Class Of Vulnerabilities Research Articles

Vulnerability of ground water to polution in the highlands by a combined approach of AHP method and remote sensing

The aim of this work is to develop a simple and convenient decision support tool for the highlands using a combined approach of the Analytic Hierarchy Process (AHP) method and remote sensing. The vulnerability assessment method developed is called O3P (Highlands method). It is a parametric method, adapted in the highlands and implement in the town of Bafoussam. This method takes into account 4 factors, including land use (O), permeability (P1), slope (P2) and water depth in the piezometers (P3). The AHP multi-criteria analysis coupled with geographic information systems (GIS) and the analysis of nitrate pollution, allowed the development and validation of the O3P method. The results obtained show 3 classes of vulnerabilities which evolve from the low vulnerability class (20.22% of the study area) to the classes of medium vulnerability (50.16 %) and high vulnerability (29.62 %). Classes of high vulnerability to pollution are more present in the central-northern part of the study area, with some occurrences towards the south. The superposition of the highest nitrate concentrations with the areas of high vulnerability confirms the validity of the method. It emerges from it following the study of the sensitivity that the land occupation factor has the most important effect in this model and whose reduction would influence to 35.54% the method. The statistical study shows that this method is acceptable because the value of the standard deviations of the IVO3P lower than that its mean.

A Systematic Study about the Vulnerabilities and Security Enhancement in Block Chain Technology

In this paper we undertake a methodical analysis of the Block Chain system's vulnerabilities and examine security upgrade strategies that could suggest future directions for investigation into Block Chain technology and its applications. Smart contracts are self-executing programs that store cryptocurrency valued at billions of SEK on second-generation block chains like Ethereum. Once they are put into place, they cannot be changed. Although smart contracts are typically thought to be secure objects, a new class of vulnerabilities that are more likely to be ethical aspects of programming than software coding flaws are revealed by a methodical examination of technology and source code.

Evaluation of Smart Contract Vulnerability Analysis Tools: A Domain-Specific Perspective

With the widespread adoption of blockchain platforms across various decentralized applications, the smart contract’s vulnerabilities are continuously growing and evolving. Consequently, a failure to optimize conventional vulnerability analysis methods results in unforeseen effects caused by overlooked classes of vulnerabilities. Current methods have difficulty dealing with multifaceted intrusions, which calls for more robust approaches. Therefore, overdependence on environment-defined parameters in the contract execution logic binds the contract to the manipulation of such parameters and is perceived as a security vulnerability. Several vulnerability analysis tools have been identified as insufficient to effectively identify certain types of vulnerability. In this paper, we perform a domain-specific evaluation of state-of-the-art vulnerability detection tools on smart contracts. A domain can be defined as a particular area of knowledge, expertise, or industry. We use a perspective specific to the area of energy contracts to draw logical and language-dependent features to advance the structural and procedural comprehension of these contracts. The goal is to reach a greater degree of abstraction and navigate the complexities of decentralized applications by determining their domains. In particular, we analyze code embedding of energy smart contracts and characterize their vulnerabilities in transactive energy systems. We conclude that energy contracts can be affected by a relatively large number of defects. It also appears that the detection accuracy of the tools varies depending on the domain. This suggests that security flaws may be domain-specific. As a result, in some domains, many vulnerabilities can be overlooked by existing analytical tools. Additionally, the overall impact of a specific vulnerability can differ significantly between domains, making its mitigation a priority subject to business logic. As a result, more effort should be directed towards the reliable and accurate detection of existing and new types of vulnerability from a domain-specific point of view.

ObNoCs : Protecting Network-on-Chip Fabrics Against Reverse-Engineering Attacks

Modern System-on-Chip designs typically use Network-on-Chip (NoC) fabrics to implement coordination among integrated hardware blocks. An important class of security vulnerabilities involves a rogue foundry reverse-engineering the NoC topology and routing logic. In this paper, we develop an infrastructure, ObNoCs , for protecting NoC fabrics against such attacks. ObNoCs systematically replaces router connections with switches that can be programmed after fabrication to induce the desired topology. Our approach provides provable redaction of NoC functionality: switch configurations induce a large number of legal topologies, only one of which corresponds to the intended topology. We implement the ObNoCs methodology on Intel Quartus™ Platform, and experimental results on realistic SoC designs show that the architecture incurs minimal overhead in power, resource utilization, and system latency.

A unit-based symbolic execution method for detecting memory corruption vulnerabilities in executable codes

Memory corruption is a serious class of software vulnerabilities, which requires careful attention to be detected and removed from applications before getting exploited and harming the system users. Symbolic execution is a well-known method for analyzing programs and detecting various vulnerabilities, e.g., memory corruption. Although this method is sound and complete in theory, it faces some challenges, such as path explosion, when applied to real-world complex programs. In this paper, we present a method for improving the efficiency of symbolic execution and detecting four classes of memory corruption vulnerabilities in executable codes, i.e., heap-based buffer overflow, stack-based buffer overflow, use-after-free, and double-free. We perform symbolic execution only on test units rather than the whole program to lower the chance of path explosion. In our method, test units are considered parts of the program’s code, which might contain vulnerable statements and are statically identified based on the specifications of memory corruption vulnerabilities. Then, each test unit is symbolically executed to calculate path and vulnerability constraints for each statement of the unit, which determine the conditions on unit input data for executing that statement or activating vulnerabilities in it, respectively. Solving these constraints gives us input values for the test unit, which execute the desired statements and reveal vulnerabilities in them. Finally, we use machine learning to approximate the correlation between system and unit input data. Thereby, we generate system inputs that enter the program, reach vulnerable instructions in the desired test unit, and reveal vulnerabilities in them. This method is implemented as a plug-in for angr framework and evaluated using a group of benchmark programs. The experiments show its superiority over similar tools in accuracy and performance.

VDoTR: Vulnerability detection based on tensor representation of comprehensive code graphs

Code vulnerability detection has long been a critical issue due to its potential threat to computer systems. It is imperative to detect source code vulnerabilities in software and remediate them to avoid cyber attacks. To automate detection and reduce labor costs, many deep learning-based methods have been proposed. However, these approaches have been found to be either ineffective in detecting multiple classes of vulnerabilities or limited by treating original source code as a natural language sequence without exploiting the structural information of code. In this paper, we propose VDoTR, a model that leverages a new tensor representation of comprehensive code graphs, including AST, CFG, DFG, and NCS, to detect multiple types of vulnerabilities. Firstly, a tensor structure is introduced to represent the structured information of code, which deeply captures code features. Secondly, a new Circle Gated Graph Neural Network (CircleGGNN) is designed based on tensor for hidden state embedding of nodes. CircleGGNN can perform heterogeneous graph information fusion more directly and effectively. Lastly, a 1-D convolution-based output layer is applied to hidden embedding features for classification. The experimental results demonstrate that the detection performance of VDoTR is superior to other approaches with higher accuracy, precision, recall, and F1-measure on multiple datasets for vulnerability detection. Moreover, we illustrate which code graph contributes the most to the performance of VDoTR and which code graph is more sensitive to represent vulnerability features for different types of vulnerabilities through ablation experiments.

SeVNoC: Security Validation of System-on-Chip Designs With NoC Fabrics

Modern System-on-Chip (SoC) designs include a variety of Network-on-Chip (NoC) fabrics to implement coordination and communication of integrated hardware intellectual property (IP) blocks. An important class of security vulnerabilities involves a rogue hardware IP interfering with this communication to compromise the integrity of the system. Such interference includes message mutation, misdirection, delivery prevention, or IP masquerading, among others. In this article, we propose a scalable RTL-level SoC validation scheme, SeVNoC, for the systematic detection of security violations in inter-IP communications for SoC designs with NoC fabrics. Given a target security property to be validated, SeVNoC entails extraction of the control-flow graph of the relevant SoC, which is analyzed through a security property-based model comparison, without incurring state-space explosion. Our experiments on full-scale realistic SoC designs with multiple IPs and NoC architecture indicate that SeVNoC detects security violations in NoC communications with near-perfect accuracy, within only a few minutes.

Automated Generation of Security Assertions for RTL Models

System-on-Chip (SoC) security is vital in designing trustworthy systems. Detecting and fixing a vulnerability in the early stages is easier and cost-effective. Assertion-based verification is widely used for functional validation of Register-Transfer Level (RTL) designs. Assertions can improve the controllability and observability that can lead to faster error detection and localization. Although assertions are widely used for functional validation of RTL models, there is limited effort in applying assertions to detect SoC security vulnerabilities. Specifically, a fundamental challenge in SoC security and trust validation is how to develop high-quality security assertions. In this article, we perform automated vulnerability analysis of RTL models to generate security assertions for six classes of vulnerabilities. Experimental results show that the generated security assertions can detect a wide variety of vulnerabilities. Our automated framework can drastically reduce the overall security validation effort compared to the manual development of security assertions. Automated generation of security assertions will enable assertion-based verification to be one of the most promising pre-silicon security sign-off solutions.

Improving monolithic kernel security and robustness through intra-kernel sandboxing

The structure of commodity operating systems kernels remains largely unchanged despite radical changes in underlying hardware and security risks. Existing research has managed to increase overall monolithic kernel security using various defense mechanisms, such as kernel control-flow integrity, and through the use of active vulnerability discovery techniques such as system call fuzzing. However, these mitigation mechanisms often focus on a class of vulnerabilities while failing to address the broader, underlying architectural issues which amplify the impact of these issues.This paper presents a novel architectural approach that aims to increase the robustness and security of monolithic operating system kernels. We propose an operating system model which focuses on strict decomposition and runtime separation between individual monolithic kernel subsystems through separate execution contexts. We propose a novel, SMP-capable nested kernel architecture that enforces separation policies in an effective, efficient and mechanism-agnostic manner, complemented by a special compiler pass and a domain-specific language that provides a handy and intuitive way of specifying separation policies and automating their integration.We implement a prototype system based on the FreeBSD operating system and the Clang/LLVM compiler. We run a series of intense benchmarks to evaluate our model and separation mechanisms.

Threat model of the testing subsystem for rare mobile device owners

Testing mobile applications for owners of rare mobile devices is a complex task because of the diversity of devices. The goal of this study is to create a threat model of the testing subsystem for owners of sparse mobile devices. The authors of the paper describe step by step the classes of users and the key components of the subsystem. The article defines the level of security and the dominant classes of subsystem vulnerabilities. The authors created an intruder model for the testing subsystem, defined the security class, and planned measures to protect the testing subsystem.

Maximizing the Security Level of Real-Time Software While Preserving Temporal Constraints

Embedded computing systems are becoming increasingly relevant in the Internet of Things (IoT) and edge computing domains, where they are often employed as the control entity of a cyber-physical system. When operating in such interconnected domains, a software system is susceptible to cyber-attacks from external agents, which can compromise the correct behavior of the system. In addition, the software executing in these systems is typically characterized by stringent timing constraints, which must be satisfied during system execution. Enabling software protections to enhance the security level of the embedded software comes at the cost of increasing the computation times of the tasks, introducing the risk of deadline misses that could also jeopardize the system behavior. This paper presents a methodology to optimize the security level of real-time software while preserving system-wide schedulability by leveraging timing analysis. The proposed approach is based on a mixed-integer linear programming (MILP) formulation that maximizes the security level of the tasks and integrates a response-time analysis technique to assess the schedulability of the system whenever additional protections are activated to shield the software against cyber-attacks targeting specific classes of vulnerabilities. An experimental evaluation is presented to assess the performance of the proposed approach on a representative set of tasks included in standard benchmarking suites for embedded software.

LBODP077 The Therapeutic Potential Of Targeting Histone Deacetylase For Pituitary Neuroendocrine Tumors By High-throughout Drug Screening

Abstract Background A growing awareness regarding all molecular mechanisms involved in both tumor shrinkage and hormonal control might allow for medical treatment of pituitary neuroendocrine tumors (PitNETs), especially macroadenomas, rather than surgery. However, there are still no effectively therapeutic targets and corresponding drugs for all subtypes of PitNETs. Methods To identify druggable cell-intrinsic vulnerabilities and correspondingly promising therapeutic agents, we examined 3 PitNETs cell lines and 21 PitNETs patient-derived primary cell cultures in sequential quantitative high-throughput screens (HTS) of 2149 FDA-approved and bio-active targeted drugs. Furthermore, we verified the most effective drug which targeted the class of mechanistic vulnerabilities, histone deacetylase (HDAC) in vitro and in vivo PitNETs models. Further RNA sequencing was employed to reveal underlying molecular mechanisms following treatment with the most effective HDAC inhibitor, panobinostat. Results The HTS effort generated a total of 55296 single-agent dose responses which were enriched among multiple inhibitors for relevant PitNETs oncogenic targets, including HDAC, Akt/mTOR, proteasome, mitogenactivated protein kinase (MEK), and phosphoinositide-3-kinase (PI3K). Inhibitors from these mechanistic classes demonstrated a relatively wide potency-range, with HDAC inhibitors being, on average, the most potent drug class. Further in vitro and In vivo testing in PitNETs models validated HDACi, especially panobinostat as a promising therapeutic approach. Transcriptional surveys revealed substantial alterations to the oxidative redox mediated by the Akt/mTOR1/4EBP1/Nuclear factor-E2-related factor 2 (Nrf2) antioxidant signaling pathway following treatment with panobinostat. Conclusion The current study revealed a class of newfound anti-tumor drugs, HDACi based on the HTS technology, which may be potential drugs for the treatment of PitNETs. Presentation: No date and time listed

Invisible Supply Chain Attacks Based on Trojan Source

A new class of vulnerabilities, called trojan source, has been recently discovered by Boucher and Anderson. This work describes the state of the art of known trojan source attacks, illustrates two new attack variants involving configuration files and Java code, and describes practical preventive measures.

Statically Detecting Vulnerabilities by Processing Programming Languages as Natural Languages

Web applications continue to be a favorite target for hackers due to a combination of wide adoption and rapid deployment cycles, which often lead to the introduction of high-impact vulnerabilities. Static analysis tools are important to search for vulnerabilities automatically in the program source code, supporting developers on their removal. However, building these tools requires programming the knowledge on how to discover the vulnerabilities. This article presents an alternative approach in which tools <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learn</i> to detect flaws automatically by resorting to artificial intelligence concepts, more concretely to natural language processing. The approach employs a sequence model to learn to characterize vulnerabilities based on an annotated corpus. Afterwards, the model is utilized to discover and identify vulnerabilities in the source code. It was implemented in the DEKANT tool and evaluated experimentally with a large set of PHP applications and WordPress plugins. Overall, we found several thousand vulnerabilities belonging to 15 classes of input validation vulnerabilities, where 4143 of them were zero-day.

Safer at any speed: automatic context-aware safety enhancement for Rust

Type-safe languages improve application safety by eliminating whole classes of vulnerabilities–such as buffer overflows–by construction. However, this safety sometimes comes with a performance cost. As a result, many modern type-safe languages provide escape hatches that allow developers to manually bypass them. The relative value of performance to safety and the degree of performance obtained depends upon the application context, including user goals and the hardware upon which the application is to be executed. Since libraries may be used in many different contexts, library developers cannot make safety-performance trade-off decisions appropriate for all cases. Application developers can tune libraries themselves to increase safety or performance, but this requires extra effort and makes libraries less reusable. To address this problem, we present NADER, a Rust development tool that makes applications safer by automatically transforming unsafe code into equivalent safe code according to developer preferences and application context. In end-to-end system evaluations in a given context, NADER automatically reintroduces numerous library bounds checks, in many cases making application code that uses popular Rust libraries safer with no corresponding loss in performance.

An Optimistic Security Model for Improving Cyber Security Using Adaptive Algorithms to Prevent SQL Injection Attacks

Throughout today's economic and social life, the use of web-based services (such as e-commerce, online banking, and web-based communications, to name a few) has become a common habit. Countless applications operate worldwide on millions of servers, and their numbers are steadily increasing. It has become a focus of attackers and hackers for the attacks because of the huge growth of internet usage. It is necessary for all companies to develop and protect their applications in order to maintain their credibility and keep their products relevant for users. Web applications have brought in new classes of computer security vulnerabilities, such as SQL injection (SQLIA), and it has exceeded previously prominent vulnerability classes in recent years. SQL injection is the instance of the broader class of vulnerabilities that are based on input validation. The primary purpose of this research is to study the vulnerabilities of SQL injection and to propose an optimistic security model for secure data transmission. In this work, the authors proposed an adaptive algorithm to prevent SQL injections.

A Cybersecurity Moonshot

Cybersecurity needs radical rethinking to change its current landscape. This article charts a vision for a cybersecurity moonshot based on radical but feasible technologies that can prevent the largest classes of vulnerabilities in modern systems.

An Empirical Analysis of Hazardous Uses of Android Shared Storage

Android shared storage is shared with all the applications (apps for short) and the user. It is common to see that a large amount of apps store different kinds of files on it. It is well known that apps granted the read or write permissions can freely access any files in the shared storage. As a consequence, the shared storage has been demonstrated to expose sensitive information and jeopardize users’ privacy. In this paper, we systematically study a simple but overlooked threat related to the shared storage—the lack of input validation (e.g., integrity verifications) when consuming files on the shared storage. We argue that the untrusted input from the shared storage is a much ubiquitous problem. By undertaking an empirically study through a static analysis tool we develop, we find over 30 percent of the 13,746 analyzed popular apps on the market suffer from such problem. By investigating the types of files consumed, we find shockingly a large fraction of apps store and consume sensitive files, which allows us to construct end-to-end attacks. Considering the ubiquity of this class of vulnerabilities, we finally define better access control policies for external storage to eliminate them for most apps.

Vulvet

Data security and privacy of Android users is one of the challenging security problems addressed by the security research community. A major source of the security vulnerabilities in Android apps is attributed to bugs within source code, insecure APIs, and unvalidated code before performing sensitive operations. Specifically, the major class of app vulnerabilities is related to the categories such as inter-component communication (ICC), networking, web, cryptographic APIs, storage, and runtime-permission validation. A major portion of current contributions focus on identifying a smaller subset of vulnerabilities. In addition, these methods do not discuss how to remove detected vulnerabilities from the affected code. In this work, we propose a novel vulnerability detection and patching framework, Vulvet , which employs static analysis approaches from different domains of program analysis for detection of a wide range of vulnerabilities in Android apps. We propose an additional light-weight technique, FP-Validation, to mitigate false positives in comparison to existing solutions owing to over-approximation. In addition to improved detection, Vulvet provides an automated patching of apps with safe code for each of the identified vulnerability using bytecode instrumentation. We implement Vulvet as an extension of Soot. To demonstrate the efficiency of our proposed framework, we analyzed 3,700 apps collected from various stores and benchmarks consisting of various weak implementations. Our results indicate that Vulvet is able to achieve vulnerability detection with 95.23% precision and 0.975 F-measure on benchmark apps; a significant improvement in comparison to recent works along with successful patching of identified vulnerabilities.

Loss of heterozygosity of essential genes represents a widespread class of potential cancer vulnerabilities

Alterations in non-driver genes represent an emerging class of potential therapeutic targets in cancer. Hundreds to thousands of non-driver genes undergo loss of heterozygosity (LOH) events per tumor, generating discrete differences between tumor and normal cells. Here we interrogate LOH of polymorphisms in essential genes as a novel class of therapeutic targets. We hypothesized that monoallelic inactivation of the allele retained in tumors can selectively kill cancer cells but not somatic cells, which retain both alleles. We identified 5664 variants in 1278 essential genes that undergo LOH in cancer and evaluated the potential for each to be targeted using allele-specific gene-editing, RNAi, or small-molecule approaches. We further show that allele-specific inactivation of either of two essential genes (PRIM1 and EXOSC8) reduces growth of cells harboring that allele, while cells harboring the non-targeted allele remain intact. We conclude that LOH of essential genes represents a rich class of non-driver cancer vulnerabilities.