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and monitoring legacy systems, prioritizing the patching of deferred vulnerabilities, and hardening or segmenting outdated infrastructure.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

Central Asian Banks Accelerate AI Adoption and Integration

The Digital Transformation of Financial Services in Central Asia The rapid convergence of financial stability and computational intelligence has transformed the Central Asian banking sector into a high-stakes laboratory for digital evolution. The financial landscape across this region is currently undergoing a radical technological shift, as banks and credit institutions pivot toward a future defined by Artificial Intelligence (AI). This

How Is Generative AI Reshaping Digital Marketing Strategy?

The Paradigm Shift: From Capturing Attention to Providing Utility The traditional digital marketing playbook has been rendered obsolete by a landscape where consumers no longer “browse” but instead “interact” with intelligent systems. For decades, the industry relied on an interruption-based model, where brands fought for a few seconds of a consumer’s attention by placing ads in the middle of their

Trend Analysis: AI Augmented Sales Strategies

Successful revenue generation no longer rests solely on the shoulders of the charismatic closer who relies on gut feeling and a Rolodex of aging contacts. The contemporary sales landscape is undergoing a fundamental transformation, transitioning from a purely human-centric craft to an augmented “mind meld” between professional expertise and generative artificial intelligence. In a world where nothing happens until somebody

Can AI Replace the Human Touch in Travel Service?

Standing in a crowded terminal while watching red “Cancelled” text flicker across every departure screen creates a hollow, sinking sensation that no smartphone notification can ever truly soothe. The modern traveler navigates a digital landscape where instant answers are expected, yet the frustration of a circular chatbot loop remains a common grievance. While a traveler might celebrate the speed of

Global AI Trends Driven by Regional Integration and Energy Need

The global landscape of artificial intelligence has transitioned from a period of speculative hype into a phase of deep, localized integration that reshapes how nations interact with emerging digital systems. This evolution is characterized by a “jet-setting” model of technology, where AI is not a monolithic force exported from a single center but a fluid tool that adapts to the