Morello-HAT: Morello High-Level API and Tooling

CHERI项目为硬件功能创建了基础设施。Morello项目为Arm架构实现了这些概念和工具。在编程语言方面，CHERI和Morello的重点主要放在C语言上，但在c++和Rust上也做了相当多的工作。Morello- hat项目（Morello高级API和工具）旨在创建一个通用的API，供编译器开发人员和高级语言程序员使用，使他们能够利用Morello的HW功能来提高其语言和程序的内存安全性和类型安全性，包括空间和时间。该项目包括三个工作包：1。使用c++、Rust、Go和Dart作为目标语言开发API 2。通过基于ml的漏洞检测和评估我们的API在缓解漏洞方面的有效性，在一系列示例应用程序上演示API的可用性和有效性。使用硬件功能来增强调试基础结构。这项工作的技术重点将放在更高级的API上，这些API将使用增强运行时类型检查、动态内存管理和并发性的能力。wp1.1将从c++的现有工作开始，设计对象类型安全的API基础，并研究在托管内存中使用这些能力。1. 为对象类型安全开发一个c++ API为C/ c++开发基于功能的垃圾收集器WP1.2将专注于Rust、Go和Dart，这些快速发展的编程语言代表了许多现代编程语言。1. 在现有Rust功能使用工作的基础上，将WP1的类型安全API扩展到具有类Rust类型系统的语言，并设计并发API。2. 提供对垃圾收集和并发的能力支持，以Go为例，它是一种具有托管内存、一流并发支持和结构类型的语言。3. Dart将成为Android应用程序开发的首选语言，它允许动态键入运行时检查。本任务侧重于用功能支持这些检查。我们将使用WP1的高级API和EPSRC AppControl项目中内置的调试接口来创建一个有效的数据收集机制，用于从Morello架构中收集独特的数据特征，这将用于训练我们的ML模型，以检测用c++、Rust、Go和Dart编写的应用程序的非功能和功能版本中的异常。WP 2.1将专注于构建数据收集机制和ML检测模型，以验证高级API 1。设置调试接口以创建高效的低级数据收集机制。2. 创建基准脆弱测试套件3。设计数据收集和数据特征分析的数据预处理和规范化算法。4. 训练和验证异常检测ML模型swp 2.2将侧重于使用基于ML的方法验证来自WP1的API。1. 开发ML运行时计算API 2。开发机器学习推理模型使用ML模型测试和验证高级API swp3保护调试基础设施wp3将通过在CHERI软件堆栈之上创建一个ICMetrics安全的软件API来增强传统暴露的调试基础设施，用于调试和身份验证。创建用于访问调试子系统和寄存器的高级API。分析调试机制和认证信号，创建高级API以访问调试基础设施2。创建ICMetrics安全软件API对调试栈wp 3.2进行集成、测试和验证，对增强的调试管理机制进行试验和评估。1. 创建渗透测试和评估程序套件2。评估和分析改进后的调试机制

The CHERI project has created the infrastructure for hardware capabilities. The Morello project implements these concepts and tools for the Arm architecture. In terms of programming languages, the focus of CHERI and Morello has been primarily on C but considerable work has also been done on C++ and some more preliminary work on Rust. The Morello-HAT project (Morello High-Level API and Tooling) intends to create a common API that can be used by compiler developers as well as programmers of higher-level languages, to allow them to leverage Morello's HW capabilities to improve memory security and type safety, spatial as well as temporal, of their language and programs.The project consists of three work packages:1. Develop the API using C++, Rust, Go and Dart as target languages2. Demonstrate the usability and effectiveness of the API on a series of example applications by ML-based detection of vulnerabilities and assessment of the effectiveness of our API in mitigation of vulnerabilities.3. Use hardware capabilities to enhance the debug infrastructure.WP 1 High-Level Capability APIThe technical focus of the work will be on higher-level APIs which will use capabilities to harden run-time type checking, dynamic memory management and concurrency.WP1.1 will start from existing work on C++ to design the API foundations for object type safety and investigate the use of capabilities in managed memory. 1. Develop a C++ API for object type safety 2. Develop a capability-based garbage collector for C/C++WP1.2 will focus on Rust, Go and Dart, fast-growing programming languages that are representative for many modern programming languages. 1. Building on existing work on use of capabilities in Rust, extend the type safety API from WP1 to languages with a Rust-like type system, and design a concurrency API. 2. Provide capability support for garbage collection and concurrency, using Go as an example of a language with managed memory, first-class concurrency support and structural typing. 3. Dart, set to become the preferred language for Android app development, allows dynamic typing which run-time checks. This task focuses on supporting these checks with capabilities.WP 2 Machine learning based validation of the high-level APIWe will use the High-level API from WP1 and the debug interface built in our EPSRC AppControl project to create an efficient data collection mechanism for collecting unique data features from the Morello architecture, which will be used to train our ML models for detection of anomalies in non-capability as well as capability versions of applications written in C++, Rust, Go and Dart.WP 2.1 will focus on building data collection mechanism and ML detection models for validation of the high-level API 1. Setup debug interface to create an efficient low-level data collection mechanism. 2. Create benchmark vulnerable testing suites 3. Design data pre-processing and normalisation algorithms for data collection and analysis of data features. 4. Train and validate the abnormality detection ML modelsWP 2.2 will focus on validating the API from WP1 using ML based approaches. 1. Develop ML runtime compute API 2. Develop ML inference models 3. Test and validate the high-level API using the ML modelsWP 3 Securing the debug infrastructureWP3 will enhance the traditionally exposed debug infrastructure by creating a ICMetrics secured software API on top of the CHERI software stack for debug and authentication. WP 3.1 Create high-level API for accessing the debug subsystem and registers 1. Analyse debug mechanism and authentication signals to create high-level API to access debug infrastructure 2. Create ICMetrics secured software API 3. Integrate, test and validate the debug stackWP 3.2 Carry out trial and evaluation on the enhanced debug management mechanism. 1. Create penetration testing and evaluation program suites 2. Evaluate and analyse the enhanced debug mechanism