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and monitoring legacy systems, prioritizing the patching of deferred vulnerabilities, and hardening or segmenting outdated infrastructure.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

Trend Analysis: Agentic AI in Data Engineering

The modern enterprise is drowning in a deluge of data yet simultaneously thirsting for actionable insights, a paradox born from the persistent bottleneck of manual and time-consuming data preparation. As organizations accumulate vast digital reserves, the human-led processes required to clean, structure, and ready this data for analysis have become a significant drag on innovation. Into this challenging landscape emerges

Why Does AI Unite Marketing and Data Engineering?

The organizational chart of a modern company often tells a story of separation, with clear lines dividing functions and responsibilities, but the customer’s journey tells a story of seamless unity, demanding a single, coherent conversation with the brand. For years, the gap between the teams that manage customer data and the teams that manage customer engagement has widened, creating friction

Trend Analysis: Intelligent Data Architecture

The paradox at the heart of modern healthcare is that while artificial intelligence can predict patient mortality with stunning accuracy, its life-saving potential is often neutralized by the very systems designed to manage patient data. While AI has already proven its ability to save lives and streamline clinical workflows, its progress is critically stalled. The true revolution in healthcare is

Can AI Fix a Broken Customer Experience by 2026?

The promise of an AI-driven revolution in customer service has echoed through boardrooms for years, yet the average consumer’s experience often remains a frustrating maze of automated dead ends and unresolved issues. We find ourselves in 2026 at a critical inflection point, where the immense hype surrounding artificial intelligence collides with the stubborn realities of tight budgets, deep-seated operational flaws,

Trend Analysis: AI-Driven Customer Experience

The once-distant promise of artificial intelligence creating truly seamless and intuitive customer interactions has now become the established benchmark for business success. From an experimental technology to a strategic imperative, Artificial Intelligence is fundamentally reshaping the customer experience (CX) landscape. As businesses move beyond the initial phase of basic automation, the focus is shifting decisively toward leveraging AI to build