The Common Vulnerabilities and Exposures (CVE) system catalogs known security flaws to help organizations identify and address risks. However, many security tools generate CVE false positives—flagging issues in unused or mitigated code—which wastes time and erodes team trust. Azul Intelligence Cloud minimizes this problem by detecting only active vulnerabilities in running Java code, reducing noise and alert fatigue. It uses a curated Java-specific CVE database, historical analysis, and code inventory to improve accuracy, prioritize real threats, and streamline remediation. The result: faster, more focused vulnerability management with less operational drag.
Whenever a plan meets reality, there are bound to be unexpected outcomes. As nice as it would be for code to always work as intended, there are times where it doesn’t. To understand what caused this unexpected outcome is often a key step in changing it to a planned outcome. In the field of computer science, many systems are in place to allow users to understand where something went wrong.
What Is CVE?
One of these systems is the Common Vulnerabilities and Exposures (CVE) program, which identifies and catalogs publicly disclosed cybersecurity vulnerabilities. CVE serves as a collection of shared knowledge about exposures and vulnerabilities.
CVE was made available to the public in September 1999. It has been used by many organizations and agencies, primarily in the United States, contributing to its cybersecurity infrastructure and helping create standards and regulations for other entities working in information security (InfoSec). CVE builds its database by assigning a CVE record to each vulnerability found.
When writing code, no one plans to create vulnerabilities to be exploited. These are discovered usually when the code is exposed to a larger userbase, with actors or agents that actively seek these exposures out. It’s a difficult problem to anticipate, as these vulnerabilities are bound to appear in a coder’s blind spots. A resource such as CVE attempts to solve the problem by assembling a large reference catalog for commonly found problems. It doesn’t solve the issue by itself though, as the data from the catalog needs to be interpreted and applied, and you need to make sure automtated detection tools that discover CVEs aren’t just generating false positives, which can undermine the effectiveness of the resource. A recommended tool to address this is Azul Intelligence Cloud, which works to support the advantages that using the CVE system gives.
CVE False Positives
A CVE false positive is when a security tool mistakenly reports a CVE in your system or application that isn’t a real security risk in your environment. For example, tools that provide a reachability analysis may generate a false positive even though the identified risk is in unreachable code, which is code that’s never used. In another example, you may have already built compensating controls that circumvent or fully mitigate the risk.
As a result, CVE false positives can waste your time and resources, while a security team investigates the alerts and diverts their resources from genuine threats. This can also add to your team’s alert fatigue, where the constant false alarms result in the security team missing or ignoring critical alerts. Similarly, false positives erode trust among the team, as developers lose confidence in your security tools. Overall, these problems add up to a reduced efficiency in your team and lower morale.
How Do You Address CVE False Positives?
Effective vulnerability management involves regularly addressing CVE false positives. You can take several steps to address false positives:
- Refine your vulnerability scanner configuration to make sure it uses the appropriate credentials for authenticated scans, to define accurate scan targets, and to avoid aggressive settings that might scan unused, deprecated or mitigated code.
- Prioritize and triage vulnerabilities based on their severity and investigate the high-risk vulnerabilities first.
- Contextually analyze the reported vulnerabilities to identify whether the code is being used.
- Use threat intelligence tools to verify the CVEs that are being actively exploited.
- Use vulnerability exception and suppression rules to help you document CVE exceptions.
- Maintain your asset inventory to provide important contextual information that will help you effectively identify false positives.
- Increase strong communication and collaboration between your security, development, and operations teams to share knowledge and context about your application infrastructure. This process will help all the teams identify and resolve false positives.
Azul Cloud Intelligence and CVE
How does Azul Intelligence Cloud (IC) help you with CVEs? IC helps your organization address CVEs in your Java applications by accurately detecting vulnerabilities that are in production. Because IC focuses on method-level vulnerabilities that occur in the code that you run, this process alone can greatly reduce false positives. Because Azul specializes in Java development, their vulnerability detection leverages a curated, Java-specific CVE knowledge base, which provides increased accuracy and relevancy to your Java codebase.
IC’s Vulnerability Detection is an agentless service that uses telemetry from Azul JVMs. This process reduces your performance overhead and operational complexity. It also features historical analysis, which allows your developers to analyze when a newly disclosed vulnerability was used in their environment.
IC’s Code Inventory identifies your unused code so that you can remove all your dead or unused code, which will reduce your attack surface and total reported vulnerabilities.
To learn more about how IC helps you address CVEs and reduce CVE false positives, see Azul Intelligence Cloud.
