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All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Mining Bug Databases for Unidentified Software Vulnerabilities

Identifying software vulnerabilities is becoming more important as critical and sensitive systems increasingly rely on complex software systems. It has been suggested in previous work that some bugs are only identified as vulnerabilities long after the bug has been made public. These vulnerabilities are known as hidden impact vulnerabilities. This paper discusses the feasibility and necessity to mine common publicly available bug databases for vulnerabilities that are yet to be identified. We present bug database analysis of two well known and frequently used software packages, namely Linux kernel and MySQL. It is shown that for both Linux and MySQL, a significant portion of vulnerabilities that were discovered for the time period from January 2006 to April 2011 were hidden impact vulnerabilities. It is also shown that the percentage of hidden impact vulnerabilities has increased in the last two years, for both software packages. We then propose an improved hidden impact vulnerability identification methodology based on text mining bug databases, and conclude by discussing a few potential problems faced by such a classifier.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Multiple social platforms reveal actionable signals for software vulnerability awareness: A study of GitHub, Twitter and Reddit

Software vulnerabilities are flaws in computer systems that leave users open to attack. In many cases, these vulnerabilities go unnoticed and remain unresolved in codebases. Thus, awareness of software vulnerabilities among the public is crucial to ensure effective cybersecurity practices, the development of high quality software, and ultimately national security. This awareness can be better understood by studying the spread and evolution of software vulnerability discussions in online communities. This work is the first to evaluate and contrast how discussions about software vulnerabilities spread on three social platforms -- Twitter, GitHub, and Reddit. To lay the groundwork, we showcase a novel fundamental framework for measuring information spread that identifies the spread mechanisms and observables across platforms, the units of information, and the groups of measurements that can be applied to focus on a specific phenomena e.g., information cascades. We then analyze and contrast social network topologies for three example social networks and measure the scale and speed of the spread of discussion of specific vulnerabilities to understand how far and how widely they spread, how many users participate in discussions, and the duration of their spread. To demonstrate the awareness of more impactful software vulnerabilities, a subset of our analysis focuses on vulnerabilities targeted during recent major cyber attacks as well as vulnerabilities exploited by advanced persistent threat groups. We discover that usually, vulnerability discussions start on GitHub, before occurring on Twitter and Reddit. While studying how some user-level and content-level characteristics influence vulnerability spread, we observe that Twitter discussions started by users predicted to be humans have larger size, breadth, depth, adoption rate, lifetime, and structural virality compared to those started by users predicted to be bots. On Reddit, we contrast the differences in thread structure that originate from posts with positive, negative and neutral polarity. We find that posts that are positive have larger, deeper and wider discussions compared to negative and neutral posts. We anticipate the results of our analysis to not only increase the understanding of software vulnerability awareness but also inform models for simulating information spread across multiple social environments online.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

User Role Identification in Software Vulnerability Discussions over Social Networks

Understanding and early awareness of software vulnerabilities is vital for preventing and mitigating potential impacts from cybersecurity events. One step toward early characterization of software vulnerabilities may involve analyzing discussion and spread of information in online social networks. Prior work has used information from such discussions over multiple online forums to develop dynamic networks among users followed by analysis of structure, spread, and information evolution. In this work, we advance the state-of-the-art by focusing on data-driven learning of types, roles, and transition of roles exhibited by users over time. In social networks, users take on particular roles based on their actions and structure of the network. Identifying “meaningful” roles can help separate potential users of interest from the larger community, and identify patterns in a network. We will identify and compare roles found in online forums (e.g., Twitter) using techniques such as feature-based Non-negative Matrix Factorization coupled with topological and influence-based measures of centrality. Since users’ activities change over time, we also analyze role evolution in dynamic networks.

Jones, Rebecca D.↗
DOE OSTI2012

Analyses Of Two End-User Software Vulnerability Exposure Metrics

The risk due to software vulnerabilities will not be completely resolved in the near future. Instead, putting reliable vulnerability measures into the hands of end-users so that informed decisions can be made regarding the relative security exposure incurred by choosing one software package over another is of importance. To that end, we propose two new security metrics, average active vulnerabilities (AAV) and vulnerability free days (VFD). These metrics capture both the speed with which new vulnerabilities are reported to vendors and the rate at which software vendors fix them. We then examine how the metrics are computed using currently available datasets and demonstrate their estimation in a simulation experiment using four different browsers as a case study. Finally, we discuss how the metrics may be used by the various stakeholders of software and to software usage decisions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Efficient Clustering of Software Vulnerabilities using Self Organizing Map (SOM)

The common vulnerabilities and exposures (CVE) database was created with a mission to ``identify, define, and catalog publicly disclosed cybersecurity vulnerabilities''. This rich body of information can be used to enable rapid and efficient response to secure and defend cyber operations and protect critical cyber infrastructure. The main goal of this paper is to develop a visual analytics tool to enable deep analysis of CVEs using unsupervised clustering techniques. We enhance our analysis by first mapping CVEs to hierarchical-classes in Common Weakness Enumeration (CWE) using information in the National Vulnerability Database (NVD). Both the mapping and the numerical representation of CVEs are enabled by V2W-BERT, which uses natural language processing of the extensive information in NVD to generate a large tabular database of 137,226 CVE entries from 1999 to 2020, where each CVE is represented by a vector of 768 numerical features. The vectorized data is processed by Self-Organizing Maps (SOM), which is an unsupervised machine learning technique for dimensionality reduction, visual representation and clustering. Using a Torus map of 6417 units, we achieve ~10-fold data compression of ~140k CVEs using SOM. The trained map is further clustered using standard K-means clustering into 138 clusters of CVEs. We conducted a brief investigation of the rich mapping of CVEs to best-matching-units to K-means clusters, as well as CVEs to CWEs. For example, this novel mapping provided insight into the role of CWE-59 and CWE-264 in several CVEs that is otherwise hard to explore in the original data. We conclude that our this novel approach will not only enable deep analysis of the complex relationships between CVEs and CWEs, but also a mechanism to quickly respond to and design mitigation actions for rapidly evolving vulnerabilities that have not been mapped to existing CWEs.

Panchal, Khyati↗
DOE OSTI2024

Mini Report: LLMs for Vulnerability Repair in Code

Software vulnerability repair is a notoriously difficult task that is both time consuming and labor intensive. While research into this area has a long history, the recent successes of large language models (LLMs) across many tasks have also spurred efforts to leverage LLM capabilities for automated software vulnerability repair. Currently, there are limitations in the capabilities of LLMs to fix bugs and insufficiently addressed problems in the evaluations of these studies may cause performance to not transfer when they are used in practice. Additionally, most research in the area treats finding and fixing bugs as separate concerns - how to best combine all the subtasks involved in removing vulnerabilities from code remains an open question. In this report, we summarize our findings and opinions on the current state of the art in LLM-assisted code vulnerability repair, highlighting current unresolved problems in the field as well as potential applications and future research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Towards Automated Assessment of Vulnerability Exposures in Security Operations

Current approaches for risk analysis of software vulnerabilities using manual assessment and numeric scoring do not complete fast enough to keep pace with the maintenance work rate to patch and mitigate the vulnerabilities. This paper proposes a new approach to modeling software vulnerability risk in the context of the network environment and firewall configuration. In the approach, vulnerability features are automatically matched up with networking, target asset, and adversary features to determine whether adversaries can exploit a vulnerability. The ability of adversaries to reach a vulnerability is modeled by automatically identifying the network services associated with vulnerabilities through a pipeline of machine learning and natural language processing and automatically analyzing network reachability. Our results show that the pipeline can identify network services accurately. We also find that only a small number of vulnerabilities pose real risks to a system. However, if left unmitigated, adversarial reach to vulnerabilities may extend to nullify the effect of firewall countermeasures.

Huff, Philip↗
DOE OSTI2025

Improving Cyber Situational Understanding

Effective cybersecurity operations require the ability to analyze large amounts of information to assess security risks and formulate defensive strategies against adversaries. This has become more complex in recent years as the sprawl and interconnectivity of devices grows through implementation of virtualization, cloud computing, and Internet of Things (IoT). The amount of data and analysis required for effective cybersecurity command and control decisions far exceeds humans’ capacity to perform manually. We characterize the analysis problem as cyber situational understanding. The research presented to improve cyber situational understanding focuses on vulnerability analysis and threat intelligence. Regarding vulnerabilities, entities must analyze and plan work for between thousands and tens of thousands of software vulnerabilities annually. Entities heavily use network firewalls to limit vulnerability exposure. As a result, some of these vulnerabilities permit exposure to adversarial exploitation, whereas others are inaccessible and therefore present negligible risk of exploitation. Distinguishing between high and low risk software vulnerabilities requires a deep understanding of the vulnerability, network firewall protection, and characteristics of the targeted device. This problem is solved by extracting network service features from vulnerability data features using both machine-learning and natural language processing. Then, the network firewall topology is parsed to determine which vulnerabilities are reachable by adversaries. Ultimately, a state-based safety analysis ascertains which vulnerabilities are unsafe. A related vulnerability analysis problem occurs in cybersecurity operations when associating an entity’s hardware and software assets to public vulnerability databases. Assets often reveal hardware and software through installation artifacts and network service identification, and entities store these artifacts in inventory databases. However, software and hardware vendors apply a standard Common Platform Enumeration (CPE) naming convention when publicly reporting vulnerabilities. Associating these two datasets often requires many hours to days of manual inspection. The proposed solution automates the mapping approach of human analysts using fuzzy matching techniques, natural language processing, and, ultimately, machine learning to present a small set of recommendations for mapping the two datasets. The result significantly reduces human analysis time and reduces the occurrence of false positives in vulnerability notifications. Finally, cyber threat intelligence (CTI) requires associating cyber observable artifacts, such as IP addresses, URIs, and file hashes, with cyber threat tactics, techniques, and procedures. Unfortunately, most CTI data is compartmentalized across multiple organizations and cannot be shared due to the legal and reputational risk with cyber threat being associated with the entity. The approach to solving this problem inovlves using a distributed ledger with anonymous token spending and authentication. This allows a consortium of semi-trusted entities to share the workload of curating CTI for a threat sharing community’s cooperative benefit.

Huff, Philip↗
DOE OSTI2019

Defending Against Adversarial Examples

Adversarial machine learning is an active field of research that seeks to investigate the security of machine learning methods against cyber-attacks. An important branch of this field is adversarial examples, which seek to trick machine learning models into misclassifying inputs by maliciously tampering with input data. As a result of the pervasiveness of machine learning models in diverse areas such as computer vision, health care, and national security, this vulnerability is a rapidly growing threat. With the increasing use of AI solutions, threats against AI must be considered before deploying systems in a contested space. Adversarial machine learning is a problem strongly tied to software security, and just like other more common software vulnerabilities, it exploits a weakness in software, like components of machine learning models. During this project, we attempted to survey and replicate several adversarial machine learning techniques with the goal of developing capabilities for Sandia to advise and defend against these threats. To accomplish this, we scanned state of the art research for robust defenses against adversarial examples and applied them to a machine learning problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Exploiting BWA to Manipulate Raw Genomic Data In-Place [PowerPoint]

The project involved exploring implications of genomic software vulnerabilities under realistic security assumptions and standard best practices for genomic variant detection. BWA is the first piece of software in the best-practices pipeline.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

ProvSec: Open Cybersecurity System Provenance Analysis Benchmark Dataset with Labels

System provenance forensic analysis has been studied by a large body of research work. This area needs fine granularity data such as system calls along with event fields to track the dependencies of events. While prior work on security datasets has been proposed, we found a useful dataset of realistic attacks and details that are needed for high-quality provenance tracking is lacking. We created a new dataset of eleven vulnerable cases for system forensic analysis. It includes the full details of system calls including syscall parameters. Realistic attack scenarios with real software vulnerabilities and exploits are used. For each case, we created two sets of benign and adversary scenarios which are manually labeled for supervised machine-learning analysis. In addition, we present an algorithm to improve the data quality in the system provenance forensic analysis. We demonstrate the details of the dataset events and dependency analysis of our dataset cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A National Secure-by-Design Strategy

The US National Cybersecurity Strategy published March 2, 2023 uses plain language to communicate that the US is calling for a major change in how we prioritize the security of software systems used in critical infrastructure. It acknowledges that our current approach, which is essentially, “let the buyer beware,” leaves entities who are least able to assess or defend vulnerable software responsible for the impacts of designed-in weaknesses while the makers of the technology bear no liability. The strategy recommends a security-by-design approach, recommending that software vendors be held liable to uphold a “duty of care” to consumers and for systems to be designed to “fail safely and recover quickly” . For energy infrastructure, the strategy calls out the need to implement the National Cyber-Informed Engineering Strategy to achieve higher confidence security for energy infrastructures. The Idaho National Laboratory, a pioneer in cyber-informed engineering concepts, is at the forefront of organizations educating others in industry, academia, and government on how to apply these concepts to real-world challenges. In this brief, we'll outline some of the basic principles of security-by-design and offer examples of how, in a water sector context, they're being put into successful practice.

42 ENGINEERING↗
DOE OSTI2025

V-INT: Automated Vulnerability Intelligence and Risk Assessment

The project team, including the University of Arkansas (UA) as the lead, the University of Arkansas at Little Rock (UALR), Network Perception (NP), and Bastazo, has successfully researched, developed, and demonstrated the V-INT toolset, and also integrated it into the commercial products of NP (i.e., NP-View) and Bastazo (i.e., Spartan). The end product is a cybersecurity software tool for energy utilities that can automatically assess the risks of software vulnerabilities in an organization’s assets considering the organization’s firewall policies. It allows security operators to identify the small portion of vulnerabilities that poses true threats to their system (i.e., those that are not protected by firewall policies) and prioritize the mitigation of these vulnerabilities to minimize risks. It also allows security operators to identify the vulnerability-induced attack paths under their organization’s firewall policy, providing effective decision supports for mitigating potential attacks.

97 MATHEMATICS AND COMPUTING↗