A vulnerability database is a platform aimed at collecting, maintaining, and disseminating information about discovered vulnerabilities targeting real computer systems. The database will customarily describe the identified vulnerability, assess the potential infliction on computer systems and the workaround required to desist a hacker. For a hacker to surmount a system's information assurance, three elements must apply: a susceptibility within the system, access to the susceptibility and the ability to exploit the susceptibility.
Types of vulnerability databases
Major vulnerability databases such as the Open Source Vulnerability Database (OSVDB) and National Vulnerability Database U.S (NVD) publish Common Vulnerabilities and Exposures (CVE). The primary purpose of CVE is to feed vulnerability databases like OSVDB vulnerabilities and exposure identification names and numbers. Vulnerability databases develop the received intelligence from CVE and investigate further providing vulnerability scores, impact ratings and the requisite workaround. CVE is paramount for linking vulnerability databases so critical patches and debugs can be shared to inhibit hackers from accessing sensitive information on private systems.
The Open Source Vulnerability Database provides an accurate, technical and unbiased index on vulnerability security. The comprehensive database catalogues over 121,000 vulnerabilities spanning a 113 year period. The OSVDB was founded in August 2002 and was launched in March 2004. In its primitive beginning, newly identified vulnerabilities were investigated by site members and explanations were detailed on the website. However, as the necessity for the service thrived, the need for dedicated staff resulted in the inception of the Open Security Foundation (OSF) which was founded as a non-profit organisation in 2005 to provide funding for security projects and primarily the OSVDB.
The National Vulnerability Database is a comprehensive cyber security vulnerability database formed in 2005 that reports on CVE. The NVD is a primary cyber security referral tool for individuals and industries alike providing informative resources on current vulnerabilities. The NVD holds in excess of 50,000 records and publishes 13 new entries daily on average. Similar to the OSVDB, the NVD publishes impact ratings and categorises material into an index to provide users with an intelligible search system.
Vulnerability databases advise organisations to develop and execute patches or other mitigations which endeavour to rectify critical vulnerabilities. However, this can often lead to the creation of additional susceptibilities as patches are created hastily to thwart further system exploitations and violations. Depending upon the level of a user or organisation, they warrant appropriate access to a vulnerability database which provides the user with disclosure of known vulnerabilities that may affect them. The justification for limiting access to individuals is to impede hackers from being versed in corporation system vulnerabilities which could potentially be further exploited.
Use of vulnerability databases
Vulnerability databases contain a vast array of identified vulnerabilities. However, few organisations possess the expertise, staff and time to revise and remedy all potential system susceptibilities hence vulnerability scoring is a method of quantitatively determining the severity of a system violation. A multitude of scoring methods exist across vulnerability databases such as US-CERT and SANS Institute's Critical Vulnerability Analysis Scale but the Common Vulnerability Scoring System (CVSS) is the prevailing technique for most vulnerability databases including OSVDB and NVD. The CVSS is based upon three primary metrics: base, temporal and environmental which each provide a vulnerability rating.
This metric covers the immutable properties of a vulnerability such as the potential impact of the exposure of confidential information, the accessibility of information and the aftermath of the irretrievable deletion of information.
The temporal metrics denote the mutable nature of a vulnerability for example the credibility of an exploitability, the current state of a system violation and the development of any workarounds that could be applied.
This aspect of the CVSS rates the potential loss to individuals or organisations from a vulnerability. Furthermore, it details the primary target of a vulnerability ranging from personal systems to large organisations and the number of potentially affected individuals.
The complication with utilising different scoring systems it that there is no consensus on the severity of a vulnerability thus different organisations may overlook critical system exploitations. The key benefit of a standardised scoring system like CVSS is that published vulnerability scores can be assessed, pursued and remedied rapidly. Organisations and individuals alike can determine the personal impact of a vulnerability on their system. The benefits derived from vulnerability databases to consumers and organisations are exponential as information systems become increasingly embedded, our dependency and reliance on them grows, as does the opportunity for data exploitation.
Common security vulnerabilities listed on vulnerability databases
Initial deployment failure
Although the functionality of a database may appear unblemished, without rigorous testing, the exiguous flaws can allow hackers to infiltrate a system's cyber security. Frequently, databases are published without stringent security controls hence the sensitive material is easily accessible.
Database attacks are the most recurrent form of cyber security breaches recorded on vulnerability databases. SQL and NoSQL injections penetrate traditional information systems and big data platforms respectively and interpolate malicious statements allowing the hackers unregulated system access.
Established databases ordinarily fail to implement crucial patches suggested by vulnerability databases due to an excessive workload and the necessity for exhaustive trialling to ensure the patches update the defective system vulnerability. Database operators concentrate their efforts into major system deficiencies which offers hackers unmitigated system access through neglected patches.
All databases require audit tracks to record when data is amended or accessed. When systems are created without the necessary auditing system, the exploitation of system vulnerabilities are challenging to identify and resolve. Vulnerability databases promulgate the significance of audit tracking as a deterrent of cyber attacks.
Data protection is essential to any business as personal and financial information is a key asset and the purloining of sensitive material can discredit the reputation of a firm. The implementation of data protection strategies is imperative to guard confidential information. Some hold the view that is it the initial apathy of software designers that in turn, necessitates the existence of vulnerability databases. If systems were devised with greater diligence, they may be impenetrable from SQL and NoSQL injections making vulnerability databases redundant.
- "Common Vulnerabilities and Exposures (CVE)". Cve.mitre.org. Retrieved 1 November 2015.
- Yun-Hua, G; Pei, L (2010). "Design & Research on Vulnerability Databases": 209–212.
- Karlsson, M (2012). "The Edit History of the National Vulnerability Database and similar Vulnerability Databases".
- "NVD Primary Resources". National Vulnerability Database. Retrieved 1 November 2015.
- Erickson, J (2008). Hacking - The Art of Exploitation (1st ed.). San Francisco: No Starch Press. ISBN 1593271441.
- First. "Common Vulnerability Scoring System (CVSS-SIG)". Retrieved 1 November 2015.
- Mell, P; Romanosky, S (2006). "Common Vulnerability Scoring System". IEEE Security and Privacy Magazine 4 (6): 85–89.
- Hayden, L (2010). IT Security Metrics (1st ed.). New York: McGraw Hill.
- Chandramouli, R; Grance, T; Kuhn, R; Landau, S (2006). "Common Vulnerability Scoring System": 85–88.
- "The Most Significant Risks of 2015 and How to Mitigate Them" (PDF). Imperva. Retrieved 2 November 2015.
- Natarajan, K; Subramani, S (2012). "Generation of Sql-injection Free Secure Algorithm to Detect and Prevent Sql-Injection Attacks". Procedia Technology 4: 790–796.
- "Vulnerability Database - Top 1000 Flaws". Network Security 8 (6). 2001.
- Afyouni, H (2006). Database Security & Auditing (1st ed.). Boston: Thomson Course Technology.
- Sirohi, D (2015). Transformational Dimensions of Cyber Crime. India: Vij Books. pp. 54–65.