Most reported CVEs for Docker Hub photos are innocent

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In the course of the improvement of JFrog Xray’s Secrets Detection, we examined its capabilities by scanning greater than eight million artifacts in in style open-source bundle registries. Equally, for JFrog Xray’s new Container Contextual Analysis characteristic, we once more examined our detection in a large-scale, real-world use case, each for eliminating bugs and for assessing the real-world viability of our present answer.

Nevertheless, not like the surprising results we bought in our Secrets and techniques Detection analysis (we found many extra energetic entry tokens than we bargained for), the outcomes of our scans of Docker Hub container photos have been in keeping with what we have been seeing, as safety engineers, for a few years now.

Particularly, when the metric for a susceptible system is solely “bundle X is put in,” we anticipate most safety alerts to be false positives. And this was precisely the case for the CVEs we detected in container photos on Docker Hub.

On this publish we are going to element our analysis methodology and findings and provide some recommendation for builders and safety professionals trying to cut back the amount of CVE false positives. 

Exploitable vs. ‘susceptible bundle is put in’

Earlier than diving in, let’s briefly have a look at some instance vulnerabilities to know circumstances the place a CVE report may very well be thought of a false optimistic, even when a susceptible element exists.

This isn’t an exhaustive listing by any means, however it does cowl essentially the most outstanding causes of CVE false positives.

Library vulnerabilities

jfrog cca 01 JFrog

Does the truth that a susceptible model of Lodash is put in assure a susceptible system?

No. By definition, we can not decide whether or not a CVE in a library is exploitable just by noting that the library is put in. It’s because a library is just not a runnable entity; there have to be another code within the system that makes use of the library in a susceptible method.

Within the instance above, even when Lodash is put in, the system is probably not susceptible. There have to be some code that calls the susceptible perform, on this case template(), from the susceptible Lodash library. Generally, there are even further necessities, corresponding to that one of many arguments handed to template() could be attacker-controlled.

Different code-related stipulations might embrace:

  • Whether or not a mitigating perform known as earlier than the susceptible perform.
  • Whether or not particular arguments of the susceptible perform are set to particular susceptible values.

Service configuration

jfrog cca 02 JFrog

Does the truth that a susceptible model of Cassandra is put in assure a susceptible system?

No. In most fashionable service vulnerabilities (particularly ones with extreme impression) the vulnerability solely manifests in non-default configurations of the service. It’s because the default and sane configuration is commonly examined essentially the most, both by the builders themselves or just by the real-world customers of the service.

Within the instance above, to realize distant code execution (RCE), the Cassandra service have to be configured with three non-default configuration flags (considered one of them being fairly uncommon).

Different configuration-related stipulations might embrace:

  • Whether or not the element is being run with particular command-line arguments or setting variables.
  • Whether or not the susceptible element was compiled with particular construct flags.

Operating setting

jfrog cca 03 JFrog

Does the truth that a susceptible model of Apache Hadoop is put in assure a susceptible system?

No. Within the instance above, the vulnerability solely manifests in a Microsoft Home windows setting. Subsequently if the susceptible element is put in in a Linux setting, it can’t be exploited.

Different environment-related stipulations might embrace:

  • Whether or not the susceptible element is working in a selected distribution (e.g. Debian)
  • Whether or not the susceptible element is compiled for a selected structure (e.g. 32-bit Arm).
  • Whether or not a firewall blocks communication to the susceptible service.

Our analysis methodology

On this analysis, we got down to discover what share of vulnerability reviews really point out that the vulnerability is exploitable, when contemplating two reporting strategies:

  1. Naive. The vulnerability is reported every time a susceptible element is put in within the related (susceptible) model vary. That is how nearly all SCA instruments work immediately.
  2. Context-sensitive. The vulnerability is just reported (or stated to be relevant) if the context of the picture signifies susceptible utilization of the element. This takes under consideration elements that have been mentioned within the earlier part (code stipulations, configuration stipulations, working setting).

We’re inquisitive about testing the above in widespread real-world environments, and performing this take a look at on as many environments as potential.

jfrog cca 04 JFrog

We realized that Docker Hub’s top “community” images ought to fulfill each requests, for 2 causes:

  1. These photos are used extraordinarily often. For instance, the highest 25 photos at the moment have greater than 1 billion downloads.
  2. Neighborhood photos often include each an fascinating element and the code that makes use of the element to some finish, which offers a practical context. That is not like “official” Docker photos that often include standalone parts which might be left unused and of their default configuration. For instance, an Nginx internet server by itself with default configuration would in all probability not be inclined to any main CVE, however it doesn’t present a practical state of affairs.

Primarily based on these elements, we arrived on the following methodology:

  1. Pull Docker Hub’s high 200 group photos, of their “newest” tag.
  2. Collect from these photos the highest 10 most “in style” CVEs (sorted by CVE prevalence throughout all photos).
  3. Run our contextual evaluation on all 200 photos.
  4. Calculate the share of the naive methodology false optimistic fee, by dividing “non-applicable occurrences” by “whole occurrences” for every of the highest 10 CVEs.

What have been the highest 10 CVEs?

And so we scanned Docker Hub’s high 200 group photos. The desk beneath lists the CVEs that appeared within the highest variety of photos.



Brief description



zlib by means of 1.2.12 has a heap-based buffer over-read or buffer overflow in inflate().

Solely functions that decision inflateGetHeader are affected.



ncurses 6.3 has an out-of-bounds learn and segmentation violation in convert_strings()



ncurses by means of v6.2 nc_captoinfo() has a heap-based buffer overflow



Golang x/crypto/acme/autocert: httpTokenCacheKey permits restricted listing traversal on Home windows



Golang internet/http earlier than 1.18.6 DoS as a result of an HTTP/2 connection can cling



Golang math/massive earlier than 1.17.13 Float.GobDecode and Rat.GobDecode DoS resulting from panic



Golang encoding/xml earlier than 1.17.12 Decoder.Skip DoS resulting from stack exhaustion



Golang io/fs earlier than 1.17.12 Glob DoS resulting from uncontrolled recursion



Golang compress/gzip earlier than 1.17.12 Reader.Learn DoS resulting from uncontrolled recursion



Golang path/filepath earlier than 1.17.12 Glob DoS resulting from stack exhaustion

What number of CVEs have been really exploitable?

We intentionally selected to run the contextual scanners on their most conservative setting — extra on that within the subsequent part.

The contextual scanner for every CVE was outlined as described within the desk beneath.


Contextual scanner


Test for 1st-party code that calls “inflateGetHeader” and “inflate”


Test for invocations of the ncurses “tic” CLI utility


Test for invocations of the ncurses “cap2info” CLI utility


Test for Home windows OS + 1st-party code that calls “autocert.NewListener” or references “autocert.DirCache”


Test for 1st-party code that calls “ListenAndServeTLS” (HTTP/2 is just obtainable over TLS)


Test for 1st-party code that calls “Rat.GobDecode” or “Float.GobDecode”


Test for 1st-party code that calls “Decoder.Skip”


Test for 1st-party code that calls “fs.Glob” with non-constant enter


Test for 1st-party code that calls “gzip.Reader.Learn”


Test for 1st-party code that calls “filepath.Glob” with non-constant enter

Operating the contextual scanners on all 200 photos gave us the next outcomes, per CVE.

jfrog cca 06 JFrog

And once we tallied the outcomes of all high 10 CVEs collectively, right here’s what we found:

jfrog cca 07 JFrog

78% of the CVE circumstances have been discovered to be non-applicable!

Wanting on the present limits of contextual evaluation

Let’s look at CVE-2022-30631, which had an exceptionally excessive applicability fee.

CVE-2022-30631 was the one one which crossed 50% applicability. In layman’s phrases, the prerequisite for this CVE to be exploitable is “Golang is used to extract an attacker-controlled gzip archive.” In actuality, the scanner will alert if first-party Golang code tries to extract any gzip archive. It’s because guaranteeing whether or not a file is attacker-controlled is a really laborious process, because of the multitude of potential sources affecting the file.

When attempting to find out whether or not a variable comes from fixed enter or exterior/attacker enter, this may be achieved for instance through knowledge circulate evaluation. Information circulate evaluation is carried out by a few of our scanners, detecting CVE-2022-30630, for instance, which had a a lot decrease applicability fee.

jfrog cca 08 JFrog

However when coping with recordsdata, even when the perform’s file path argument is fixed, there isn’t a assure that the file is just not attacker-controlled, and vice versa. Subsequently, we anticipate the real-world applicability of this CVE to be even decrease.

Why 78% is definitely a conservative quantity

From the instance above, we will see that some CVEs might have an exaggerated applicability fee, that means the real-world applicability could also be even decrease. You will need to focus on why (within the widespread case) it nonetheless is smart to run conservative scanners. There are two causes for this: first, as a result of we choose false positives to false unfavourable, and second, due to efficiency concerns.

Choice in direction of false positives and never false negatives

Each expertise has its limitations, and that is doubly true when attempting to unravel computationally infeasible issues corresponding to “can a sure enter be managed by exterior sources.” In sure circumstances (the simpler ones), we will make sure assumptions that make the computation of the answer potential with extraordinarily excessive confidence.

Nevertheless, in different circumstances (the more durable one), the place 100% confidence is just not assured, we should always do two issues:

  1. Want scanners that have a tendency to indicate false positives (in our case, present outcomes as relevant when in actuality they don’t seem to be). That is carried out as a result of, on this case, the consequence will probably be examined by an engineer and evaluated whether or not it’s actually relevant or not. Within the reverse case, the place the vulnerability could be flagged as non-applicable, the engineer will assume it may be ignored, and thus the vulnerability could be left susceptible, which is a way more extreme state of affairs.
  2. At any time when potential, present the boldness fee and/or the explanation for low confidence in a selected discovering, in order that even relevant outcomes might be prioritized by safety/devops engineers.

Efficiency concerns

A contextual scanner that’s primarily based on knowledge circulate evaluation (for instance, a scanner that tries to find out whether or not a selected perform’s argument is coming from attacker-controlled enter or not) will all the time have an choice in its implementation whether or not to supply extra correct outcomes or to run sooner. For instance, essentially the most correct sort of contextual scanner should not less than:

  1. Permit for an infinite name depth when attempting to construct an intra-module knowledge circulate graph between an attacker-controlled supply and the requested sink.
  2. Contemplate inter-module calls when constructing the information circulate graph.

These operations enormously enhance the scanner’s run time.

When coping with a large amount of scanned artifacts per minute (as could also be requested from a JFrog Artifactory/Xray occasion) we should obtain a fragile stability between the accuracy and the velocity of the contextual scanner.

Even when contemplating the mentioned limitations, 78% continues to be a large variety of vulnerabilities that may be both de-prioritized or ignored. Moreover, we anticipate this quantity to turn into increased as expertise advances and as much less “relevant by default” CVEs are found.

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