Critical Vulnerabilities Research Articles

VRKeyLogger: Virtual keystroke inference attack via eavesdropping controller usage pattern in WebVR

WebVR is an emerging technology that allows users to experience VR (Virtual Reality) through typical web browsers, providing an integrated environment for various VR applications. One important problem of the VR technology is how to securely interact with users, in particular, implementing secure text input. A promising approach is to use a virtual keyboard rendered as a VR object. The VR user can enter certain text by clicking a sequence of virtual keys through the VR controllers, and the input text is handled in a secure way. However, despite the sensitivity of the input text, we found that there is a critical vulnerability that the VR controllers are not properly protected. The VR controller status can be disclosed to malicious entities, imposing a severe threat that an attacker's website can infer the input text by eavesdropping and analyzing the VR controller's movements. To accurately infer the input, the attacker should address two challenges: 1) determining which clicks correspond to the virtual keyboard and 2) identifying which key is pressed. In this paper, we propose a new keystroke inference attack framework, VRKeyLogger, that addresses such challenges with two key components: key-click classifier and key-click identifier. The key-click classifier effectively distinguishes clicks on the virtual keyboard based on the SVM classifier trained by the major features of the VR controller uses. The key-click identifier then accurately identifies which key is pressed by transforming the clicked position into the local coordinate system of the virtual keyboard. We implemented a proof-of-concept prototype and conducted a user study with nine participants. In the extensive user study with three real-world WebVR applications, our VRKeyLogger results in classification and identification accuracy of 93.98 and 96.8% on average, respectively. This implies that the proposed attack poses a serious threat to WebVR security.

A High-Performance Algorithm and Tool for Detecting Critical Vulnerabilities

As web applications continue to advance and permeate various aspects of our lives, the prevalence of dangerous website security vulnerabilities, including SQL injection and cross-site scripting, poses a significant threat. These vulnerabilities create an enticing opportunity for hackers to exploit websites for personal gain, political motives, or to gain notoriety. While some research and commercial software have been developed to scan and identify these vulnerabilities, this paper introduces an efficient algorithmic approach and accompanying tool specifically designed for web security vulnerability detection. The proposed method addresses the need for accurate vulnerability detection by presenting a comprehensive study and tool. Through experimental evaluations, it is demonstrated that the algorithm achieves a high level of accuracy in detecting vulnerabilities. Moreover, the tool outperforms popular commercial software available in the market, boasting faster performance and the ability to identify less commonly known vulnerabilities such as shell injection or file inclusion.

Defining Strategic and Critical Vulnerabilities in Asymmetrical Trade Interdependence


 As the world becomes more economically integrated, a complex web of asymmetric interdependences has emerged, allowing some states to wield disproportionate economic power. Consequently, recourse to economic coercion as a tool for compellence, deterrence, or co-optation has become much more frequent in current times. Debates around dependence-induced strategic and critical vulnerabilities have thus gained traction with an end objective to reduce or mitigate them. But a lack of conceptual framework underpinning the ideas of dependence, vulnerabilities, and strategic and critical vulnerabilities plagues the present decision-making apparatus, which runs the risk of treating subjects under each of these categories as synonymous. To prevent a one-size-fits-all approach emanating from the lack of conceptual differentiation, this paper presents a framework, in the form of a series of tests, to understand whether trade in a certain commodity between countries can be classified as a critical vulnerability.

Guiding the retraining of convolutional neural networks against adversarial inputs.

When using deep learning models, one of the most critical vulnerabilities is their exposure to adversarial inputs, which can cause wrong decisions (e.g., incorrect classification of an image) with minor perturbations. To address this vulnerability, it becomes necessary to retrain the affected model against adversarial inputs as part of the software testing process. In order to make this process energy efficient, data scientists need support on which are the best guidance metrics for reducing the adversarial inputs to create and use during testing, as well as optimal dataset configurations. We examined six guidance metrics for retraining deep learning models, specifically with convolutional neural network architecture, and three retraining configurations. Our goal is to improve the convolutional neural networks against the attack of adversarial inputs with regard to the accuracy, resource utilization and execution time from the point of view of a data scientist in the context of image classification. We conducted an empirical study using five datasets for image classification. We explore: (a) the accuracy, resource utilization, and execution time of retraining convolutional neural networks with the guidance of six different guidance metrics (neuron coverage, likelihood-based surprise adequacy, distance-based surprise adequacy, DeepGini, softmax entropy and random), (b) the accuracy and resource utilization of retraining convolutional neural networks with three different configurations (one-step adversarial retraining, adversarial retraining and adversarial fine-tuning). We reveal that adversarial retraining from original model weights, and by ordering with uncertainty metrics, gives the best model w.r.t. accuracy, resource utilization, and execution time. Although more studies are necessary, we recommend data scientists use the above configuration and metrics to deal with the vulnerability to adversarial inputs of deep learning models, as they can improve their models against adversarial inputs without using many inputs and without creating numerous adversarial inputs. We also show that dataset size has an important impact on the results.

Adversarial robustness in deep neural networks based on variable attributes of the stochastic ensemble model.

Deep neural networks (DNNs) have been shown to be susceptible to critical vulnerabilities when attacked by adversarial samples. This has prompted the development of attack and defense strategies similar to those used in cyberspace security. The dependence of such strategies on attack and defense mechanisms makes the associated algorithms on both sides appear as closely processes, with the defense method being particularly passive in these processes. Inspired by the dynamic defense approach proposed in cyberspace to address endless arm races, this article defines ensemble quantity, network structure, and smoothing parameters as variable ensemble attributes and proposes a stochastic ensemble strategy based on heterogeneous and redundant sub-models. The proposed method introduces the diversity and randomness characteristic of deep neural networks to alter the fixed correspondence gradient between input and output. The unpredictability and diversity of the gradients make it more difficult for attackers to directly implement white-box attacks, helping to address the extreme transferability and vulnerability of ensemble models under white-box attacks. Experimental comparison of ASR-vs.-distortion curves with different attack scenarios under CIFAR10 preliminarily demonstrates the effectiveness of the proposed method that even the highest-capacity attacker cannot easily outperform the attack success rate associated with the ensemble smoothed model, especially for untargeted attacks.

SDN/NFV-based framework for autonomous defense against slow-rate DDoS attacks by using reinforcement learning

The unforeseen and skyrocketed shift in the number of connections to the Internet during the last years has created vast and critical vulnerabilities in networks that cybercriminals have quickly seized to launch high-volume DDoS attacks. Existing tools, such as advanced firewalls or intrusion prevention systems (IPS), cannot handle such an elevated volume of attacks because these solutions are dependent on humans. Therefore, adaptation of the current network security solutions to automated ones is more significant than ever to foster the development of the zero-touch networks and service management (ZSM) paradigm. Building on our preliminary work in this field, in this study, we provide a software-defined networking (SDN)-based framework that automates the detection and mitigation of slow-rate DDoS attacks. The framework uses deep learning (DL) to detect attacks and reinforcement learning (RL) to mitigate them. Furthermore, a network function virtualization (NFV)-assisted moving target defense (MTD) mechanism is included to amplify the effectiveness and flexibility of the solution. The framework is tested on a simulated network using open-source tools, namely Open Network Operating System (ONOS), Containernet, Apache Web Server, and Docker. The source code of a prototype of the framework is shared, which can be used and improved by interested researchers. Finally, the experimental results demonstrate that RL agents learn optimal DDoS mitigation policies in different scenarios and that they quickly adapt to new conditions that vary in short periods of time.

Fedlabx: a practical and privacy-preserving framework for federated learning

Federated learning (FL) draws attention in academia and industry due to its privacy-preserving capability in training machine learning models. However, there are still some critical security attacks and vulnerabilities, including gradients leakage and interference attacks. Meanwhile, communication is another bottleneck in basic FL schemes since large-scale FL parameter transmission leads to inefficient communication, latency, and slower learning processes. To overcome these shortcomings, different communication efficiency strategies and privacy-preserving cryptographic techniques have been proposed. However, a single method can only partially resist privacy attacks. This paper presents a practical, privacy-preserving scheme combining cryptographic techniques and communication networking solutions. We implement Kafka for message distribution, the Diffie–Hellman scheme for secure server aggregation, and gradient differential privacy for interference attack prevention. The proposed approach maintains training efficiency while being able to addressing gradients leakage problems and interference attacks. Meanwhile, the implementation of Kafka and Zookeeper provides asynchronous communication and anonymous authenticated computation with role-based access controls. Finally, we prove the privacy-preserving properties of the proposed solution via security analysis and empirically demonstrate its efficiency and practicality.

A socio-technical approach for the assessment of critical infrastructure system vulnerability in extreme weather events

A socio-technical approach for the assessment of critical infrastructure system vulnerability in extreme weather events

SECURING IOT HEALTHCARE APPLICATIONS AND BLOCKCHAIN: ADDRESSING SECURITY ATTACKS

The Internet of Things (IoT) describes the connection of bodily devices as "things" that can communicate with other systems and devices through the Internet and exchange statistics (data or information), facilitating the exchange of data with other systems and devices. These devices have sensors, software, and various components designed to exchange data seamlessly within the IoT network. Securing and protecting the data transmitted over the Internet from unauthorized access is imperative to ensuring the integrity and confidentiality of the information. IoT Smart health monitoring systems, integral components of the IoT landscape, are susceptible to various attacks. These include denial of service (DoS), fingerprint, router, select, forwarding, sensor, and replay attacks, all of which pose significant threats to the security of these systems. As such, there is a pressing need to address and mitigate the vulnerabilities associated with IoT healthcare applications. This paper aims to explore the significant role of IoT devices in healthcare systems and provide an in-depth review of attacks that threaten the security of IoT healthcare applications. The study analyses the existing literature on the vulnerabilities present in smart health monitoring systems and the potential application of blockchain technology as a robust solution to enhance the security of IoT healthcare applications. This research reveals critical vulnerabilities in IoT healthcare applications and highlights blockchain's effectiveness in mitigating them, providing insights for robust security measures and strategic decision-making in secure healthcare systems. This paper provides valuable insight and recommendations for policymakers, researchers, and practitioners involved in the domain of the IoT healthcare system.

Anaplastic thyroid cancer cells upregulate mitochondrial one-carbon metabolism to meet purine demand, eliciting a critical targetable vulnerability

Anaplastic thyroid cancer (ATC) is one of the most aggressive and lethal tumor types, characterized by loss of differentiation, epithelial-to-mesenchymal transition, extremely high proliferation rate, and generalized resistance to therapy. To identify novel relevant, targetable molecular alterations, we analyzed gene expression profiles from a genetically engineered ATC mouse model and from human patient datasets, and found consistent upregulation of genes encoding enzymes involved in the one-carbon metabolic pathway, which uses serine and folates to generate both nucleotides and glycine.Genetic and pharmacological inhibition of SHMT2, a key enzyme of the mitochondrial arm of the one-carbon pathway, rendered ATC cells glycine auxotroph and led to significant inhibition of cell proliferation and colony forming ability, which was primarily caused by depletion of the purine pool. Notably, these growth-suppressive effects were significantly amplified when cells were grown in the presence of physiological types and levels of folates. Genetic depletion of SHMT2 dramatically impaired tumor growth in vivo, both in xenograft models and in an immunocompetent allograft model of ATC.Together, these data establish the upregulation of the one-carbon metabolic pathway as a novel and targetable vulnerability of ATC cells, which can be exploited for therapeutic purposes.

Community-based natural hazard vulnerability assessment in rural Jamaica

ABSTRACT Disasters occur when natural hazards disrupt the livelihoods of a large enough number of vulnerable people such that they require outside assistance. Global, regional, and local influences accumulate into a building pressure of vulnerability that creates disasters when met by a natural hazard. Vulnerability to natural hazards, which incorporates numerous geophysical and social factors, varies by location and community. For Comfort Castle, a rural farming community in the mountains of eastern Jamaica, local vulnerability is connected to place, a history of colonialism and enslavement, and accessibility. To alleviate the building pressure of vulnerability in Comfort Castle, potential mitigation interventions are identified by using the Pressure and Release model to analyse the influence of geographic place. Using ethnography to identify key areas of vulnerability from the community perspective, this study finds that the Rio Grande Valley Road is a critical vulnerability factor and target for mitigation strategies. The road is the only access point to the community and is in poor condition. Geographic information systems (GIS) analyses of the road further estimate the impact of landslide hazards on Comfort Castle. Addressing access limitations would reduce multiple dimensions of vulnerability while also increasing local disaster recovery capacity and improving livelihoods.

An integrated cellular and molecular model of gastric neuroendocrine cancer evolution highlights therapeutic targets.

Gastric neuroendocrine carcinomas (G-NEC) are aggressive malignancies with poorly understood biology and a lack of disease models. Here, we use genome sequencing to characterize the genomic landscapes of human G-NEC and its histologic variants. We identify global and subtype-specific alterations and expose hitherto unappreciated gains of MYC family members in a large part of cases. Genetic engineering and lineage tracing in mice delineate a model of G-NEC evolution, which defines MYC as a critical driver and positions the cancer cell of origin to the neuroendocrine compartment. MYC-driven tumors have pronounced metastatic competence and display defined signaling addictions, as revealed by large-scale genetic and pharmacologic screening of cell lines and organoid resources. We create global maps of G-NEC dependencies, highlight critical vulnerabilities, and validate therapeutic targets, including candidates for clinical drug repurposing. Our study gives comprehensive insights into G-NEC biology.

Location selection of field hospitals amid COVID-19 considering effectiveness and fairness: A case study of Florida

The world has experienced an unprecedented global health crisis since 2020, the COVID-19 pandemic, which inflicted massive burdens on countries' healthcare systems. During the peaks of the pandemic, the shortages of intensive care unit (ICU) beds illustrated a critical vulnerability in the fight. Many individuals suffering the effects of COVID-19 had difficulty accessing ICU beds due to insufficient capacity. Unfortunately, it has been observed that many hospitals do not have enough ICU beds, and the ones with ICU capacity might not be accessible to all population strata. To remedy this going forward, field hospitals could be established to provide additional capacity in helping emergency health situations such as pandemics; however, location selection is a crucial decision ultimately for this purpose. As such, we consider finding new field hospital locations to serve the demand within certain travel-time thresholds, while accounting for the presence of vulnerable populations. A multi-objective mathematical model is proposed in this paper that maximizes the minimum accessibility and minimizes the travel time by integrating the Enhanced 2-Step Floating Catchment Area (E2SFCA) method and travel-time-constrained capacitated p-median model. This is performed to decide on the locations of field hospitals, while a sensitivity analysis addresses hospital capacity, demand level, and the number of field hospital locations. Four counties in Florida are selected to implement the proposed approach. Findings can be used to identify the ideal location(s) of capacity expansions concerning the fair distribution of field hospitals in terms of accessibility with a specific focus on vulnerable strata of the population.

Lines of Efforts in Order to Improve the Military Supply Chains Performance in Military Operations

Abstract Supply chain resilience plays a critical role in maintaining operational readiness and effectiveness in military operations. However, the unique challenges faced by military supply chains necessitate tailored approaches to enhance resilience. This paper presents a comprehensive framework for optimizing supply chain resilience in military operations. The proposed framework incorporates key elements such as risk assessment, contingency planning, information sharing, and resource allocation. It leverages advanced analytics techniques, including simulation modeling, optimization algorithms, and real-time data integration, to support decision-making and improve resilience. Results demonstrate that the optimized supply chain resilience strategy significantly enhances the military's ability to respond to disruptions, reduce recovery time, and minimize operational downtime. Moreover, the framework identifies critical vulnerabilities and recommends proactive measures to enhance system robustness. By adopting this framework, military organizations can better prepare for and respond to a wide range of supply chain disruptions, ultimately improving operational efficiency and mission success. The findings of this study contribute to the emerging field of military logistics by providing a systematic approach to optimize supply chain resilience and offering valuable insights for military decision-makers.

0209 Cardiovascular Measures Display Robust Phenotypic Stability to Total Sleep Deprivation Across Long-Duration Intervals

Abstract Introduction For the first time, we determined whether individual differences in cardiovascular (CV) measures are observed in response to total sleep deprivation as well as the stability of such responses across 4-month and 8-month long-duration studies. Methods We conducted a five-day experiment twice (at months 2 and 4) in a 4-month study (N=6 healthy adults; 3 females), and a similar five-day experiment three times (at months 2, 4, and 8) in an 8-month study (N=5 healthy adults; 2 females). During these repeated experiments, CV measures were collected via echocardiography or blood pressure monitor [systolic blood pressure (SBP)] at three assessment time points: 1) after two baseline 8h time in bed (TIB) nights (BL); 2) after a night of total sleep deprivation (TSD); and 3) after two recovery nights of 8-10h TIB (REC). Seated SBP, stroke volume (SV), heart rate (HR), cardiac index (CI), left ventricular ejection time (LVET), and systemic vascular resistance index (SVRI) were collected. Data did not significantly differ between the two studies and therefore were pooled together for analysis (N=11). Intraclass correlation coefficients (ICCs) assessed the stability of CV measures during TSD. Spearman’s rho assessed the relative rank of individuals’ averaged TSD-TSD responses across CV measures. Results TSD ICCs across months 2 and 4 were substantial to almost perfect for all CV measures (0.755-0.962). In the 8-month sample subset, TSD ICCs were moderate to almost perfect across 2, 4, and 8 months for all CV measures (0.643-0.944). Individuals also exhibited significant consistency of responses within CV measures: SV was positively correlated with LVET (ρ=0.791, P=0.004), HR was negatively correlated with LVET (ρ=-0.627, P=0.039) and CI was negatively correlated with SVRI (ρ=-0.936, P< 0.001). Conclusion Vulnerability to repeated TSD exposures showed trait-like stability in CV measures across months 2, 4, and 8, with significant consistency of responses within CV measures. For the first time, we demonstrate robust phenotypic stability of cardiovascular measures during total sleep deprivation across 2-month, 4-month and 8-month time points in long-duration studies. Our results herald the use of biomarkers and countermeasures for prediction and mitigation of this critical vulnerability. Support (if any) NASA grants NNX14AN49G and 80NSSC20K0243 (NG)

A Problem-tailored Adversarial Deep Neural Network-Based Attack Model for Feed-Forward Physical Unclonable Functions

With the exceeding advancement in technology, the sophistication of attacks is considerably increasing. Standard security methods fall short of achieving the security essentials of IoT against physical attacks due to the nature of IoTs being resource-constrained elements. Physical Unclonable Functions (PUFs) have been successfully employed as a lightweight memoryless solution to secure IoT devices. PUF is a device that exploits the integrated circuits’ inherent randomness originated during the fabrication process to give each physical entity a unique identifier. Nevertheless, because PUFs are vulnerable to mathematical clonability, Feed-Forward Arbiter PUF (FF PUF) was introduced to withstand potential attack methods. Motivated by the necessity to expose a critical vulnerability of the standard FF PUFs design, we introduce a problem-tailored adversarial model to attack FF PUF design using a carefully engineered loop-specific neural network-based design calibrated and trained using FPGA-based in-silicon implementation data to exhibit real-world attacking scenarios posed on FF PUFs, in addition to applying simulated data. The empirical results show that the proposed adversarial model adds outperforming results to the existing studies in attacking FF PUFs, manifesting the improved efficiency in breaking FF PUFs. We demonstrate our high-performing results in numerical experiments of language modeling using the deep Neural Networks method.

Critical vulnerabilities for diversion of controlled substances in the emergency department: Observations and healthcare failure mode and effect analysis

Objectives Drug theft by healthcare workers is a recognized problem in emergency departments (EDs) that can lead to patient, healthcare worker, and organization harm. Diversion takes various forms, including tampering with syringes, pilfering from waste containers and falsely documenting drug administration. Before implementing risk-mitigating interventions, we need a detailed understanding of the vulnerabilities in ED medication-use processes. This study sought to identify the critical failure modes (CFMs) within EDs that increase diversion risk and characterize the system factors contributing to CFMs. Methods Between June 2018 and February 2019, we conducted observations in two Ontario EDs. Observers recorded tasks carried out by nurses, pharmacists, and physicians. We performed a Healthcare Failure Mode and Effect Analysis, informed by the observation data, to proactively identify CFMs in the medication-use processes. Failure modes were coded for their effects on diversion risk and the contributing system factors. Results We identified 28 CFMs that increase diversion risk by enabling inappropriate access to controlled substances or compromising documentation. CFMs are multifactorial, stemming primarily from factors related to person (e.g., intent to divert) and tools/technology (e.g., limited automatic reconciliation of records), followed by organization (e.g., practices that diffuse accountability), environment (e.g., workspaces that obscure illicit behaviours), and task (e.g., unstructured processes leading to lapses). Conclusion The study findings inform opportunities to revise vulnerable processes and bolster safeguards, decreasing diversion risk and protecting patients and healthcare workers.

Endocannabinoid-dependent decrease of GABAergic transmission on dopaminergic neurons is associated with susceptibility to cocaine stimulant effects in pre-adolescent male MAOA hypomorphic mice exposed to early life stress

Vulnerability to cocaine use disorder depends upon a combination of genetic and environmental risk factors. While early life adversity is a critical environmental vulnerability factor for drug misuse, allelic variants of the monoamine oxidase A (MAOA) gene have been shown to moderate its influence on the risk of drug-related problems. However, data on the interactions between MAOA variants and early life stress (ES) with respect to predisposition to cocaine abuse are limited. Here, we show that a mouse model capturing the interaction of genetic (low-activity alleles of the Maoa gene; MAOANeo) and environmental (i.e., ES) vulnerability factors displays an increased sensitivity to repeated in vivo cocaine psychomotor stimulant actions associated with a reduction of GABAA receptor-mediated inhibition of dopamine neurons of the ventral tegmental area (VTA). Depolarization-induced suppression of inhibition (DSI), a 2-arachidonoylglycerol (2AG)-dependent form of short-term plasticity, also becomes readily expressed by dopamine neurons from male MAOANeo ES mice repeatedly treated with cocaine. The activation of either dopamine D2 or CB1 receptors contributes to cocaine-induced DSI expression, decreased GABA synaptic efficacy, and hyperlocomotion. Next, in vivo pharmacological enhancement of 2AG signaling during repeated cocaine exposure occludes its actions both in vivo and ex vivo. This data extends our knowledge of the multifaceted sequelae imposed by this gene-environment interaction in VTA dopamine neurons of male pre-adolescent mice and contributes to our understanding of neural mechanisms of vulnerability for early onset cocaine use.

Disaster Risk Assessment Scheme—A Road System Survey for Budapest

This study presents a method to analyze the most critical elements of the public road system concerning outer effects which hinder the normal operation of the whole system. The surveyed public road network in Budapest, Hungary is studied as a graph: Dijkstra’s algorithm is applied to find the shortest path, and the Boykov-Kolmogorov method is used to calculate the maximum flow of the network. Those elements are identified whose damage can critically influence the operation of the network, and where the infrastructure available for rescue teams has a bottleneck. Finally, the Wilcoxon post hoc test was applied with Bonferroni correction. The tests have proven that the new method can successfully identify the critical vulnerabilities of the network to determine its weak points by considering reduced road capacities and the increased needs for transportation arising due to a disaster. This pilot study confirmed that after the elimination of the problems in statistical methods, the new framework can robustly identify those road network elements whose development is of key importance from a disaster management perspective.

Abstract 3986: Novel LIPA targeted therapy for treating ovarian cancer

Abstract BACKGROUND: Ovarian cancer (OCa) is the deadliest of all gynecologic cancers in the United States. Currently approved therapies have improved OCa survival for clinically localized disease, however, the majority (~90%) of patients with high-grade serous OCa (HGSOC) experience relapse with incurable metastases. There is a dire need for new therapeutic approaches. We hypothesized that the high basal endoplasmic reticulum stress (ERS) in OCa represents a critical and targetable vulnerability and may overcome the tumor heterogeneity. The objective of this project is to exploit increased ERS in ovarian cancer cells by engaging the novel target LIPA using the unique compound ERX-41. METHODS: The utility of ERX-41 as a new therapy was evaluated using MTT and CellTiter-Glo Cell Viability Assays. We used multiple established and patient derived OCa cell lines. The effect of ERX-41 on the Cell viability of patient-derived organoids (PDO) was measured using CellTiter-Glo 3D Assay. Long term effects of ERX-41 on cell survival were measured using colony formation assays. Apoptosis was measured using Annexin V and Caspase-Glo® 3/7 Assays. Cell cycle analysis was analyzed by Flow Cytometry. Mechanistic studies were done using LIPA knockout (KO) cells, RT-qPCR, and western blotting. Status of LIPA in OCa was determined using TNMplot database. In vivo efficacy of ERX-41 was tested using both cell line derived (CDX) and patient derived (PDXs) xenografts. RESULTS: TNM plot results showed that LIPA is highly expressed in OCa tumors compared to normal tissues and LIPA expression correlated with clinical grade. Kaplan-Meier plotter analyses of TCGA data revealed that LIPA expression is negatively correlated with overall survival in OCa patients. MTT and CellTitre-Glo assay results showed that ERX-41 significantly reduced the cell viability of both established and primary OCa cells, and PDO’s with an IC50 of ~500nM. ERX-41 treatment also significantly reduced the cell survival, increased S-phase arrest, and promoted apoptosis of OCa cells. A time course study revealed a robust and consistent induction of ERS markers (CHOP and sXBP1) in OCa cells by ERX-41 within 4h. Western blotting analyses also confirmed increased expression of ERS markers including CHOP, elF2α, PERK, and ATF4 upon ERX-41 treatment confirming that ERX-41 induces ERS. In xenograft studies, ERX-41 treatment resulted in ~66% reduction of tumor volume measured by Xenogen-IVIS. Further, in studies using PDX tumors, treatment with ERX-41 resulted in a significant reduction (~60%) of tumor volume and tumor weight. CONCLUSION: Collectively, our results suggest that ERX-41 is a novel therapeutic agent that targets the LIPA with a unique mechanism of action and implicate ERX-41 binding to LIPA induces ER stress, and apoptosis of OCa cells. Further molecular characterization of how ERX-41 binding to LIPA induces ER stress in OCa cells is ongoing. Citation Format: Alexia B. Collier, Suryavathi Viswanadhapalli, Tae-Kyung Lee, Kara Kassees, Karla Parra, Gaurav Sharma, Tanner Reese, Michael Hsieh, Xihui Liu, Xue Yang, Behnam Ebrahimi, Uday P. Pratap, Rahul Gopalam, Chia Yuan Chen, Scott Terry Elmore, Gangadhara Reddy Sareddy, Edward R. Kost, Jung-Mo Ahn, Ganesh V. Raj, Ratna K. Vadlamudi. Novel LIPA targeted therapy for treating ovarian cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3986.