No left boundary for Vulnerability Detection

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It’s another common problem in nearly all Vulnerability Management products. In the post “What’s wrong with patch-based Vulnerability Management checks?” I wrote about the issues in plugin descriptions, now let’s see what can go wrong with the detection logic.

The problem is that Vulnerability Management vendors, in many cases, have no idea which versions of the Software were actually vulnerable.

OMG?! How this can be true? 🙂 Let’s take an example.

Each vulnerability at some points in time:

  • was implemented in the program code as a result of some mistake (intentional or not)
  • existed in some versions of the program
  • was detected and fixed

Read more about this in “Vulnerability Life Cycle and Vulnerability Disclosures“.

No left boundary in Vulnerability Detection

Let’s suppose that we have some Software A with released versions 1, 2 … 20.

Just before the release of version 10, some programmer made a mistake (bug) in the code and since the version 10 Software A has become critically vulnerable. Before the release of version 20, Software Vendor was informed about this vulnerability and some programmer fixed it in version 20. Then Software Vendor released a security bulletin: “Critical vulnerabilities in the Software A. You are not vulnerable if you have installed the latest version 20.”

And what does Vulnerability Management vendor? This vendor only sees this security bulletin. It is logical for him to decide that all versions of Software A starting from 1 are vulnerable. So, it will mark installed versions 1 … 9 of the Software A as vulnerable, even so actually they are NOT.

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Retrieving data from Splunk Dashboard Panels via API

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Fist of all, why might someone want to get data from the panels of a dashboard in Splunk? Why it might be useful? Well, if the script can process everything that human analyst sees on a Splunk dashboard, all the automation comes very natural. You just figure out what routine operations the analyst usually does using the dashboard and repeat his actions in the script as is. It may be the anomaly detection, remediation task creation, reaction on various events, whatever. It really opens endless possibilities without alerts, reports and all this stuff. I’m very excited about this. 🙂

Exporting data from Splunk dashboard

Let’s say we have a Splunk dashboard and want to get data from the table panel using a python script. The problem is that the content of the table that we see is not actually stored anywhere. In fact it is the results of some search query, from the XML representation of the dashboard, executed by Splunk web GUI. To get this data we should execute the same search request.

That’s why we should:

  1. Get XML code of the dashboard
  2. Get the search query for each panel
  3. Process searches based on other searches and get complete search query for each panel
  4. Launch the search request and get the results

First of all, we need to create a special account that will be used for getting data from Splunk. In Web GUI “Access controls -> Users”.

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Open Positioner: my new project for tracking IT and security jobs

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The idea of my new project is to retrieve the data from job-searching websites and provide better filtering, searching and visualization.

I think for the most people who read this, searching for a job in Internet is a pretty common activity. Even if you are not going to change job right now, it might be quite interesting to know what skills are currently the most valuable for your specialization and what is going on on the Global labor market.

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Vulnerability Life Cycle and Vulnerability Disclosures

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Vulnerability Life Cycle diagram shows possible states of the vulnerability. In a previous post I suggested to treat vulnerabilities as bugs. Every known vulnerability, as same as every bug, was implemented by some software developer at some moment of time and was fixed at some moment of time later. What happens between this two events?

Vulnerability life-cycle

Right after the vulnerability was implemented in the code by some developer (creation) nobody knows about it. Well, of course, if it was done unintentionally. By the way, making backdoors look like an ordinary vulnerabilities it’s a smart way to do such things. 😉 But let’s say it WAS done unintentionally.

Time passed and some researcher found (discovery) this vulnerability and described it somehow. What’s next? It depends on who was that researcher.

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What is a vulnerability and what is not?

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It looks like a pretty simple question. I used it to started my MIPT lecture. But actually the answer is not so obvious. There are lots of formal definitions of a vulnerability. For example in NIST Glossary there are 17 different definitions. The most popular one (used in 13 documents) is:

Vulnerability is a weakness in an information system, system security procedures, internal controls, or implementation that could be exploited or triggered by a threat source
NISTIR 7435 The Common Vulnerability Scoring System (CVSS) and Its Applicability to Federal Agency Systems

But I prefer this one, it’s from the glossary as well:

Vulnerability is a bug, flaw, weakness, or exposure of an application, system, device, or service that could lead to a failure of confidentiality, integrity, or availability.

I think the best way to talk about vulnerabilities is to treat them as bugs and errors. Because people deal with such entities more often in a form of software freezes and BSODs. 😉

You probably heard a joke, that a bug can be presented as a feature if it is well-documented and the software developers don’t want to fix it.

Bug, feature and vulnerability

Vulnerability is also a specific bug that can lead to some security issues. Or at least it is declared.

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Creating Splunk Alerts using API

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As I mentioned in “Accelerating Splunk Dashboards with Base Searches and Saved Searches“, Splunk Reports are basically the Saved Searches. Moreover, Splunk Alerts are also the same Saved Searches with some additional parameters.

Creating Splunk Alerts using API

The question is what parameters you need to set to get the right Alert.

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What’s wrong with patch-based Vulnerability Management checks?

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My last post about Guinea Pigs and Vulnerability Management products may seem unconvincing without some examples. So, let’s review one. It’s a common problem that exists among nearly all VM vendors, I will demonstrate it on Tenable Nessus.

If you perform vulnerability scans, you most likely seen these pretty huge checks in your scan results like “KB4462917: Winsdows 10 Version 1607 and Windows Server 2016 October 2018 Security Update“. This particular Nessus plugin detects 23 CVEs at once.

What's wrong with patch-centric Vulnerability Management?

And, as you can see, it has formalized “Risk Information” data in the right column. There is only one CVSS score and vector, one CPE, one exploitability flag, one criticality level. Probably because of architectural limitations of the scanner. So, two very simple questions:

  • for which CVE (of these 23) is this formalized Risk Information block?
  • for which CVE (of these 23) exploit is available?

Ok, maybe they show CVSS for the most critical (by their logic) CVE. Maybe they somehow combine this parameter from data for different CVEs. But in most cases this will be inaccurate. Risk information data for every of these 23 vulnerabilities should be presented independently.

As you can see on the screenshot, one of these vulnerabilities is RCE the other is Information Disclosure. Vulnerability Management solution tells us that there is an exploit. Is this exploit for RCE or DoS? You should agree, that it can be crucial for vulnerability prioritization. And more than this, in the example there are 7 different RCEs in Internet Explorer, MSXML parser, Windows Hyper-V, etc. All this mean different attack scenarios. How is it possible to show it Vulnerability Scanner like one entity with one CVSS and exploitability flag? What can the user get from this? How to search in all this?

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