Security Vulnerability Analyses of Large Language Models (LLMs) through Extension of the Common Vulnerability Scoring System (CVSS) Framework

Large Language Models (LLMs) have revolutionized Generative Artificial Intelligence (GenAI) tasks, becoming an integral part of various applications in society, including text generation, translation, summarization, and more. However, their widespread usage emphasizes the critical need to enhance their security posture to ensure the integrity and reliability of their outputs and minimize harmful effects. Prompt injections and training data poisoning attacks are two of the most prominent vulnerabilities in LLMs, which could potentially lead to unpredictable and undesirable behaviors, such as biased outputs, misinformation propagation, and even malicious content generation. The Common Vulnerability Scoring System (CVSS) framework provides a standardized approach to capturing the principal characteristics of vulnerabilities, facilitating a deeper understanding of their severity within the security and AI communities. By extending the current CVSS framework, we generate scores for these vulnerabilities such that organizations can prioritize mitigation efforts, allocate resources effectively, and implement targeted security measures to defend against potential risks.

Vulnerability assessment of UAV engine to laser based on improved shotline method

Laser anti-drone technology is entering the sequence of actual combat,and it is necessary to consider the vulnerability of typical functional parts of UAVs.Since the concept of"vulnerability"was proposed,a variety of analysis programs for battlefield targets to traditional weapons have been developed,but a comprehensive assessment methodology for targets'vulnerability to laser is still missing.Based on the shotline method,this paper proposes a method that equates laser beam to shotline array,an efficient vulnerability analysis program of target to laser is established by this method,and the program includes the circuit board and the wire into the vulnerability analysis category,which improves the precision of the vulnerability analysis.Taking the UAV engine part as the target of vulnerability analysis,combine with the"life-death unit method"to calculate the laser penetration rate of various materials of the UAV,and the influence of laser weapon system parameters and striking orientation on the killing probability is quantified after introducing the penetration rate into the vulnerability analysis program.The quantitative analysis method proposed in this paper has certain general expansibility,which can provide a fresh idea for the vulnerability analysis of other targets to laser.

A New Framework for Software Vulnerability Detection Based on an Advanced Computing

The detection of software vulnerabilities written in C and C++languages takes a lot of attention and interest today.This paper proposes a new framework called DrCSE to improve software vulnerability detection.It uses an intelligent computation technique based on the combination of two methods:Rebalancing data and representation learning to analyze and evaluate the code property graph(CPG)of the source code for detecting abnormal behavior of software vulnerabilities.To do that,DrCSE performs a combination of 3 main processing techniques:(i)building the source code feature profiles,(ii)rebalancing data,and(iii)contrastive learning.In which,the method(i)extracts the source code’s features based on the vertices and edges of the CPG.The method of rebalancing data has the function of supporting the training process by balancing the experimental dataset.Finally,contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers away.The experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabilities using the Verum dataset.As a result,the method proposed in the article has brought a pretty good performance in all metrics,especially the Precision and Recall scores of 39.35%and 69.07%,respectively,proving the efficiency of the DrCSE Framework.It performs better than other approaches,with a 5%boost in Precision and a 5%boost in Recall.Overall,this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date.

Systematic Security Guideline Framework through Intelligently Automated Vulnerability Analysis

This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities,generating applicable guidelines based on real-world software.The existing analysis of software security vulnerabilities often focuses on specific features or modules.This partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the software.The key novelty lies in overcoming the constraints of partial approaches.The proposed framework utilizes data from various sources to create a comprehensive functionality profile,facilitating the derivation of real-world security guidelines.Security guidelines are dynamically generated by associating functional security vulnerabilities with the latest Common Vulnerabilities and Exposure(CVE)and Common Vulnerability Scoring System(CVSS)scores,resulting in automated guidelines tailored to each product.These guidelines are not only practical but also applicable in real-world software,allowing for prioritized security responses.The proposed framework is applied to virtual private network(VPN)software,wherein a validated Level 2 data flow diagram is generated using the Spoofing,Tampering,Repudiation,Information Disclosure,Denial of Service,and Elevation of privilege(STRIDE)technique with references to various papers and examples from related software.The analysis resulted in the identification of a total of 121 vulnerabilities.The successful implementation and validation demonstrate the framework’s efficacy in generating customized guidelines for entire systems,subsystems,and selected modules.

A Review of Deep Learning-Based Vulnerability Detection Tools for Ethernet Smart Contracts

In recent years,the number of smart contracts deployed on blockchain has exploded.However,the issue of vulnerability has caused incalculable losses.Due to the irreversible and immutability of smart contracts,vulnerability detection has become particularly important.With the popular use of neural network model,there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts.This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts.Subsequently,it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection.These tools are categorized based on their open-source status,the data format and the type of feature extraction they employ.Then we conduct a comprehensive comparative analysis of these tools,selecting representative tools for experimental validation and comparing them with traditional tools in terms of detection coverage and accuracy.Finally,Based on the insights gained from the experimental results and the current state of research in the field of smart contract vulnerability detection tools,we suppose to provide a reference standard for developers of contract vulnerability detection tools.Meanwhile,forward-looking research directions are also proposed for deep learning-based smart contract vulnerability detection.

An Analysis of Liberia’s Vulnerability to Climate Change in the Context of Least Developed Countries (LDCs): A Review

Climate change is an alarming global challenge, particularly affecting the least developed countries (LDCs) including Liberia. These countries, located in regions prone to unpredictable temperature and precipitation changes, are facing significant challenges, particularly in climate-sensitive sectors such as mining and agriculture. LDCs need more resilience to adverse climate shocks but have limited capacity for adaptation compared to other developed and developing nations. This paper examines Liberia’s susceptibility to climate change as a least developed country, focusing on its exposure, sensitivity, and adaptive capacity. It provides an overview of LDCs and outlines the global distribution of carbon dioxide emissions. The paper also evaluates specific challenges that amplify Liberia’s vulnerability and constrain sustainable adaptation, providing insight into climate change’s existing and potential effects. The paper emphasizes the urgency of addressing climate impacts on Liberia and calls for concerted local and international efforts for effective and sustainable mitigation efforts. It provides recommendations for policy decisions and calls for further research on climate change mitigation and adaptation.

BArcherFuzzer:An Android System Services Fuzzier via Transaction Dependencies of BpBinder

By the analysis of vulnerabilities of Android native system services,we find that some vulnerabilities are caused by inconsistent data transmission and inconsistent data processing logic between client and server.The existing research cannot find the above two types of vulnerabilities and the test cases of them face the problem of low coverage.In this paper,we propose an extraction method of test cases based on the native system services of the client and design a case construction method that supports multi-parameter mutation based on genetic algorithm and priority strategy.Based on the above method,we implement a detection tool-BArcherFuzzer to detect vulnerabilities of Android native system services.The experiment results show that BArcherFuzzer found four vulnerabilities of hundreds of exception messages,all of them were confirmed by Google and one was assigned a Common Vulnerabilities and Exposures(CVE)number(CVE-2020-0363).

Application of Hazard Vulnerability Analysis Based on Kaiser Model in Neonatal Breast Milk Management

Objective:To analyze the existing risks in breast milk management at the neonatal department and provide corresponding countermeasures.Methods:22 risk events were identified in 7 risk links in the process of bottle-feeding of breast milk.Hazard Vulnerability Analysis based on the Kaiser model was applied to investigate and evaluate the risk events.Results:High-risk events include breast milk quality inspection,hand hygiene during collection,disinfection of collectors,cold chain management,hand hygiene during the reception,breast milk closed-loop management,and post-collection disposal.Root cause analysis of high-risk events was conducted and breast milk management strategies outside the hospital and within the neonatal department were proposed.Conclusion:Hazard Vulnerability Analysis based on the Kaiser model can identify and assess neonatal breast milk management risks effectively,which helps improve the management of neonatal breast milk.It is conducive to the safe development and promotion of bottle feeding of breast milk for neonates,ensuring the quality of medical services and the safety of children.

Spatial patterns of ecosystem vulnerability changes during 2001–2011 in the three-river source region of the Qinghai-Tibetan Plateau, China

The three-river source region (TRSR, including Yangtze, Yellow and Lancang rivers), located in the Qinghai-Tibetan Plateau, China, is a typical alpine zone with apparent ecosystem vulnerability and sensitivity. In this paper, we introduced many interdisciplinary factors, such as landscape pattern indices (Shannon diversity index and Shannon evenness index) and extreme climate factors (number of extreme high temperature days, number of extreme low temperature days, and number of extreme precipitation days), to establish a new model for evaluating the spatial patterns of ecosystem vulnerability changes in the TRSR. The change intensity (CI) of ecosystem vulnerability was also analyzed. The results showed that the established evaluation model was effective and the ecosystem vulnerability in the whole study area was intensive. During the study period of 2001–2011, there was a slight degradation in the eco-environmental quality. The Yellow River source region had the best eco-environmental quality, while the Yangtze River source region had the worst one. In addition, the zones dominated by deserts were the most severely deteriorated areas and the eco-environmental quality of the zones occupied by evergreen coniferous forests showed a better change. Furthermore, the larger the change rates of the climate factors (accumulative temperature of ≥10°C and annual average precipitation) are, the more intensive the CI of ecosystem vulnerability is. This study would provide a scientific basis for the eco-environmental protection and restoration in the TRSR.

Spatio-temporal Evolution of Marine Fishery Industry Ecosystem Vulnerability in the Bohai Rim Region

The building of the ocean power strategy and the implementation of the blue agriculture plan urgently need to strengthen the sustainable development of marine fishery.Taking vulnerability as the starting point, this paper constructs the vulnerability index system of marine fishery industry ecosystem from the aspects of sensitivity and response capacity, and combines the entropy method with the Topsis to comprehensively analyze the spatio-temporal evolution characteristics of vulnerability of marine fishery industry ecosystem in the Bohai Rim Region from 2001 to 2015.The results show that: 1) In the time dimension, from 2001 to 2015, the vulnerability of the marine fishery industry ecosystem in the Bohai Rim Region shows a fluctuant and degressive trend;2) In the spatial dimension, the spatial distribution of the marine fishery industry ecosystem vulnerability in the Bohai Rim Region presents the gradient characteristics which shows high vulnerability in the east and low vulnerability in the west.According to the evolution track of the system’s vulnerability level, the vulnerability of the marine fishery industry ecosystem is divided into ‘declining’ and ‘stable’ types of evolutionary structures;3) The development of marine fishery in the Bohai Rim Region needs to be derived from the marine fishery’s ecological environment and the industrial development mode and structure, which can improve the marine environment remediation efforts, optimize the marine fishery industry structure, vigorously focus on pelagic fishery, and enhance the introduction of marine fishery’s science and technology talents, etc.Then, the marine fishery’s development in the Bohai Rim Region will be moving in the green, circular and sustainable direction.

A methodology for damage evaluation of underground tunnels subjected to static loading using numerical modeling

We have proposed a methodology to assess the robustness of underground tunnels against potential failure.This involves developing vulnerability functions for various qualities of rock mass and static loading intensities.To account for these variations,we utilized a Monte Carlo Simulation(MCS)technique coupled with the finite difference code FLAC^(3D),to conduct two thousand seven hundred numerical simulations of a horseshoe tunnel located within a rock mass with different geological strength index system(GSIs)and subjected to different states of static loading.To quantify the severity of damage within the rock mass,we selected one stress-based(brittle shear ratio(BSR))and one strain-based failure criterion(plastic damage index(PDI)).Based on these criteria,we then developed fragility curves.Additionally,we used mathematical approximation techniques to produce vulnerability functions that relate the probabilities of various damage states to loading intensities for different quality classes of blocky rock mass.The results indicated that the fragility curves we obtained could accurately depict the evolution of the inner and outer shell damage around the tunnel.Therefore,we have provided engineers with a tool that can predict levels of damages associated with different failure mechanisms based on variations in rock mass quality and in situ stress state.Our method is a numerically developed,multi-variate approach that can aid engineers in making informed decisions about the robustness of underground tunnels.

Towards rapid and automated vulnerability classification of concrete buildings

With the overwhelming number of older reinforced concrete buildings that need to be assessed for seismic vulnerability in a city,local governments face the question of how to assess their building inventory.By leveraging engineering drawings that are stored in a digital format,a well-established method for classification reinforced concrete buildings with respect to seismic vulnerability,and machine learning techniques,we have developed a technique to automatically extract quantitative information from the drawings to classify vulnerability.Using this technique,stakeholders will be able to rapidly classify buildings according to their seismic vulnerability and have access to information they need to prioritize a large building inventory.The approach has the potential to have significant impact on our ability to rapidly make decisions related to retrofit and improvements in our communities.In the Los Angeles County alone it is estimated that several thousand buildings of this type exist.The Hassan index is adopted here as the method for automation due to its simple application during the classification of the vulnerable reinforced concrete buildings.This paper will present the technique used for automating information extraction to compute the Hassan index for a large building inventory.

Agricultural Vulnerability to Drought in China's Agro-pastoral Ecotone:A Case Study of Yulin City,Shaanxi Province

Agro-pastoral ecotone of northern China is the prominent area for agricultural production,but it is also the most typical ecological fragile area with frequent drought disasters.Taking Yulin City at Shaanxi Province in China as the case area,the paper aims to investigate the spatio-temporal changes of agricultural vulnerability to drought in China’s agro-pastoral ecotone in the period 2000 to2020.The results show that:1)the agricultural vulnerability to drought in Yulin City has shifted from high vulnerability in the period2000–2010 to low vulnerability in the period 2011–2020.2)There exist obvious spatio-temporal differences of the agricultural vulnerability to drought in Yulin City during the research period.3)Four sensitive events and 14 resilient events were identified in the research and the crops of Yulin had become more resilient to drought.Finally,the paper put forward with policy implications to make adaptive strategies of agriculture to climate change in China’s agro-pastoral ecotone in the future,e.g.,carrying out agricultural zoning based on agricultural production conditions,intensifying the construction of disaster prevention and relief system,and integrating with modern agricultural technology to develop new type agriculture.

SMINER:Detecting Unrestricted and Misimplemented Behaviors of Software Systems Based on Unit Test Cases

Despite the advances in automated vulnerability detection approaches,security vulnerabilities caused by design flaws in software systems are continuously appearing in real-world systems.Such security design flaws can bring unrestricted and misimplemented behaviors of a system and can lead to fatal vulnerabilities such as remote code execution or sensitive data leakage.Therefore,it is an essential task to discover unrestricted and misimplemented behaviors of a system.However,it is a daunting task for security experts to discover such vulnerabilities in advance because it is timeconsuming and error-prone to analyze the whole code in detail.Also,most of the existing vulnerability detection approaches still focus on detecting memory corruption bugs because these bugs are the dominant root cause of software vulnerabilities.This paper proposes SMINER,a novel approach that discovers vulnerabilities caused by unrestricted and misimplemented behaviors.SMINER first collects unit test cases for the target system from the official repository.Next,preprocess the collected code fragments.SMINER uses pre-processed data to show the security policies that can occur on the target system and creates a test case for security policy testing.To demonstrate the effectiveness of SMINER,this paper evaluates SMINER against Robot Operating System(ROS),a real-world system used for intelligent robots in Amazon and controlling satellites in National Aeronautics and Space Administration(NASA).From the evaluation,we discovered two real-world vulnerabilities in ROS.

Vulnerability assessment of coastal wetlands in Minjiang River Estuary based on cloud model under sea level rise

The change of coastal wetland vulnerability affects the ecological environment and the economic development of the estuary area.In the past,most of the assessment studies on the vulnerability of coastal ecosystems stayed in static qualitative research,lacking predictability,and the qualitative and quantitative relationship was not objective enough.In this study,the“Source-Pathway-Receptor-Consequence”model and the Intergovernmental Panel on Climate Change vulnerability definition were used to analyze the main impact of sea level rise caused by climate change on coastal wetland ecosystem in Minjiang River Estuary.The results show that:(1)With the increase of time and carbon emission,the area of high vulnerability and the higher vulnerability increased continuously,and the area of low vulnerability and the lower vulnerability decreased.(2)The eastern and northeastern part of the Culu Island in the Minjiang River Estuary of Fujian Province and the eastern coastal wetland of Meihua Town in Changle District are areas with high vulnerability risk.The area of high vulnerability area of coastal wetland under high emission scenario is wider than that under low emission scenario.(3)Under different sea level rise scenarios,elevation has the greatest impact on the vulnerability of coastal wetlands,and slope has less impact.The impact of sea level rise caused by climate change on the coastal wetland ecosystem in the Minjiang River Estuary is mainly manifested in the sea level rise,which changes the habitat elevation and daily flooding time of coastal wetlands,and then affects the survival and distribution of coastal wetland ecosystems.

Assessing the vulnerability of ecosystems to climate change based on climate exposure, vegetation stability and productivity

Background: Global warming has brought many negative impacts on terrestrial ecosystems, which makes the vulnerability of ecosystems one of the hot issues in current ecological research. Here, we proposed an assessment method based on the IPCC definition of vulnerability. The exposure to future climate was characterized using a moisture index(MI) that integrates the effects of temperature and precipitation. Vegetation stability, defined as the proportion of intact natural vegetation that remains unchanged under changing climate, was used together with vegetation productivity trend to represent the sensitivity and adaptability of ecosystems. Using this method, we evaluated the vulnerability of ecosystems in Southwestern China under two future representative concentration pathways(RCP 4.5 and RCP 8.5) with MC2 dynamic global vegetation model.Results:(1) Future(2017–2100) climate change will leave 7.4%(under RCP 4.5) and 57.4% of(under RCP 8.5) of areas under high or very high vulnerable climate exposure;(2) in terms of vegetation stability, nearly 45% of the study area will show high or very high vulnerability under both RCPs. Beside the impacts of human disturbance on natural vegetation coverage(vegetation intactness), climate change will cause obvious latitudinal movements in vegetation distribution, but the direction of movements under two RCPs were opposite due to the difference in water availability;(3) vegetation productivity in most areas will generally increase and remain a low vulnerability in the future;(4) an assessment based on the above three aspects together indicated that future climate change will generally have an adverse impact on all ecosystems in Southwestern China, with non-vulnerable areas account for only about 3% of the study area under both RCPs. However, compared with RCP 4.5, the areas with mid-and highvulnerability under RCP 8.5 scenario increased by 13% and 16%, respectively.Conclusion: Analyses of future climate exposure and projected vegetation distribution indicate widespread vulnerability of ecosystems in Southwestern China, while vegetation productivity in most areas will show an increasing trend to the end of twenty-first century. Based on new climate indicators and improved vulnerability assessment rules, our method provides an extra option for a more comprehensive evaluation of ecosystem vulnerability, and should be further tested at larger spatial scales in order to provide references for regional, or even global, ecosystem conservation works.

Geotechnical and GIS-based environmental factors and vulnerability studies of the Okemesi landslide,Nigeria

Landslide is a geological hazard typically associated with extreme events such as earthquakes,heavy rainfall,volcanic eruptions,changes in groundwater level,etc.This study was carried out in Okemesi-Ekiti(also known as Okemesi),Southwest Nigeria,with the purpose of using remote sensing and GIS technologies to analyze the environmental factors(grain size,direct shear strength resistance,rainfall data,wet density,surface,and slope)resulting in the occurrence of the Okemesi landslide.The study also aimed to conduct a vulnerability analysis in the study area to identify regions with a probability of landslide occurrence.The grain size analysis of the soil in the Okemesi landslide area showed that slope materials comprised 17.14%gravel,59.31%sand,and 19.48%fines,thus the soil type could be classified as poorly graded gravely sand with a high possibility of landslide occurrence.The geomorphic characteristics of the study area was characterized by slopes ranging from 0.00°to 49.00°,while most slopes in the area were less than 8.00°.The slope aspect direction was mainly in south(157.51°–202.50°),southwest(202.51°–247.50°),west(247.51°–292.50°),and north(0.00°–22.50°and 337.51°–360.00°).The highlands were primarily bounded by the slope directions of north(0.00°–22.50°and 337.51°–360.00°),northeast(22.51°–67.50°),east(67.51°–112.51°),and southeast(112.51°–157.50°),which indicated the potential direction of mass movement.The study area can be divided into three vulnerability zones:high,medium,and low,with the area percentages of 9.00%,61.80%,and 29.20%,respectively.The analysis suggested that the Okemesi landslide was likely triggered by rainfall,which might have weakened the physical structure of slope materials.Understanding the causes and impacts of landslides is crucial for policymakers to implement measures to mitigate landslide hazards,protect infrastructure,and prevent the loss of life in the landslide-prone regions.

Machine Learning-Based Efficient Discovery of Software Vulnerability for Internet of Things

With the development of the 5th generation of mobile communi-cation(5G)networks and artificial intelligence(AI)technologies,the use of the Internet of Things(IoT)has expanded throughout industry.Although IoT networks have improved industrial productivity and convenience,they are highly dependent on nonstandard protocol stacks and open-source-based,poorly validated software,resulting in several security vulnerabilities.How-ever,conventional AI-based software vulnerability discovery technologies cannot be applied to IoT because they require excessive memory and com-puting power.This study developed a technique for optimizing training data size to detect software vulnerabilities rapidly while maintaining learning accuracy.Experimental results using a software vulnerability classification dataset showed that different optimal data sizes did not affect the learning performance of the learning models.Moreover,the minimal data size required to train a model without performance degradation could be determined in advance.For example,the random forest model saved 85.18%of memory and improved latency by 97.82%while maintaining a learning accuracy similar to that achieved when using 100%of data,despite using only 1%.

Vulnerability and adaptation of an oasis social–ecological system affected by glacier change in an arid region of northwestern China

The Hexi Inland River Basin in an arid region of northwestern China was chosen as the study area for this research. The authors define the vulnerability of an oasis social-ecological system to glacier change; select 16 indicators from natural and socioeconomic systems according to exposure, sensitivity, and adaptive capacity; and construct a vulnerability-assessment indicator system aimed at an inland river basin in the arid region of Northwestern China. Vulnerability of the oasis socialecological system affected by glacier change in the study area is evaluated by Spatial Principal Component Analysis(SPCA) under the circumstance of glacier change. The key factors affecting the vulnerability are analyzed. The vulnerability of the oasis social-ecological system in the Hexi Inland River Basin affected by glacier change is of more than medium grade, accounting for about 48.0% of the total number of counties in the study area. In terms of the spatial pattern of the vulnerability, the oasis economic belt is the most vulnerable. With the rapid development of the area's society and economy, the exposure of the system to glacial changes is significantly increased; and an increase in glacial meltwater is not enough to overcome the impact of increased exposure, which is the main reason for the high vulnerability. Based on the result of the vulnerability analysis and combined with the present industrial structure in the Hexi Inland River Basin, near-,medium-, and long-term adaptation initiatives are put forward in the article.

Characterizing Vulnerability of Crop-Based Rural Systems to Climate Change and Variability: Agro-Ecology Specific Empirical Evidence from the Dabus Watershed, North-West Ethiopia

Climate change is impacting climate sensitive rural livelihood systems. Exposure, sensitivity, and adaptive capacity of agricultural livelihoods to climate variability and change differ across agro-ecologies and these pose a challenge to climate resilient development strategy. This study assesses agro-ecology specific vulnerability of smallholder farmers to climate change and variability in the Dabus Watershed (North-west Ethiopia), based on a survey of 734 farm households complemented with focus group discussion and key informant interviews. Recognizing the physiographic and climatic diversity that exists across agro-ecologies in the study area, Livelihood Vulnerability Index (LVI) framed within the United Nations Intergovernmental Panel on Climate Change (IPCC) vulnerability framework (LVI-IPCC) is adapted to assess agro-ecology specific vulnerability in two local agro-ecologies, namely wet lowland and dry lowland. For each agro-ecology, exposure, sensitivity and adaptive capacity indices as well as LVI-IPCC vulnerability score was calculated. The result shows that the dry lowland agro-ecology has a relatively higher exposure and sensitivity to climate stresses with a comparatively limited adaptive capability. On the other hand, the wet lowland agro-ecology exhibits intermediate vulnerability with a relatively lower perceived exposure and higher adaptive capacity. Higher exposure relative to adaptive capacity resulted in a positive LVI-IPCC score in the dry lowland agro-ecology and positioned it in more vulnerable level than the wet lowland. A higher adaptive capacity relative to exposure unveils a negative LVI-IPCC score for the wet lowland agro-ecology and positioned it in a moderate vulnerability category. In line with the findings, there is a need to set agro-ecology specific priorities for intervention that is most needed to cop up with the effects of climate variability and change in each agro-ecology. Climate risk exposure levels can be reduced through timely provision of climate specific information and early warning systems aimed at enhancing preparedness of farm households to extreme events. It is also crucial to expand availability of infrastructural facilities such as market, health services, and veterinary services so as to enhance adaptive capacity. Supporting alternative livelihood options and enhancing water harvesting practices for supplementary irrigation also call policy attention.