NIST Deprioritizes Pre-2018 CVEs Amid Backlog and New Threats

Article Highlights
Off On

The US National Institute of Standards and Technology (NIST) recently made a significant decision affecting the cybersecurity landscape by marking all Common Vulnerabilities and Exposures (CVEs) published before January 1, 2018, as “Deferred” in the National Vulnerability Database (NVD). This move impacts over 20,000 entries and potentially up to 100,000, signaling that these CVEs will no longer be prioritized for further enrichment data updates unless they appear in the Cybersecurity and Infrastructure Security Agency’s (CISA) Known Exploited Vulnerabilities (KEV) catalog. NIST’s decision comes in response to an ongoing struggle with a growing backlog in processing vulnerability data, exacerbated by a 32% surge in submissions in the past year.

An Overwhelming Backlog and Strategic Reprioritization

NIST’s challenges in processing and enriching the vast amount of incoming data have delayed its goal of clearing the backlog by the end of fiscal year 2024. In response, NIST is developing new systems to handle these issues more efficiently. Industry experts consider this move practical given the complexities of managing vulnerabilities at scale. Ken Dunham from Qualys describes it as an evolution in the face of changing cyber threats. Meanwhile, Jason Soroko from Sectigo interprets this as a strategic reprioritization, with resources redirected towards addressing emerging threats, assuming that legacy issues have been mitigated through routine patch management practices. The responsibility for managing deferred CVEs now shifts more heavily onto organizations. For security teams, this means identifying and monitoring legacy systems, prioritizing the patching of deferred vulnerabilities, and hardening or segmenting outdated infrastructure. Using real-time threat intelligence to detect attempts at exploiting these vulnerabilities becomes crucial. This shift highlights a broader trend where organizations must adopt proactive risk management strategies due to the increasing volume of CVEs and limited resources available to handle them.

Embracing Advanced Technology for Improved Efficiency

In addressing its backlog, NIST is also exploring the potential use of artificial intelligence (AI) and machine learning to streamline the processing of vulnerability data. This move reflects an ongoing trend in the cybersecurity industry toward leveraging advanced technologies for more efficient management of vulnerabilities. By incorporating AI and machine learning, NIST aims to ensure that both older and newer vulnerabilities receive appropriate attention within the constraints of available resources. This nuanced approach to cybersecurity management underscores the need for a balance between addressing legacy vulnerabilities and staying ahead of emerging threats. Organizations are encouraged to adopt similar strategies, using technology to enhance their cybersecurity efforts and ensure comprehensive coverage of potential vulnerabilities. This shift in focus not only addresses immediate backlog issues but also sets the stage for more sustainable and scalable vulnerability management practices in the future.

New Paradigm for Cybersecurity Management

The US National Institute of Standards and Technology (NIST) has recently made a crucial decision that impacts the cybersecurity domain by designating all Common Vulnerabilities and Exposures (CVEs) published before January 1, 2018, as “Deferred” in the National Vulnerability Database (NVD). This adjustment affects over 20,000 entries and potentially up to 100,000, indicating that these CVEs will no longer receive prioritized updates for enrichment data unless they are listed in the Cybersecurity and Infrastructure Security Agency’s (CISA) Known Exploited Vulnerabilities (KEV) catalog. NIST’s decision is a response to an ongoing challenge with a growing accumulation of vulnerability data, which has been aggravated by a 32% increase in submissions over the past year. This strategic shift aims to address the backlog more effectively and allocate resources more efficiently, ensuring newer and more critical vulnerabilities receive the attention they require for maintaining robust cybersecurity measures.

Explore more

Why Poor CRM Data Quality Is Sabotaging Enterprise AI ROI

The modern corporate landscape is currently locked in a high-stakes arms race to integrate artificial intelligence into every facet of sales and marketing, yet most of these digital engines are running on fumes. While executives pour millions into sophisticated neural networks and predictive modeling, they often overlook a sobering reality: artificial intelligence is a force multiplier that accelerates the impact

The Great AI Content Glut Fails to Capture Human Attention

Generative Artificial Intelligence is now capable of producing media at infinite scale with near-zero marginal cost, yet human capacity to process this content remains stubbornly finite. The current digital ecosystem is flooded with an overwhelming volume of automated material that threatens to bury genuine communication under a mountain of synthetic noise. As marketing departments and media houses increasingly rely on

How to Drive B2B Demand with ABM, Brand, and Content

The silent shift of high-value prospects into private digital communities has rendered the traditional, volume-heavy marketing funnel nearly obsolete for modern enterprise organizations. In the current 2026 landscape, the frantic pursuit of lead quantity has been replaced by a sophisticated focus on account quality and relationship depth. Decision-makers are no longer responding to unsolicited outreach; instead, they navigate the “dark

Blogging Success Hits 12-Year Low Despite Record AI Use

The modern digital landscape is currently witnessing a historic collapse in content marketing efficacy that contradicts the massive technological advancements seen over the last few years. While automation tools have flooded the market and become a standard part of the professional workflow, the actual impact of a well-crafted blog post has reached its lowest point since the early 2010s. This

How AI Shopping Assistants Are Transforming Retail Branding

The Intermediary Invasion: When Algorithms Choose Your Wardrobe Digital shoppers are increasingly delegating their entire decision-making process to sophisticated autonomous agents that bypass traditional marketing channels entirely. This transition marks the arrival of a computational layer where an algorithm, rather than a human, determines the value of a brand. As these bots take over the tasks of browsing and comparison,