Introducing the Robot Vulnerability Database (RVD) (1912.11299v3)

Abstract: Cybersecurity in robotics is an emerging topic that has gained significant traction. Researchers have demonstrated some of the potentials and effects of cyber attacks on robots lately. This implies safety related adverse consequences causing human harm, death or lead to significant integrity loss clearly overcoming the privacy concerns in classical IT world. In cybersecurity research, the use of vulnerability databases is a very reliable tool to responsibly disclose vulnerabilities in software products and raise willingness of vendors to address these issues. In this paper we argue, that existing vulnerability databases are of insufficient information density and show some biased content with respect to vulnerabilities in robots. This paper presents the Robot Vulnerability Database (RVD), a directory for responsible disclosure of bugs, weaknesses and vulnerabilities in robots. This article aims to describe the design and process as well as the associated disclosure policy behind RVD. Furthermore the authors present preliminary selected vulnerabilities already contained in RVD and call to the robotics and security communities for contribution to the endeavour of eliminating zero-day vulnerabilities in robotics.

• The paper introduces a domain-specific vulnerability database (RVD) that targets robotics security by integrating tailored severity scoring and detailed reproduction guidelines.
• The paper outlines a novel methodology that classifies vulnerabilities using a robotics-specific taxonomy and leverages Docker for rapid vulnerability reproduction.
• The paper emphasizes community involvement and data sharing to overcome measurement biases and enhance cybersecurity in robotic systems.

An Overview of the Robot Vulnerability Database (RVD)

The paper, "Introducing the Robot Vulnerability Database (RVD)", explores the creation and development of a domain-specific vulnerability database aimed at addressing the unique cybersecurity challenges within the field of robotics. The authors assert that while there are numerous vulnerability databases available, existing databases are ill-equipped to handle the intricacies associated with robotics. Robotics introduces complexities due to its integration of hardware and software, making errors in these systems potentially more damaging compared to traditional IT systems.

Motivation and Background

The cybersecurity of robotic systems is increasingly critical due to the potential for cyberattacks to cause not only data breaches but also physical harm to humans and the environment. The authors argue that traditional databases such as the National Vulnerability Database (NVD) fall short in providing detailed, actionable insights on robotic vulnerabilities due to insufficient information and lack of focus on domain-specific issues. Such shortcomings include poor reproduction details of vulnerabilities and a lack of appropriate severity scoring mechanisms, specific to robotics, which fail to account for the physical interactions and safety implications inherent to robotics systems.

Design and Objectives of RVD

The RVD is put forward as a structured approach to addressing these shortcomings by documenting and categorizing software and hardware vulnerabilities specific to robots and their components. The database aims to enhance information sharing by incorporating standards from established projects like the Common Vulnerabilities and Exposures (CVE) list while introducing details that are unique to robotics.

Key features of the RVD include:

1. Scope: The database covers robotic systems and their components comprehensively.
2. Terminology and Taxonomy: The authors emphasize clarity and consistency in language and categorization to avoid confusion and miscommunication.
3. Sharing Model: RVD adopts a mostly open model with an emphasis on community contribution, privacy, and facilitating integration with private sources where necessary.
4. Severity Assessment: It offers a robot-specific scoring system (RVSS), aiming to better gauge the severity of vulnerabilities by considering robotics' unique operational risks.

Methodological Contributions

The paper discusses an extensive framework for the database that includes:

• The methodology for classifying vulnerabilities, emphasizing robotics-focused severity scoring.
• Structuring information to facilitate rapid reproduction of vulnerabilities using technologies such as Docker for OS virtualization.
• Suggested amendments in the measurement process to ensure realistic assessments of vulnerabilities' potential impacts.

Preliminary Findings and Implications

The paper presents preliminary data from RVD entries, highlighting a skew in vulnerability distribution across different vendors, which could imply varying levels of commitment to security across the industry. The paper also underscores the importance of community involvement in identifying and addressing security flaws, stressing that rising security engagements correlate with diverse vulnerability severity in vendors.

The authors explicitly critique the selection, publication, abstraction, and measurement biases in vulnerability research. They underscore that RVD seeks to overcome these challenges by providing detailed reproduction instructions and advocating for more comprehensive reporting.

Future Directions

The paper concludes by outlining future enhancements, including mining data for open source robotic components, automating security pipeline management, and improving the distinction between robotics, operational technologies (OT), and informational technologies (IT). With these efforts, the RVD aims to mitigate zero-day vulnerabilities and lead to more secure robotic ecosystems.

In summary, the RVD signifies a dedicated step toward establishing robust cybersecurity practices in robotics. It recognizes the field's specific needs and attempts to bridge the gap left by general vulnerability databases. By engaging the robotics and cybersecurity communities, the RVD aspires to become a pivotal resource in mitigating the growing cybersecurity threats poised by robotic systems.