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1. An Empirical Study of Vulnerabilities in Robotics

   https://doi.org/10.1109/COMPSAC51774.2021.00105  

   Cottrell, Kaitlyn; Bose, Dibyendu Brinto; Shahriar, Hossain; Rahman, Akond (July 2021, 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC))

   null (Ed.)

   The ubiquitous usage of robots in modern society necessitates secure development of robotics systems. Practitioners who engage in robot development can benefit from a systematic study that investigates the categories of vulnerabilities that appear in robotics systems. The goal of this paper is to help practitioners mitigate vulnerabilities in robotics systems by conducting an empirical study of vulnerabilities in robotics systems. We conduct an empirical study where we analyze 176 robotics-related vulnerabilities collected from the Robot Vulnerability Database (RVD). Our findings show that: (i) robotics-related vulnerabilities can be classified into nine categories; (ii) memory-related vulnerabilities are the most frequent category, (iii) 92.6% of the reported vulnerabilities are software-related, and (iv) software components in robotics systems include more critical vulnerabilities compared to that of hardware components. Based on our findings, we provide a list of development activities that can be used to mitigate vulnerabilities for robotics systems. 

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2. The Benefits of Vulnerability Discovery and Bug Bounty Programs: Case Studies of Chromium and Firefox

   https://doi.org/10.1145/3543507.3583352  

   Atefi, Soodeh; Sivagnanam, Amutheezan; Ayman, Afiya; Grossklags, Jens; Laszka, Aron (April 2023, Proceedings of the ACM Web Conference 2023 (WWW'23))

   Recently, bug-bounty programs have gained popularity and become a significant part of the security culture of many organizations. Bug-bounty programs enable organizations to enhance their security posture by harnessing the diverse expertise of crowds of external security experts (i.e., bug hunters). Nonetheless, quantifying the benefits of bug-bounty programs remains elusive, which presents a significant challenge for managing them. Previous studies focused on measuring their benefits in terms of the number of vulnerabilities reported or based on the properties of the reported vulnerabilities, such as severity or exploitability. However, beyond these inherent properties, the value of a report also depends on the probability that the vulnerability would be discovered by a threat actor before an internal expert could discover and patch it. In this paper, we present a data-driven study of the Chromium and Firefox vulnerability-reward programs. First, we estimate the difficulty of discovering a vulnerability using the probability of rediscovery as a novel metric. Our findings show that vulnerability discovery and patching provide clear benefits by making it difficult for threat actors to find vulnerabilities; however, we also identify opportunities for improvement, such as incentivizing bug hunters to focus more on development releases. Second, we compare the types of vulnerabilities that are discovered internally vs. externally and those that are exploited by threat actors. We observe significant differences between vulnerabilities found by external bug hunters, internal security teams, and external threat actors, which indicates that bug-bounty programs provide an important benefit by complementing the expertise of internal teams, but also that external hunters should be incentivized more to focus on the types of vulnerabilities that are likely to be exploited by threat actors. 

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3. Come for syntax, stay for speed, understand defects: an empirical study of defects in Julia programs

   Akond Rahman, Dibyendu Brinto (October 2023, Empirical software engineering)

   Robert Feldt and Thomas Zimmermann (Ed.)

   Julia has emerged as a popular programming language to develop scientific software, in part due to its flexible syntax akin to scripting languages while retaining the execution speed of a compiled language. Similar to any programming language, Julia programs are susceptible to defects. However, a systematic characterization of defects in Julia programs remains under-explored. A systematic analysis of defects in Julia programs will act as a starting point for researchers and toolsmiths in building developer tools to improve the quality of Julia programs. To this end, we conduct an empirical study with 742 defects that appear in Julia programs by mining 30,494 commits and 3,038 issue reports collected from 112 open-source Julia projects. From our empirical analysis, we identify 9 defect categories and 7 defect symptoms. We observe certain defect categories to be Julia-specific, e.g., type instability and world age defects. We also survey 52 developers to rank the identified categories based on perceived severity. Based on our empirical analysis, we provide specific recommendations for researchers and toolsmiths. 

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4. A Survey on Large Language Model (LLM) Security and Privacy: The Good, The Bad, and The Ugly

   https://doi.org/10.1016/j.hcc.2024.100211  

   Yao, Yifan; Duan, Jinhao; Xu, Kaidi; Cai, Yuanfang; Sun, Zhibo; Zhang, Yue (June 2024, High-Confidence Computing)

   Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized natural language understanding and generation. They possess deep language comprehension, human-like text generation capabilities, contextual awareness, and robust problem-solving skills, making them invaluable in various domains (e.g., search engines, customer support, translation). In the meantime, LLMs have also gained traction in the security community, revealing security vulnerabilities and showcasing their potential in security-related tasks. This paper explores the intersection of LLMs with security and privacy. Specifically, we investigate how LLMs positively impact security and privacy, potential risks and threats associated with their use, and inherent vulnerabilities within LLMs. Through a comprehensive literature review, the paper categorizes the papers into “The Good” (beneficial LLM applications), “The Bad” (offensive applications), and “The Ugly” (vulnerabilities of LLMs and their defenses). We have some interesting findings. For example, LLMs have proven to enhance code security (code vulnerability detection) and data privacy (data confidentiality protection), outperforming traditional methods. However, they can also be harnessed for various attacks (particularly user-level attacks) due to their human-like reasoning abilities. We have identified areas that require further research efforts. For example, Research on model and parameter extraction attacks is limited and often theoretical, hindered by LLM parameter scale and confidentiality. Safe instruction tuning, a recent development, requires more exploration. We hope that our work can shed light on the LLMs’ potential to both bolster and jeopardize cybersecurity. 

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5. VFCFinder: Pairing Security Advisories and Patches

   https://doi.org/10.1145/3634737.3657007  

   Dunlap, Trevor; Lin, Elizabeth; Enck, William; Reaves, Bradley (July 2024, Proceedings of the ACM ASIA Conference on Computer and Communications Security (AsiaCCS))

   Security advisories are the primary channel of communication for discovered vulnerabilities in open-source software, but they often lack crucial information. Specifically, 63% of vulnerability database reports are missing their patch links, also referred to as vulnerability fixing commits (VFCs). This paper introduces VFCFinder, a tool that generates the top-five ranked set of VFCs for a given security advisory using Natural Language Programming Language (NL-PL) models. VFCFinder yields a 96.6% recall for finding the correct VFC within the Top-5 commits, and an 80.0% recall for the Top-1 ranked commit. VFCFinder generalizes to nine different programming languages and outperforms state-of-the-art approaches by 36 percentage points in terms of Top-1 recall. As a practical contribution, we used VFCFinder to backfill over 300 missing VFCs in the GitHub Security Advisory (GHSA) database. All of the VFCs were accepted and merged into the GHSA database. In addition to demonstrating a practical pairing of security advisories to VFCs, our general open-source implementation will allow vulnerability database maintainers to drastically improve data quality, supporting efforts to secure the software supply chain. 

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