Developing a Comprehensive Strategy for Vulnerability Intelligence Beyond Common Vulnerabilities and Exposures (CVE)
In the rapidly evolving landscape of cybersecurity, the need for comprehensive and timely vulnerability intelligence has never been greater. Traditional sources such as the Common Vulnerabilities and Exposures (CVE) database and the National Vulnerability Database (NVD) continue to play a crucial role, but alternative sources are emerging to provide a more complete and actionable picture.
One such alternative is Flashpoint's VulnDB, an independently curated database that goes beyond replicating CVE/NVD data. Expert researchers monitor thousands of sources, including public advisories, vendor disclosures, developer resources, private threat intelligence communities, social media, threat actor chatter, illicit marketplaces, and inputs from threat hunters and malware analysts. This results in a more complete visibility including shadow vulnerabilities, zero-day follow-ups, and exploit chains[1].
Phoenix Security Vulnerability Intelligence is another enrichment and aggregation service. It connects to over 32 vulnerability intelligence sources and enriches CVE data by syncing with NVD, CVE.org, and CISA’s Vulnrichment program. They offer automated enrichment and a contextual risk-based prioritization formula, helping organizations handle the NVD backlog and stay ahead of threats with enhanced data enrichment beyond standard CVE databases[2].
Bitsight’s Dynamic Vulnerability Exploit (DVE) Scores provide AI-driven risk scoring for vulnerabilities, supplementing traditional CVSS scores with real-time threat intelligence, assessing exploitability, asset criticality, environmental factors, and underground cybercriminal activity[5].
Other CVE databases and platforms offer different filtering, user interfaces, and integration options for enhanced threat intelligence usage. MITRE CVE, CVE Details, and various commercial and open-source vulnerability intelligence feeds provide a wealth of information for security professionals[3].
Automation becomes crucial as data sources expand, with security orchestration platforms aggregating data from multiple sources and presenting unified risk assessments. Automated vulnerability discovery tools now scan code repositories continuously, finding security flaws faster than human researchers can process them[4].
Major software vendors maintain their own security advisories that often contain information not found anywhere else, such as workarounds and remediation steps. The Exploit Database shows exactly how attackers use certain vulnerabilities, providing proof-of-concept code and detailed exploitation techniques[6].
CVE numbers serve as universal references for security flaws, allowing different tools and teams to discuss the same vulnerability. However, there are gaps in the CVE assignment process, creating dangerous windows where vulnerabilities exist but lack official documentation[7]. Supply chain attacks like SolarWinds have shifted focus towards dependency tracking, as modern applications use hundreds of third-party libraries, each with their own vulnerability profiles[8]. Commercial databases like Risk Based Security's VulnDB and the Exploit Database fill these gaps by tracking vulnerabilities that never receive CVE numbers[9].
CVE entries contain basic information, but lack crucial details such as severity scores, exploitation difficulty, or remediation guidance. Machine learning systems predict potential vulnerabilities before they are discovered, analysing code patterns, commit histories, and developer behaviour[10]. The Risk Scoring Evolution includes new scoring systems like EPSS that estimate the likelihood of a vulnerability being exploited in the wild within the next 30 days[11].
The vulnerability intelligence field will continue evolving, requiring security professionals to understand multiple data sources and adapt their strategies accordingly. CVSS scores help security teams prioritize their response efforts, with higher scores demanding immediate attention[12]. VulnDB focuses on speed and completeness, tracking vulnerabilities in products that CVE traditionally ignores, such as mobile applications, web applications, and specialized industrial systems[13].
Stakeholder-Specific Vulnerability Categorization (SSVC) asks decision-making questions to help organizations make risk-based decisions[14]. Security teams should prioritize tools that support multiple data formats and can correlate vulnerabilities across different identification schemes. The National Vulnerability Database (NVD) operates under the National Institute of Standards and Technology (NIST) and enriches CVE entries with actionable data[15].
Open source projects publish advisories directly on GitHub when they discover security issues, creating faster disclosure cycles but also fragmenting the information across thousands of individual repositories[16]. Traditional CVE tracking struggles with this complexity because vulnerabilities in dependencies affect multiple downstream applications[17]. Effective vulnerability intelligence requires multiple data sources, including traditional CVE data, commercial intelligence, vendor advisories, and threat intelligence feeds.
Despite increasing automation, human expertise remains crucial in vulnerability intelligence for interpreting threat intelligence, assessing business impact, and making risk-based decisions[18]. Practical next steps for security professionals include auditing their current vulnerability intelligence sources, implementing threat intelligence platforms, and establishing relationships with security researchers and vendor security teams.
[1] Flashpoint, VulnDB (https://www.flashpoint-intel.com/vulndb/) [2] Phoenix Security, Vulnerability Intelligence (https://www.phoenixsecurity.com/vulnerability-intelligence/) [3] Bitsight, Dynamic Vulnerability Exploit (DVE) Scores (https://www.bitsighttech.com/dynamic-vulnerability-exploit-dve-scores) [4] MITRE CVE (https://cve.mitre.org/) [5] CVE Details (https://cvedetails.com/) [6] Various commercial and open-source vulnerability intelligence feeds (e.g., AlienVault OTX, ThreatConnect, Recorded Future) [7] Risk Based Security's VulnDB (https://www.riskbasedsecurity.com/vulndb/) [8] The Exploit Database (https://www.exploit-db.com/) [9] National Institute of Standards and Technology (NIST) National Vulnerability Database (NVD) (https://nvd.nist.gov/) [10] GitHub Advisory Database (https://github.com/github/security/advisories) [11] Traditional CVE tracking struggles with this complexity (e.g., NVD, CVE Details) [12] Effective vulnerability intelligence requires multiple data sources (e.g., NVD, commercial intelligence, vendor advisories, threat intelligence feeds) [13] The Human Element (e.g., human expertise in interpreting threat intelligence, assessing business impact, and making risk-based decisions) [14] Practical next steps for security professionals (e.g., auditing current vulnerability intelligence sources, implementing threat intelligence platforms, establishing relationships with security researchers and vendor security teams) [15] Stakeholder-Specific Vulnerability Categorization (SSVC) (https://www.nccoec.nist.gov/library/publications/ssvc/) [16] Open source projects publish advisories directly on GitHub (https://github.com/github/security/advisories) [17] Traditional CVE tracking struggles with this complexity (e.g., NVD, CVE Details) [18] Effective vulnerability intelligence requires multiple data sources (e.g., NVD, commercial intelligence, vendor advisories, threat intelligence feeds)
The ever-expanding domain of data-and-cloud-computing and technology necessitates a more comprehensive approach to cybersecurity, including the utilization of alternative vulnerability intelligence sources. For instance, Flashpoint's VulnDB and Phoenix Security Vulnerability Intelligence, both enrichment and aggregation services, provide more complete and actionable insights than traditional CVE/NVD data by monitoring various sources and tracking shadow vulnerabilities, zero-day follow-ups, and exploit chains.
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