Researchers from CISA and NIST have proposed a new cybersecurity metric designed to calculate the likelihood that a vulnerability has been exploited in the wild.
Peter Mell of NIST and Jonathan Spring of CISA have published a paper describing equations for what they call Likely Exploited Vulnerabilities, or LEV.
Thousands of vulnerabilities are discovered every year in software and hardware, but only a small percentage are ever exploited in the wild.
Knowing which vulnerabilities have been exploited or predicting which flaws are likely to be exploited is important for organizations when trying to prioritize patching.
Known Exploited Vulnerabilities (KEV) lists such as the one maintained by CISA and the Exploit Prediction Scoring System (EPSS), which relies on data to estimate the probability that a vulnerability will be exploited, can be very useful. However, KEV lists may be incomplete and EPSS may be inaccurate.
LEV aims to enhance — not replace — KEV lists and EPSS. This is done through equations that take into account variables such as the first date when an EPSS score is available for a specified vulnerability, the date of the most recent KEV list update, inclusion in KEV, and the EPSS score for a given day (measured across multiple days).
LEV probabilities can be useful for measuring the expected number and proportion of vulnerabilities that threat actors have exploited.
It can also be useful for estimating the comprehensiveness of KEV lists. “Previously, KEV maintainers had no metric to demonstrate how close their list was to including all relevant vulnerabilities,” the researchers explained.
In addition, LEV probabilities can help augment KEV- and EPSS-based vulnerability remediation prioritization — in the case of KEV by identifying higher-probability vulnerabilities that may be missing, and in the case of EPSS by finding vulnerabilities that may be underscored.
While in theory LEV could turn out to be a very useful tool for vulnerability prioritization, the researchers pointed out that collaboration is necessary, and NIST is looking for industry partners “with relevant datasets to empirically measure the performance of LEV probabilities”.
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