Vulnerability Scoring Research Articles

Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach

Cities become mission-critical zones during pandemics and it is vital to develop a better understanding of the factors that are associated with infection levels. The COVID-19 pandemic has impacted many cities severely; however, there is significant variance in its impact across cities. Pandemic infection levels are associated with inherent features of cities (e.g., population size, density, mobility patterns, socioeconomic condition, and health & environment), which need to be better understood. Intuitively, the infection levels are expected to be higher in big urban agglomerations, but the measurable influence of a specific urban feature is unclear. The present study examines 41 variables and their potential influence on the incidence of COVID-19 infection cases. The study uses a multi-method approach to study the influence of variables, classified as demographic, socioeconomic, mobility and connectivity, urban form and density, and health and environment dimensions. This study develops an index dubbed the pandemic vulnerability index at city level (PVI-CI) for classifying the pandemic vulnerability levels of cities, grouping them into five vulnerability classes, from very high to very low. Furthermore, clustering and outlier analysis provides insights on the spatial clustering of cities with high and low vulnerability scores. This study provides strategic insights into levels of influence of key variables upon the spread of infections, along with an objective ranking for the vulnerability of cities. Thus, it provides critical wisdom needed for urban healthcare policy and resource management. The calculation method for the pandemic vulnerability index and the associated analytical process present a blueprint for the development of similar indices for cities in other countries, leading to a better understanding and improved pandemic management for urban areas, and more resilient planning for future pandemics in cities across the world.

A hybrid scoring system for prioritization of software vulnerabilities

While security experts, firms, security providers, threat analysts all around the globe are working hard to provide ironclad security for information system softwares, vulnerabilities in softwares are still being detected on a daily basis. Although it is very important to neutralize all these vulnerabilities as quickly as possible, prioritization of vulnerabilities to determine the order for resolving these vulnerabilities is an equally important step for optimization of the remediation process. Among different available techniques for prioritization of vulnerabilities, scoring the vulnerabilities is the most common practice. In this paper, we have proposed a novel hybrid technique, VIEWSS: Variable Impact-Exploitability Weightage Scoring System, which scores vulnerabilities by varying the Impact-Exploitability score ratio towards calculation of base scores. We have also discussed three popular quantitative vulnerability scoring techniques; CVSS (Common Vulnerability Scoring System), VRSS (Vulnerability Rating and Scoring System) and WIVSS (Weighted Impact Vulnerability Scoring System) by explaining the underlying metrics, scoring systems, formulas and some limitations of these techniques. The proposed technique, VIEWSS is also compared with the discussed techniques by generating scores for around 83,500 vulnerabilities reported on NVD (National Vulnerability Database) in the last five years. It is observed that our proposed technique generates the greatest number of distinct scores and also the score distribution of our technique performs well on different parameters including skewness and kurtosis. Further when these techniques were applied on a dataset containing around 12,200 ‘high-risk’ vulnerabilities extracted by cross-referencing NVD and Exploit-DB, our proposed technique outperformed all the discussed techniques in terms of identifying the threat accurately.

Trust and betrayal in older adult financial exploitation

Objectives Although the knowledge base regarding the financial exploitation of older adults is expanding, work to understand the subpopulations of older adult financial exploitation victims and their experiences is greatly needed. This study uses betrayal trauma theory (BTT) as the foundation for conceptualizing the harm that arises from elder family financial exploitation. Methods The study uses a cross-sectional design to investigate group differences among a total sample of 95 community-dwelling older adults, 32 of the participants (33.7%) were older adult victims of family financial exploitation and the remaining 63 (66.3%) were victims of financial exploitation perpetrated by strangers. Results The group of older adults who were victims of elder family financial exploitation had significantly lower functional ability scores, higher stress and financial exploitation vulnerability scores and lost more money on average than those victimized by strangers. Conclusion The present study provides support that BTT provides a valuable framework for understanding why older adult family financial exploitation victims are more vulnerable than victims of exploitation committed by strangers. Attention to this subgroup of financially exploited older adults will provide improved understanding of the unique challenges these victims face and inform prevention and intervention services.

Mapping Heat Vulnerability of a Community Mental Health Center Population.

Individuals with serious mental illness are vulnerable to extreme heat due to biological, social, and place-based factors. We examine the spatial correlation of prevalence of individuals treated at a community mental health center to heat vulnerability. We applied a heat vulnerability index (HVI)to the catchment of the Connecticut Mental Health Center in New Haven, Connecticut. Geocoded addresses were mapped to correlate patient prevalence with heat vulnerability of census tracts. Census tracts closer to the city center had elevated vulnerability scores. Patient prevalence was positively correlated with HVI score (Pearson's r(44) = 0.67, p < 0.01). Statistical significance persists after correction for spatial autocorrelation (modified t-test p < 0.01). The study indicates that individuals treated at this community mental health center are more likely to live in census tracts with high heat vulnerability. Heat mapping strategies can help communicate risk and target resources at the local scale.

Stress load and volunteer motivation of patient volunteers in Fangcang shelter hospitals in Shanghai, China during the COVID-19 pandemic: a cross-sectional survey

ObjectivesThis study aims to assess the volunteer motivation and stress load of patient volunteers in the Fangcang shelter hospitals (FSHs), examine their associations, and explore the potential influence factors of...

Automation of Quantifying Security Risk Level on Injection Attacks Based on Common Vulnerability Scoring System Metric

An injection attack is a cyber-attack that is one of The Open Web Application Security Project Top 10 Vulnerabilities. These attacks take advantage of insufficient user input validation into the system through the input surface of a Web application as that user in the browser. The company’s cyber security team must filter thousands of attacks to prioritize which attacks are considered the most dangerous to be mitigated first. This activity of filtering thousands of attacks takes much time because you have to check these attacks one by one. Therefore, a method is needed to assess how dangerous a cyber-attack is that enters an organization’s or company’s server. Injection attack detection can be done by analyzing the request data in the web server log. Our research attempts to perform quantification modeling of the variations of two types of injection attacks, SQL Injection (SQLi) and Cross-Site Scripting (XSS), using Common Vulnerability Scoring System Metrics (CVSS). CVSS metrics are generally used to calculate the level of dangerous weakness in the system. This metric is never used to calculate the level of how dangerous an attack is. The modeling that we have made shows that SQLi and XSS attacks have many variations in levels ranging from low to high levels. We discovered that when classified with Common Weakness Enumeration Database, SQLi and XSS attacks CVE values would have high-level congruence with almost 94% value between one another vector on CVSS.

A Data-Driven Approach for Assessing Sea Level Rise Vulnerability Applied to Puget Sound, Washington State, USA

Sea level rise (SLR) will exert pressures on assets with social value, including things such as infrastructure and habitats, in the coastal zone. Assessing and ranking the vulnerability of those assets can provide insights that support planning and projects that can reduce those vulnerabilities. In this study, we develop a quantitative, data-drive framework for calculating a sea level rise vulnerability score, using publicly available spatial data, for 111,239 parcels in Puget Sound, Washington State, USA. Notably, our approach incorporates an assessment of coastal erosion, as well as coastal flooding, in an evaluation of the exposure of each parcel, and impacts to habitats are quantified alongside impacts to existing infrastructure. The results suggest that sea level rise vulnerability in Puget Sound is widely distributed, but the overall distribution of scores is heavily skewed, suggesting that adaptation actions directed at a relatively small number of parcels could yield significant reductions in vulnerability. The results are also coupled with a concurrently developed social vulnerability index, which provides additional insight regarding those people and places that may be predisposed to adverse impacts from SLR-related hazards. We find that the proposed approach offers advantages in terms of advancing equitable SLR-related risk reduction, but also that the results should be carefully interpreted considering embedded assumptions and data limitations.

Disproportional vulnerability of mountain aquatic invertebrates to climate change effects

ABSTRACT Mountain freshwater communities are generally considered sensible to accelerated climatic changes, though their vulnerabilities have not been well evaluated. Individual species or species groups are expected to respond differently depending on their adaptations, traits, or distributions, but this has not yet been distinguished. This work used available climate change vulnerability scores (ccvs) of European Ephemeroptera, Plecoptera, and Trichoptera (EPT) species (n = 1,402) to (1) compare the vulnerability between species pools of different ecoregions including alpine species and alpine endemics, (2) contrast the vulnerability between the different insect orders, and (3) assess the altitude–vulnerability relationship within the European Alps. We revealed fifty alpine Plecoptera and Trichoptera species that are categorized highly vulnerable to climate change effects (= 31 percent of all highly vulnerable European species) with the highest proportions in species inventories of alpine endemics and high-altitude waters (51 percent of high-altitude species are classified as highly vulnerable). The ccvs analysis specifically for mountain waters shows that a disproportionately high number of alpine species, and particularly alpine endemics, will be affected by climate change and suggests that Ephemeroptera may be better prepared than Plecoptera and Trichoptera. Thus, this trait-based evaluation suggests that mountain stream invertebrate communities are undergoing disproportionate restructurings in response to climate change effects more than lowland communities are.

Attack Path Detection for IIoT Enabled Cyber Physical Systems: Revisited

In this paper, we propose a generic vulnerability and risk assessment method for IIoT-enabled critical systems. We focus on reducing risk factors and vulnerable structures in order to provide security issues for the IIoT and enabled complex systems. In addition to the existing risk assessment and related methods, we represent the IIoT-enabled network topology as a directed graph, and we develop an attack tree-based approach using graph theory. We assume that each device is a potential critical node due to the existing vulnerabilities, which are defined in the National Vulnerability Database (NVD), and we establish directed relations between nodes, considering cyber and physical interactions. We improve existing attack path-identifying methods using the Depth First Search (DFS) algorithm to find all the paths from the source to the target nodes. In the generated topology, each node has the pre-assigned Common Vulnerability Scoring System (CVSS) scores acting as a weight. We also implement the Floyd-Warshall algorithm to identify path risk levels. Finally, we assess the identified vulnerable paths from varying source and target pairs via path and node-reducing procedures, considering risk thresholds. We perform our simulation on a custom Python simulator, considering the transportation and supply sectors. We compare our results with the previous ones. Simulation results show that our proposed methods and procedures outperform existing risk assessment and filtering methods in terms of running time and attack path identification and filtering.

Agreement and prognostic accuracy of three ED vulnerability screeners: findings from a prospective multi-site cohort study

ObjectivesTo evaluate the agreement between three emergency department (ED) vulnerability screeners, including the InterRAI ED Screener, ER2, and PRISMA-7. Our secondary objective was to evaluate the discriminative accuracy of screeners in predicting discharge home and extended ED lengths-of-stay (> 24 h).MethodsWe conducted a nested sub-group study using data from a prospective multi-site cohort study evaluating frailty in older ED patients presenting to four Quebec hospitals. Research nurses assessed patients consecutively with the three screeners. We employed Cohen's Kappa to determine agreement, with high-risk cut-offs of three and four for the PRISMA-7, six for the ER2, and five for the interRAI ED Screener. We used logistic regression to evaluate the discriminative accuracy of instruments, testing them in their dichotomous, full, and adjusted forms (adjusting for age, sex, and hospital academic status).ResultsWe evaluated 1855 older ED patients across the four hospital sites. The mean age of our sample was 84 years. Agreement between the interRAI ED Screener and the ER2 was fair (K = 0.37; 95% CI 0.33–0.40); agreement between the PRISMA-7 and ER2 was also fair (K = 0.39; 95% CI = 0.36–0.43). Agreement between interRAI ED Screener and PRISMA-7 was poor (K = 0.19; 95% CI 0.16–0.22). Using a cut-off of four for PRISMA-7 improved agreement with the ER2 (K = 0.55; 95% CI 0.51–0.59) and the ED Screener (K = 0.32; 95% CI 0.2–0.36). When predicting discharge home, the concordance statistics among models were similar in their dichotomous (c = 0.57–0.61), full (c = 0.61–0.64), and adjusted forms (c = 0.63–0.65), and poor for all models when predicting extended length-of-stay.ConclusionED vulnerability scores from the three instruments had a fair agreement and were associated with important patient outcomes. The interRAI ED Screener best identifies older ED patients at greatest risk, while the PRISMA-7 and ER2 are more sensitive instruments.

What Diverse Samples Can Teach Us About Cognitive Vulnerability to Depression

Tests of generalizability can diversify psychological science and improve theories and measurement. To this end, we conducted five studies testing the cognitive vulnerability to depression hypothesis featured in the hopelessness theory of depression: Study 1 was conducted with Honduran young adults (n = 50); Study 2 was conducted with Nepali adults (n = 34); Study 3 was conducted with Western hemisphere adults (n = 104); Study 4 was conducted with Black U.S. adults (n = 119); and Study 5 was conducted with U.S. undergraduates (n = 110). Results showed that cognitive vulnerability could be measured reliably in diverse populations and the distribution of vulnerability scores was similar for all samples. However, the tendency to generate negative inferences about stress had different implications for depression depending on sample; the association between cognitive vulnerability and depressive symptoms did not generalize to Honduran and Nepali participants. It is now necessary to understand why a negative cognitive style confers risk for depression in some contexts but not others (e.g., is it issues related to measurement, theory, or both). The results also suggest that understanding and reducing the global burden of depression will require more than simply “translating” existing cognitive measures and theories to other countries.

Financial vulnerability of trauma centers: A national analysis.

Trauma centers function as an essential safeguard in the United States health care system. However, there has been minimal study of their financial health or vulnerability. We sought to perform a nationwide analysis of trauma centers using detailed financial data and a recently developed Financial Vulnerability Score (FVS) metric. The RAND Hospital Financial Database was used to evaluate all American College of Surgeons-verified trauma centers nationwide. The composite FVS was calculated for each center using six metrics. Financial Vulnerability Score tertiles were used to classify centers as high, medium, or low vulnerability, and hospital characteristics were analyzed and compared. Hospitals were also compared by US Census region and teaching versus nonteaching hospitals. A total of 311 American College of Surgeons-verified trauma centers were included in the analysis, with 100 (32%) Level I, 140 (45%) Level II, and 71 (23%) Level III. The largest share of the high FVS tier was consisted of Level III centers (62%), with the majority of Level I (40%) and Level II (42%) in the middle and low FVS tier, respectively. The most vulnerable centers had fewer beds, negative operating margins, and significantly less cash on hand. Lower FVS centers had greater asset/liability ratios, lower outpatient shares, and three times less uncompensated care. Nonteaching centers were statistically significantly more likely to have high vulnerability compared with teaching centers (46% vs. 29%). Statewide analysis showed high discrepancy among individual states. With approximately 25% of Levels I and II trauma centers at high risk for financial vulnerability, disparities in characteristics, including payer mix and outpatient status, should be targeted to reduce vulnerabilities and bolster the health care safety net. Prognostic and Epidemiological; Level IV.

Uji Vulnerability Assessment Dalam Mengetahui Tingkat Keamanan Web Aplikasi Sistem Informasi Laporan Diskominfo Dan Sandi Aceh

Along with the increasing need for information systems in the Districts/Cities of Aceh Province in supporting the development process of a region, the penetration test and evaluation of the system has been delayed. This is due to the limited number of experts in the province as well as the level of dependence on human resources in Districts/Cities who have limited capacity in terms of testing information systems. Therefore, with the presence of the latest web application information system at Diskominfo and Sandi Aceh, it requires system testing to determine the feasibility level of publication of the information system. The testing technique in this study used the VAPT Life Cycle method. Where the VAPT Life Cycle will identify, describe, assess vulnerabilities based on the CVSS (Common Vulnerability Scoring System) and provide solutions for handling vulnerabilities. The vulnerability discovery process in this study uses the Nessus Vulnerability Scanning tool. From the findings there are 4 vulnerabilities, 1 in the high category and 3 in the medium category. This vulnerability data can be used as evaluation material to close or fix existing security holes.

A model-based mode-switching framework based on security vulnerability scores

Software vulnerabilities can affect critical systems within an organization impacting processes, workflows, privacy, and safety. When a software vulnerability becomes known, affected systems are at risk until appropriate updates become available and eventually deployed. This period can last from a few days to several months, during which attackers can develop exploits and take advantage of the vulnerability. It is tedious and time-consuming to keep track of vulnerabilities manually and perform necessary actions to shut down, update, or modify systems. Vulnerabilities affect system components, such as a web server, but sometimes only target specific versions or component combinations.In this paper, we propose a novel approach for automated mode switching of software systems to support system administrators in dealing with vulnerabilities and reducing the risk of exposure. We rely on model-driven techniques and use a multi-modal architecture to react to discovered vulnerabilities and provide automated contingency support. We have developed a dedicated domain-specific language to describe potential mitigation as mode switches. We have evaluated our approach with a web server case study, analyzing historical vulnerability data. Based on the vulnerabilities scores sum, we demonstrated that switching to less vulnerable modes reduced the attack surface in 98.9% of the analyzed time.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Groundwater Risk Assessment Based on DRASTIC and Special Vulnerability of Solidified/Stabilized Heavy-Metal-Contaminated Sites

Solidification/stabilization technology is commonly used in the remediation of heavy-metal-contaminated sites, which reduces the leaching capacity of heavy metals, but the total amount of heavy metals in the soil is not reduced, there is still a risk of heavy metal re-release and contamination of groundwater, and the risk of groundwater contamination of solidified/stabilized heavy-metal-contaminated sites needs to be assessed. Through the analysis of the system structure of solidified/stabilized heavy-metal-contaminated sites, combined with the integration method of pollution sources—the vadose zone-aquifer, based on the DRASTIC model and the special vulnerability of the solidification/stabilization site, a groundwater pollution risk assessment index system including 4 influencing factors such as site hazard, pollutant hazard, aquifer vulnerability, and natural conditions and a total of 18 evaluation indexes was constructed. Each evaluation index was graded and assigned a scoring value combined with the Analytic Hierarchy Process (AHP) to calculate index weights. The comprehensive weights of site hazard, contaminant stability, aquifer vulnerability, and natural conditions were 0.1894, 0.3508, 0.3508, and 0.1090, respectively. The isometric method was used to classify the pollution risk into five risk levels (very low risk [0, 2), low risk [2, 4), medium risk [4, 6), high risk [6, 8), and very high risk [8, 10]), and a groundwater comprehensive index pollution risk assessment model was established. The model was applied to the actual site. The results showed that under the scenario of direct landfill of remediated soil, the comprehensive indexes of groundwater pollution risk for As and Cd were 4.55 and 4.58, respectively, both of which were medium risk. When the surrounding protective measures were supplemented, the comprehensive indexes of groundwater pollution risk for As and Cd were 3.98 and 4.02, respectively. Cd remained as medium risk and As as low risk. In both scenarios, the combined groundwater contamination risk index of Cd was greater than that of As because the contaminant stability of As was higher than that of Cd. The average percentage of aquifer vulnerability score reached 45.50%, which was higher than the weight of site inherent vulnerability of 35.08%, indicating that the original site hydrogeological conditions are fragile, groundwater is vulnerable to contamination, and the in situ landfill solidification/stabilization of soil is at risk. In order to further reduce the risk, the topographic slope was increased, thereby increasing the surface drainage capacity, which reduced the combined groundwater contamination risk index for As and Cd to 3.94 and 3.90, both of which were low risk. This study provides a new method for assessing the risk of groundwater contamination at solidified/stabilized heavy-metal-contaminated sites. It also has reference significance for selecting solidification/stabilization remediation parameters

Sensitivity analysis of indicator weights for the construction of flood vulnerability indexes: A participatory approach

The assessment of flood vulnerability is a complex task that involves numerous uncertainties. Within this context, sensitivity analyses are crucial to better understand the variability of vulnerability index outcomes according to different input parameters. The present study sheds light on the importance of assessing the sensitivity of different criteria weights to construct flood vulnerability indexes using the Maquiné basin (Brazil) as a case study. Specifically, we compared vulnerability scores based on weights derived from a participatory survey with 44 stakeholders to those based on an equal weighting scheme. Results helped us identify areas with low and high uncertainty and the variables contributing to this. Overall, the preference for indicator weights did not vary significantly among stakeholders with distinct socioeconomic characteristics. Furthermore, the choice of weights only had an impact on the spatial distribution of flood vulnerability in certain regions. Compared to equal weights, the flood vulnerability outcomes obtained by averaging the stakeholder scenarios were similar, indicating that the results were robust and not highly sensitive to the choice of weights. By adopting a participatory approach, we were able to consider multiple stakeholders' views, which helped to provide a more comprehensive perspective of flood vulnerability and potentially increased the acceptance of the results. Based on our findings, end-users can better understand the relative importance of each indicator and how they contribute to vulnerability. Furthermore, our results can help identify points where stakeholders disagree, which can be used to facilitate dialogue and consensus building. The methodology applied is straightforward and could be easily adapted to other multi-criteria decision-making problems.

Support for the Vulnerability Management Process Using Conversion CVSS Base Score 2.0 to 3.x.

COVID-19 forced a number of changes in many areas of life, which resulted in an increase in human activity in cyberspace. Furthermore, the number of cyberattacks has increased. In such circumstances, detection, accurate prioritisation, and timely removal of critical vulnerabilities is of key importance for ensuring the security of various organisations. One of the most-commonly used vulnerability assessment standards is the Common Vulnerability Scoring System (CVSS), which allows for assessing the degree of vulnerability criticality on a scale from 0 to 10. Unfortunately, not all detected vulnerabilities have defined CVSS base scores, or if they do, they are not always expressed using the latest standard (CVSS 3.x). In this work, we propose using machine learning algorithms to convert the CVSS vector from Version 2.0 to 3.x. We discuss in detail the individual steps of the conversion procedure, starting from data acquisition using vulnerability databases and Natural Language Processing (NLP) algorithms, to the vector mapping process based on the optimisation of ML algorithm parameters, and finally, the application of machine learning to calculate the CVSS 3.x vector components. The calculated example results showed the effectiveness of the proposed method for the conversion of the CVSS 2.0 vector to the CVSS 3.x standard.

An active defense model based on situational awareness and firewalls

SummaryWith the rapid development of the internet, cyberspace security issues have become increasingly prominent. The importance of constructing a cyberspace security system is self‐evident, but compared with attackers, defenders in cyberspace are in a castle‐like passive defense state in most cases. Therefore, building a reliable, accurate, timely, and active defense system is challenging. The key is to accurately focus on defense priorities, the anticipation of attackers who will likely succeed, and blocking attacks in a timely manner. In this article, we propose an active defense model based on the interaction of situational awareness and firewalls. First, by biasing the integrity, confidentiality, and availability of assets to get the score of assets, and using the Common Vulnerability Scoring System to assess the threat level of assets, we combine the two to determine the maximum system damage that the asset will suffer if it is lost, and then focus on defense. Meanwhile, log analysis of the network situational awareness platform can predict successful attackers, and then the linked firewall strategy can block these attacks in time before the attackers obtain attack gains. After that, we force the attackers to give up their attacks on the target by increasing the attack cost. We compared our model with iptables auto‐blocking and nginx auto‐blocking, and our model excelled them across the board in terms of comprehensiveness and false positive rate. The experimental results verify thar our active defense model proposed in this article can better reduce the defense cost and increase the attack cost, thus achieving the relatively defense goal.

Are there narcissistic career choices? An investigation of narcissistic traits and vocational interests

The present study focused on the relationships between vocational interests and 11 scales associated with narcissistic grandiosity and four scales associated with narcissistic vulnerability based on results from a convenience sample of 716 undergraduate business management students. Past research suggests vulnerable and grandiose narcissism differ significantly in their empirical correlates; thus, we expected scales capturing narcissistic grandiosity and narcissistic vulnerability to display diverging patterns of correlation with the vocational interest scales. Our results generally supported this prediction. Vulnerable narcissism scores generally had positive correlations with artistic, social, and applied interests. In contrast, grandiose narcissism scores had significant correlations with science and business interests and negative correlations with workstyle dimensions, such as accountability. Limitations and future research suggestions are discussed.

Cortisol reactivity to psychosocial stress in vulnerable and grandiose narcissists: An exploratory study.

Narcissistic personality manifests itself in at least two different forms: grandiose and vulnerable. In the present study, we compared cortisol and emotional responses to psychosocial stress between subjects high in vulnerable and grandiose narcissism scores, and examined possible associations between narcissism, other personality traits, and stress responses. We hypothesized that subjects with higher scores of vulnerable narcissism would show stronger emotional and physiological reactivity than those with high scores of grandiose narcissism. A final sample of forty-seven participants underwent a Trier Social Stress Test (TSST), provided saliva samples to assess cortisol levels, and completed several personality questionnaires. Consistent with our hypothesis, subjects with higher scores of vulnerable narcissism had a stronger cortisol and emotional response than those with high scores of grandiose narcissism. Vulnerable narcissism was positively correlated with schizotypal traits, while grandiose narcissism was positively correlated with psychopathic traits. Participants with a mixed-type of narcissism were also discussed. This study provides the first evidence of differential physiological and emotional reactivity to social evaluation threat according to scores of vulnerable and grandiose narcissism. Since this is an exploratory study, the results must be interpreted with caution. However, the results will be informative for future confirmatory research with larger and more heterogeneous samples.