Confidential virtual machines (CVMs) are designed to protect data in cloud machines, but they fail in this task in common Software-as-a-Service (SaaS) cloud environments. In such settings, the software stack within a CVM, including service programs and the operating system, that receives and processes data may intentionally disclose it to attackers. We present Erebor, a sandboxing architecture for CVMs that processes client data in secure containers, where restrictions apply to both (a) access by all untrusted outside components and (b) the sandbox's ability to communicate data through memory and software-controlled direct or covert exits. Erebor enables such restrictions through a security monitor design based on intra-kernel privilege isolation for CVM, fully compatible with emerging cloud deployments without requiring host modifications. Under realistic scenarios, such as large language model inference and private information retrieval, Erebor only adds a performance overhead of 4.5%-13.2%, demonstrating its practicality in terms of enabling strong data sandboxing in modern cloud machines.

The PHP interpreter, powering over 70% of web-sites on the internet, plays a crucial role in web development. Existing approaches to finding bugs in PHP primarily focus on detecting explicit security issues through crashes or sanitizer-based oracles, but fail to identify logic bugs that can silently lead to incorrect results. We observe that the introduction of Just-In-Time (JIT) compilation mode in PHP presents an opportunity for differential testing, as it provides an alternative implementation of the same language specification. We propose, ZendDiff, an automatic differential testing framework that effectively detects logic bugs in the PHP interpreter by comparing JIT and non-JIT execution results. Our differential testing incorporates three techniques: program state probing for fine-grained execution state comparison, JIT-aware program mutation to sufficiently exercise JIT functionality, and dual verification to handle non-deterministic behaviors in PHP programs. Our experimental results demonstrate that ZendDiffoutperforms the official test suite used in PHP’s continuous integration, achieving higher code coverage and executing more Zend opcodes. Through ablation studies, we validate the effectiveness of these techniques. To date, ZendDiffhas identified 51 previously unknown logic bugs in the PHP interpreter, with 37 already fixed and 3 confirmed by the PHP maintainers. ZendDiffhas been acknowledged by the PHP community and offers a practical tool for automatically discovering logic bugs in the PHP interpreter.

Graph Database Management Systems (GDBMSs) store graphs as data. They are used naturally in applications such as social networks, recommendation systems and program analysis. However, they can be affected by logic bugs, which cause the GDBMSs to compute incorrect results and subsequently affect the applications relying on them. In this work, we propose injective and surjective Graph Query Transformation (GQT) to detect logic bugs in GDBMSs. Given a query Q, we derive a mutated query Q', so that either their result sets are: (i) semantically equivalent; or (ii) variant based on the mutation to be either a subset or superset of each other. When the expected relationship between the results does not hold, a logic bug in the GDBMS is detected. The key insight to mutate Q is that the graph pattern in graph queries enables systematic query transformations derived from injective and surjective mappings of the directed edge sets between Q and Q'. We implemented injective and surjective Graph Query Transformation (GQT) as a tool called GraphGenie and evaluated it on 6 popular and mature GDBMSs. GraphGenie has found 25 unknown bugs, comprising 16 logic bugs, 3 internal errors, and 6 performance issues. Our results demonstrate the practicality and effectiveness of GraphGenie in detecting logic bugs in GDBMSs which has the potential for improving the reliability of applications relying on these GDBMSs.

Many detection and defense mechanisms have been proposed to prevent and mitigate memory errors. A good understanding of the pros and cons of memory defense mechanisms is essential before practical deployment. However, the environment, compilers, and defenses change over time. Thus, a benchmark providing comprehensive evaluations of a range of compilation option choices and defenses is helpful to give developers guidance. The most well-known test suite for evaluating spatial memory errors and exploits is RIPE. However, we show that it is no longer applicable, and a new benchmark is needed. Furthermore, a benchmark should be extensible to evolve over time easily. We propose RecIPE, a new extensible and comprehensive benchmark for evaluating memory error defenses. For extensibility and customisability, RecIPE consists of two components. The TestGen component generates vulnerable code from configurable templates across various vulnerable attributes allowing easy changes through the templates. Measuring the attack and exploitation is with the DefEval component, serving as an attacker at runtime and can be updated with new exploitations. We present a comprehensive evaluation of compiler defenses and sanitizers on RecIPE with gcc and clang, showing the strengths and weaknesses of the various defenses. The results show pros and cons which may not be well known, including gaps between the in-principle guarantee and practical implementation. Our results also point out directions for further improvement in defenses.