Vulnerability detection is a critical problem in software security and attracts growing attention both from academia and industry. Traditionally, software security is safeguarded by designated rule-based detectors that heavily rely on empirical expertise, requiring tremendous effort from software experts to generate rule repositories for large code corpus. Recent advances in deep learning, especially Graph Neural Networks (GNN), have uncovered the feasibility of automatic detection of a wide range of software vulnerabilities. However, prior learning-based works only break programs down into a sequence of word tokens for extracting contextual features of codes, or apply GNN largely on homogeneous graph representation (e.g., AST) without discerning complex types of underlying program entities (e.g., methods, variables). In this work, we are one of the first to explore heterogeneous graph representation in the form of Code Property Graph and adapt a well-known heterogeneous graph network with a dual-supervisor structure for the corresponding graph learning task. Using the prototype built, we have conducted extensive experiments on both synthetic datasets and real-world projects. Compared with the state-of-the-art baselines, the results demonstrate promising effectiveness in this research direction in terms of vulnerability detection performance (average F1 improvements over 10\% in real-world projects) and transferability from C/C++ to other programming languages (average F1 improvements over 11%).

Jack Ma, the founder of tech giant Alibaba, has made a rare public appearance in China, potentially signalling government efforts to quell concerns in the country's tech sector after a bruising two-year regulatory crackdown. China's best-known entrepreneur has kept a low profile since late 2020 when a speech he made attacking Chinese regulators was followed by Beijing pulling Alibaba affiliate Ant Group's planned initial public offering. A record fine of $2.75bn was then imposed on the company for alleged unfair practices. Ma has been spotted around the world over the past two years and was reportedly living in Japan for much of 2022. He returned to China last week, said two sources with knowledge of the matter who were quoted by the Reuters news agency.

Intel Software Guard Extensions (Intel SGX) is a securing computing tool that generates a trusted execution environment (TEE) for users that need secure and confidential environments for such use cases as private key management, multi-party computing with private data, and securing public cloud deployment for critical applications. While the Intel SGX SDK for Linux* OS successfully tackles these important use cases, its implementation is not simple. It can require significant system redesign and code changes by engineers because under the SGX SDK's threat model, the OS is not trusted, and only trusted applications and code can be worked on in the secure environment portioned out by SGX, i.e., an "enclave." Therefore, the trusted and untrusted components of the applications involved need to be separated. Moreover, engineers will then need to re-engineer some of their code base to ensure it will be trusted in this enclave.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. Opaque, a startup that helps organizations analyze encrypted data in the cloud, today announced that it closed a $9.5 million seed funding round led by Intel Capital with contributions from Race Capital, The House Fund, and FactoryHQ. Cofounder Raluca Ada Popa says that the funds will help to expand Opaque's ongoing contributions to the open source and data security communities. A majority of data sits in private hands.

Christopher Savoie, PhD is the CEO & founder of Zapata Computing. He is a published scholar in medicine, biochemistry and computer science. In the coming weeks, the Chinese fintech giant Ant Group is set to raise $34 billion in the world's largest-ever IPO. Although it only spun out of Alibaba in 2014, Ant's valuation, at $310 billion, will be comparable to that of JPMorgan Chase, whose origins date back to 1799. In their 2018 book, Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World, Harvard Business School professors Marco Iansiti and Karim R. Lakhani make the case that Ant's stunning growth can be directly attributed to its use of artificial intelligence (AI).