Key elements of a balanced risk scoring model

Through my conversations with customers, I’ve noticed that nearly everyone has their own unique way of calculating vulnerability risk or priority. Some rely on free data sources like the NVD (National Vulnerability Database) or downstream sources, while others use custom-built algorithms that account for various factors. Each approach has its strengths and limitations, but ultimately, effective vulnerability prioritization depends on asking the right questions and leveraging high-quality data.

In this article, I’ll share some key components that can help build a balanced risk scoring model, as well as some open questions to consider for fine-tuning your vulnerability management approach. My hope is to spark a discussion around what’s important in vulnerability prioritization, helping teams focus on what matters most.

Why a Reliable Risk Score Matters

Vulnerability management is not just about patching; it's about strategically mitigating risk. With thousands of vulnerabilities disclosed every year, security teams are tasked with focusing on the ones that pose the highest risk to the business. An effective risk score model enables organizations to highlight the vulnerabilities that are both technically severe and contextually relevant.

The Risks of Relying on Free Data Sources

Relying solely on free data sources, like the NVD and its downstream feeds, has its challenges. While the NVD is a great starting point, it can sometimes misclassify or provide outdated scores. If your prioritization is built solely on this data, you could end up addressing low-risk issues while critical vulnerabilities remain exposed.

Building a robust risk score model requires more than NVD data alone. It requires adding layers of context, threat intelligence, and organization-specific factors. Here are some of the key elements that can help build a well-rounded vulnerability risk score model.

Key Components of a Balanced Risk Score Model

Here’s a breakdown of some core elements that contribute to an effective risk score model, along with ideas on how each might be scored. I hope these ideas can spark some thought around building or refining your own model.

1. Impact Level: Measuring Potential Damage

Impact level refers to the type of access or damage a vulnerability could cause if exploited. For example, a vulnerability that allows "System Access" would be more dangerous than one leading only to a Denial of Service (DoS). This factor answers the question: What could happen if this vulnerability is exploited?

Possible Scoring: Higher scores could be assigned to vulnerabilities with system-level impact, representing their potential for significant damage. Lesser impact types, like DoS, might receive a moderate score, while informational vulnerabilities could be rated low.

2. Exploitability: Is This Actively Targeted?

Exploitability reflects whether attackers are actively exploiting a vulnerability. Recent exploit activity increases the urgency of addressing a vulnerability, while historical exploitation still indicates a risk, albeit lower. This factor answers: Is this vulnerability already in use by attackers?

Possible Scoring: Higher scores could be given for vulnerabilities with recent or active exploit links. Historical exploitability could receive a moderate score, helping to prioritize vulnerabilities that have been targeted in the past.

3. Scanner Uptake: Recognized by Security Tools

If a vulnerability is widely detected by security scanners, it reflects its recognition across different security platforms. While not as critical, scanner uptake can still serve as an indicator of broader industry awareness.

Possible Scoring: Vulnerabilities with high scanner uptake could receive a small score bump, slightly increasing their priority based on their visibility.

4. CVSS Score: Standardized Severity

The CVSS score (Common Vulnerability Scoring System) is a widely-used measure of severity. It provides a baseline score based on attributes like exploitability, impact, and attack complexity. However, while CVSS is useful, it’s often too generic to use on its own, which is why many teams add contextual factors alongside it.

Possible Scoring: CVSS scores can be scaled into a risk model, with higher severity scores contributing proportionally to the overall risk score.

5. Zero-Day Status: Top Priority Consideration

Zero-day vulnerabilities demand immediate attention due to their critical nature. These vulnerabilities have been disclosed publicly, making them accessible to attackers, and they often still lack a patch or have limited remediation options. Zero-day vulnerabilities inherently carry high risk, and managing them requires swift action to prevent exploitation.

In many cases, vendors release patches shortly after disclosure — often within 24 hours. However, during this gap, organizations remain vulnerable, making zero-days a top priority regardless of patch status. To manage zero-day vulnerabilities effectively, security teams should consider containment strategies, such as:

• Isolating affected systems
• Restricting network access
• Applying temporary mitigations provided by the vendor

This factor answers: Is this vulnerability an urgent risk due to immediate exploitability and limited defenses?

Possible Scoring: Zero-day vulnerabilities should receive a high-priority score boost, emphasizing the urgency to address them immediately.

6. Criticality: Secunia’s Severity Rating

The Criticality Score is a proprietary scoring system developed by Secunia to classify vulnerabilities based on their severity and the ease of exploitation. This score provides an extra layer of context to help prioritize vulnerabilities based on specific criteria. The Secunia Criticality Score is distinct from other scoring models because it accounts for factors like exploit availability, interaction requirements, and targeted services or applications.

The Criticality Score categorizes vulnerabilities into five levels:

• Extremely Critical: Applies to easily exploitable vulnerabilities with active zero-day exploits, particularly those impacting critical services like FTP, HTTP, SMTP, or client systems (e.g., email clients, browsers). Vulnerabilities of this type can also affect operating system-level functions, such as font handling.
• Highly Critical: Assigned to remotely exploitable vulnerabilities that could lead to system compromise without user interaction, though no known exploits exist at the time of disclosure. These vulnerabilities often impact services like FTP, HTTP, SMTP, or client systems.
• Moderately Critical: Used for remotely exploitable vulnerabilities causing denial-of-service or information disclosure. This level also includes vulnerabilities allowing system compromise on local networks, particularly in services like SMB, RPC, or NFS.
• Less Critical: Typically applied to cross-site scripting vulnerabilities, local privilege escalations, and vulnerabilities allowing sensitive data exposure to local users.
• Not Critical: Covers vulnerabilities with limited privilege escalation potential or those exploitable only locally, such as non-sensitive system information disclosures.

Possible Scoring: In a prioritization model, Extremely Critical vulnerabilities would receive the highest weighting, emphasizing their urgency due to active exploits and potential ease of attack. Less Critical or Not Critical vulnerabilities would receive proportionally lower scores, ensuring that resources focus on the vulnerabilities that pose the greatest risk.

7. Threat Score: Real-World Relevance

The Threat Score is another essential factor, indicating the likelihood that a vulnerability will be exploited in real-world scenarios. This factor captures threat intelligence that goes beyond technical severity to account for how likely it is that attackers will use this vulnerability. It answers: How relevant is this vulnerability to real-world attackers?

Possible Scoring: Higher Threat Scores would carry significant weight in the risk score, helping ensure that the most relevant vulnerabilities are prioritized.

Open Questions: Beyond the Basic Model

While these components cover many aspects of vulnerability risk, there are additional factors worth considering that could further refine prioritization. Here are some open questions that I believe are important for building a comprehensive model:

1. How Many Devices Are Affected? A vulnerability impacting thousands of devices across an organization should likely be treated differently than one affecting only a small number of assets. Do you have visibility into asset distribution?
2. Are These Devices Sensitive or Internet-Facing? Devices that are internet-facing or critical to business operations may need higher prioritization. This consideration would add even more context, helping to identify vulnerabilities with a high potential for exposure.
3. Historical Vendor Performance on Patching: Certain vendors are known for releasing patches quickly and consistently, while others may have slower or lower-quality updates. Factoring in a vendor’s track record on patching quality and speed can be valuable in determining urgency.
4. Client or Server Impact: Vulnerabilities on client devices may pose different risks compared to server-side vulnerabilities. The environment and potential attack surface can shift depending on the type of device.
5. Using Public data sources like:

1. EPSS : for exploitation likelyhood
2. CISA: KEV data
3. CWE: for root cause insights and some predictive risk
4. MITRE ATT&CK Framework Mappings
5. OWASP (for mapping critical web application security risks)
6. Vulnerability Chaining , now in CVSS4
7. etc. ( what do you use?)

Final Thoughts

An effective vulnerability risk score model helps organizations focus on vulnerabilities that are both high-risk and relevant to their unique environment. By combining technical severity with real-world threat intelligence and contextual factors, security teams can create a more accurate, actionable prioritization list.

This is a high-level view of what could go into an effective vulnerability risk scoring model. I hope this post sparks some discussion on the methods and priorities others use in their own environments. What factors do you consider crucial in your vulnerability prioritization model? Let’s connect and exchange insights!

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