Toward least-privilege isolation for software

Hackers leverage software vulnerabilities to disclose, tamper with, or destroy sensitive data. To protect sensitive data, programmers can adhere to the principle of least-privilege, which entails giving software the minimal privilege it needs to operate, which ensures that sensitive data is only available to software components on a strictly need-to-know basis. Unfortunately, applying this principle in practice is dif- �cult, as current operating systems tend to provide coarse-grained mechanisms for limiting privilege. Thus, most applications today run with greater-than-necessary privileges. We propose sthreads, a set of operating system primitives that allows �ne-grained isolation of software to approximate the least-privilege ideal. sthreads enforce a default-deny model, where software components have no privileges by default, so all privileges must be explicitly granted by the programmer. Experience introducing sthreads into previously monolithic applications|thus, partitioning them|reveals that enumerating privileges for sthreads is di�cult in practice. To ease the introduction of sthreads into existing code, we include Crowbar, a tool that can be used to learn the privileges required by a compartment. We show that only a few changes are necessary to existing code in order to partition applications with sthreads, and that Crowbar can guide the programmer through these changes. We show that applying sthreads to applications successfully narrows the attack surface by reducing the amount of code that can access sensitive data. Finally, we show that applications using sthreads pay only a small performance overhead. We applied sthreads to a range of applications. Most notably, an SSL web server, where we show that sthreads are powerful enough to protect sensitive data even against a strong adversary that can act as a man-in-the-middle in the network, and also exploit most code in the web server; a threat model not addressed to date

A Vulnerability Assessment of the East Tennessee State University Administrative Computer Network.

A three phase audit of East Tennessee State University\u27s administrative computer network was conducted during Fall 2001, Spring 2002, and January 2004. Nmap and Nessus were used to collect the vulnerability data. Analysis discovered an average of 3.065 critical vulnerabilities per host with a low of 2.377 in Spring 2001 to a high of 3.694 in Fall 2001. The number of unpatched Windows operating system vulnerabilities, which accounted for over 75% of these critical vulnerabilities, strongly argues for the need of an automated patch deployment system for the approximately 3,000 Windows-based systems at ETSU

CVE-driven Attack Technique Prediction with Semantic Information Extraction and a Domain-specific Language Model

This paper addresses a critical challenge in cybersecurity: the gap between vulnerability information represented by Common Vulnerabilities and Exposures (CVEs) and the resulting cyberattack actions. CVEs provide insights into vulnerabilities, but often lack details on potential threat actions (tactics, techniques, and procedures, or TTPs) within the ATT&CK framework. This gap hinders accurate CVE categorization and proactive countermeasure initiation. The paper introduces the TTPpredictor tool, which uses innovative techniques to analyze CVE descriptions and infer plausible TTP attacks resulting from CVE exploitation. TTPpredictor overcomes challenges posed by limited labeled data and semantic disparities between CVE and TTP descriptions. It initially extracts threat actions from unstructured cyber threat reports using Semantic Role Labeling (SRL) techniques. These actions, along with their contextual attributes, are correlated with MITRE's attack functionality classes. This automated correlation facilitates the creation of labeled data, essential for categorizing novel threat actions into threat functionality classes and TTPs. The paper presents an empirical assessment, demonstrating TTPpredictor's effectiveness with accuracy rates of approximately 98% and F1-scores ranging from 95% to 98% in precise CVE classification to ATT&CK techniques. TTPpredictor outperforms state-of-the-art language model tools like ChatGPT. Overall, this paper offers a robust solution for linking CVEs to potential attack techniques, enhancing cybersecurity practitioners' ability to proactively identify and mitigate threats