new opportunities in software security research, including misuse by malicious
actors. We review use cases for AI code generators for security and introduce
an evaluation benchmark.

人工智能代码生成器在软件安全研究中取得了新的进展，我们回顾了人工智能代码生成器在安全领域的应用案例，并引入了一个评估基准。

AI 代码生成器与安全：友还是敌？

AI Code Generators for Security: Friend or Foe?

In this work, we present a method to add perturbations to the code
descriptions, i.e., new inputs in natural language (NL) from well-intentioned
developers, in the context of security-oriented code, and analyze how and to
what extent perturbations affect the performance of AI offensive code
generators. Our experiments show that the performance of the code generators is
highly affected by perturbations in the NL descriptions. To enhance the
robustness of the code generators, we use the method to perform data
augmentation, i.e., to increase the variability and diversity of the training
data, proving its effectiveness against both perturbed and non-perturbed code
descriptions.

本文利用自然语言中新的输入，即代码描述的扰动，在安全导向的代码环境下分析了这些扰动对 AI 进攻代码生成器性能的影响，并使用该方法进行了数据增强以增加训练数据的变化和多样性，证明其对扰动和非扰动代码描述的有效性。

数据增广提升 AI 攻击代码生成器的鲁棒性

Enhancing Robustness of AI Offensive Code Generators via Data  Augmentation

This work presents MLIR, a novel approach to building reusable and extensible
compiler infrastructure. MLIR aims to address software fragmentation, improve
compilation for heterogeneous hardware, significantly reduce the cost of
building domain specific compilers, and aid in connecting existing compilers
together. MLIR facilitates the design and implementation of code generators,
translators and optimizers at different levels of abstraction and also across
application domains, hardware targets and execution environments. The
contribution of this work includes (1) discussion of MLIR as a research
artifact, built for extension and evolution, and identifying the challenges and
opportunities posed by this novel design point in design, semantics,
optimization specification, system, and engineering. (2) evaluation of MLIR as
a generalized infrastructure that reduces the cost of building
compilers-describing diverse use-cases to show research and educational
opportunities for future programming languages, compilers, execution
environments, and computer architecture. The paper also presents the rationale
for MLIR, its original design principles, structures and semantics.

MLIR 是一种新型的编译器基础结构方法，旨在通过适应不同应用域、硬件目标和执行环境的代码生成器、转换器和优化器，解决软件碎片化问题，改善异构硬件编译，显著降低领域特定编译器的构建成本，促进现有编译器的连接，同时提供扩展和演进的研究工具。