Readers may recall that I blogged about a similar topic just over a month ago, in an article titled Advisories != Vulnerabilities, and How It Affects Statistics. In this installment, instead of “advisories”, we have “CVEs” and the inherent problems when using CVE identifiers in the place of “vulnerabilities”. Doing so is technically inaccurate, and it negatively influences statistics, ultimately leading to bad conclusions.
NSS Labs just released an extensive report titled “Vulnerability Threat Trends; A Decade in Review, Transition on the Way“, by Stefan Frei. While the report is interesting, and the fundamental methodology is sound, Frei uses a dataset that is not designed for true vulnerability statistics. Additionally, I believe that some factors that Frei attributes to trends are incorrect. I offer this blog as open feedback to bring additional perspective to the realm of vulnerability stats, which is a long ways from approaching maturity.
Vulnerabilities versus CVE
In the NSS Labs paper, they define a vulnerability as “a weakness in software that enables an attacker to compromise the integrity, availability, or confidentiality of the software or the data that it processes.” This is as good a definition as any. The key point here is a weakness, singular. What Frei fails to point out, is that the CVE dictionary is not a vulnerability database in the same sense as many others. It is a specialty database designed primarily to assign a unique identifier to a vulnerability, or a group of vulnerabilities, to coordinate tracking and discussion. While CVE says “CVE Identifiers are unique, common identifiers for publicly known information security vulnerabilities” , it is more important to note the way CVE abstracts, which is covered in great detail. From the CVE page on abstraction:
CVE Abstraction Content Decisions (CDs) provide guidelines about when to combine multiple reports, bugs, and/or attack vectors into a single CVE name (“MERGE”), and when to create separate CVE names (“SPLIT”).
This clearly denotes that a single CVE may represent multiple vulnerabilities. With that in mind, every statistic generated by NSS Labs for this report is not accurate, and their numbers are not reproduceable using any other vulnerability dataset (unless it too is only based on CVE data and does not abstract differently, e.g. NVD). This distinction puts the report’s statements and conclusions in a different light:
As of January 2013 the NVD listed 53,489 vulnerabilities ..
In the last ten years on average 4,660 vulnerabilities were disclosed per year ..
.. with an all-‐time high of 6,462 vulnerabilities counted in 2006 ..
The abstraction distinction means that these numbers aren’t just technically inaccurate (i.e. terminology), they are factually inaccurate (i.e. actual stats when abstracting on a per-vulnerability basis). In each case where Frei uses the term “vulnerability”, he really means “CVE”. When you consider that a single CVE may cover as many as 66 or more distinct vulnerabilities, it really invalidates any statistic generated using this dataset as he did. For example:
However, in 2012 alone the number of vulnerabilities increased again to a considerable 5,225 (80% of the all-‐time high), which is 12% above the ten-‐year average. This is the largest increase observed in the past six years and ends the trend of moderate declines since 2006.
Based on my explanation, what does 5,225 really mean? If we agree for the sake of argument, that CVE averages two distinct vulnerabilities per CVE assignment, that is now over 10,000 vulnerabilities. How does that in turn change any observations on trending?
The report’s key findings offer 7 high-level conclusions based on the CVE data. To put all of the above in more perspective, I will examine a few of them and use an alternate dataset, OSVDB, that abstracts entries on a per-vulnerability basis. With those numbers, we can see how the findings stand. NSS Labs report text is quoted below.
The five year long trend in decreasing vulnerability disclosures ended abruptly in 2012 with a +12% increase
Based on OSVDB data, this is incorrect. Both 2009 (7,879) -> 2010 (8,835) as well as 2011 (7,565) -> 2012 (8,919) showed an upward trend.
More than 90 percent of the vulnerabilities disclosed are moderately or highly critical – and therefore relevant
If we assume “moderately” is “Medium” criticality, as later defined in the report, is 4.0 -‐ 6.9 then OSVDB shows 57,373 entries that are CVSSv2 4.0 – 10.0, out of 82,123 total. That means 90% is considerably higher than we show. Note: we do not have complete CVSSv2 data for 100% of our entries, but we do have them for all entries affiliated with the ones Frei examined and more. If “moderately critical” and “highly critical” refer to different ranges, then they should be more clearly defined.
It is also important to note that this finding is a red herring, due to the way CVSS scoring works. A remote path disclosure in a web application scores a 5.0 base score (CVSS2#AV:N/AC:L/Au:N/C:P/I:N/A:N). This skews the scoring data considerably higher than many in the industry would agree with, as 5.0 is the same score you get for many XSS vulnerabilities that can have more serious impact.
9 percent of vulnerabilities disclosed in 2012 are extremely critical (with CVSS score>9.9) paired with low attack/exploitation complexity
This is another red herring, because any CVSS 10.0 score means that “low complexity” was factored in. The wording in the report implies that a > 9.9 score could be paired with higher complexity, which isn’t possible. Further, CVSS is scored for the worst case scenario when details are not available (e.g. CVE-2012-5895). Given the number of “unspecified” issues, this may seriously skew the number of CVSSv2 10.0 scores.
Finally, there was one other element to this report that was used in the overview, and later in the document, that is used to attribute a shift in disclosure trends. From the overview:
The parallel and massive drop of vulnerability disclosures by the two long established purchase programs iDefense VCP and TippingPoint ZDI indicate a transition in the way vulnerability and exploit information is handled in the industry.
I believe this is a case of “correlation does not mean causation“. While these are the two most recognized third-party bug bounty programs around, there are many variables at play here. In the bigger picture, shifts in these programs do not necessarily mean anything. Some of the factors that may have influenced disclosure numbers for those two programs include:
- There are more bug bounty programs available. Some may offer better price or incentive for disclosing through them, stealing business from iDefense/ZDI.
- Both companies have enjoyed their share of internal politics that affected at least one program. In 2012, several people involved in the ZDI program left the company to form their startup. It has been theorized that since their departure, ZDI has not built the team back up and that disclosures were affected as a result.
- ZDI had a small bout of external politics, in which one of their most prevalent bounty collectors (Luigi Auriemma) had a serious disagreement about ZDI’s handling of a vulnerability, as relates to Portnoy and Exodus. Auriemma’s shift to disclose via his own company would dramatically affect ZDI disclosure totals alone.
- Both of these companies have a moving list of software that they offer a bounty on. As it changes, it may result in spikes of disclosures via their programs.
Regardless, iDefense and ZDI represent a small percentage of overall disclosures, it is curious that Frei opted to focus on this so prominently as a reason for vulnerability trends changing without considering some influencing factors. Even during a good year, 2011 for example, iDefense (42) and ZDI (297) together accounted for 339 out of 7,565 vulnerabilities, only ~ 4.5% of the overall disclosures. There are many other trends that could just as easily explain relatively small shifts in disclosure totals. When making statements about trends in vulnerability disclosure and how it affects statistics, it isn’t something that should be done by casual observers. They simply miss a lot of the low-level details you glean on the day-to-day vulnerability handling and cataloging.
To be clear, I am not against using CVE/NVD data to generate statistics. However, when doing so, it is important that the dataset be explained and qualified before going into analysis. The perception and definition of what “a vulnerability” is changes based on the person or VDB. In vulnerability statistics, not all vulnerabilities are created equal.