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Category: Security

Permissions Requirements for ML Services

Published 2019-04-24 by Kevin Feasel

Niels Berglund looks at the permissions required to create external libraries with SQL Server Machine Learning Services:

This post is the fourth in a series about installing R packages in SQL Server Machine Learning Services (SQL Server ML Services). To see all posts in the series go to Install R Packages in SQL Server ML Services Series.

Why this series came about is a colleague of mine Dane pinged me and asked if I had any advice as he had issues installing an R package into one of their SQL Server instances. I tried to help him and then thought it would make a good topic for a blog post. Of course, at that time I didn’t think it would be more posts than one, but here we are.

These permissions are a bit more complicated than they might first appear to be.

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Multi-Tenant Security in Kudu + Impala

Published 2019-04-23 by Kevin Feasel

Grant Henke shows how you can combine Apache Impala’s fine-grained authorization with Apache Kudu’s coarse-grained authentication for multi-tenant scenarios:

Kudu supports coarse-grained authorization of client requests based on the authenticated client Kerberos principal. The two levels of access which can be configured are:
1. Superuser – principals authorized as a superuser are able to perform certain administrative functionality such as using the kudu command line tool to diagnose or repair cluster issues.
2.User – principals authorized as a user are able to access and modify all data in the Kudu cluster. This includes the ability to create, drop, and alter tables as well as read, insert, update, and delete data.

Access levels are granted using whitelist-style Access Control Lists (ACLs), one for each of the two levels. 

Read on to see how to tie it all together.

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Importing a Private Key From VARBINARY

Published 2019-04-23 by Kevin Feasel

Solomon Rutzky tries out various methods of loading certificates and private keys in SQL Server:

These results confirm that:
1. You can import a certificate from a VARBINARY literal
2. You can import a private key when creating a certificate from a VARBINARY literal
3. You cannot import a private key when creating a certificate from an assembly
4. Except when creating a certificate from an assembly, any combination of sources for the certificate (i.e. public key and meta-data) and the private key should be valid

It’s a long post with a lot of detail and quite a few tests, so check it out.

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Data Breach Causes

Published 2019-04-23 by Kevin Feasel

Grant Fritchey takes us through some of the immediate causes of data breaches:

Fine, let’s talk about a business then. How about 24 million loan records, including bank account information, email, phones, social security numbers and all the rest. Yeah, that was sitting on an Elasticsearch database with no password of any kind. Oh, and the S3 storage was completely open too. Security? Is that still a thing?

How about exposing your entire client list because you left the password off the database (Elasticsearch again, is it hard to add a password to Elasticsearch). How about stacks of resumes (ElasticSearch, again, and MongoDB).

Those are just breaches from this year. If we go back, we can find more and more. Please, put a password on your systems. 

The OWASP Top 10 application security risks is out there and provides a lot of useful information on how to prevent the problems Grant mentions.

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The Prevalence of Persistent XSS

Published 2019-04-12 by Kevin Feasel

Adrian Colyer has a review of a security-minded paper:

Does your web application make use of local storage? If so, then like many developers you may well be making the assumption that when you read from local storage, it will only contain the data that you put there. As Steffens et al. show in this paper, that’s a dangerous assumption! The storage aspect of local storage makes possible a particularly nasty form of attack known as a persistent client-side cross-site scripting attack. Such an attack, once it has embedded itself in your browser one time (e.g. that one occasion you quickly had to jump on the coffee shop wifi), continues to work on all subsequent visits to the target site (e.g., once you’re back home on a trusted network).

In an analysis of the top 5000 Alexa domains, 21% of sites that make use of data originating from storage were found to contain vulnerabilities, of which at least 70% were directly exploitable using the models described in this paper.

Adrian’s been on a security paper kick the last few days, so be sure to check those out.

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Using Windows Authentication on Non-Windows Devices

Published 2019-04-05 by Kevin Feasel

Drew Furgiuele shows us how to connect to SQL Server using Windows Authentication if you’re not coming from a Windows device:

SQL Server supports different kinds of authentication mechanisms and protocols: the older NTLM protocol, and Kerberos. A lot of people cringe when you mention Kerberos because, well, Kerberos is hard. It’s arcane, it’s complex, and it’s hard to even describe unless you use it on the regular.

Simply put, it’s a ticketing and key system: you, a user, requests a ticket from a store, usually by authenticating to it via a username and password. If you succeed, you get a ticket that get stored within your local machine. Then, when you want to access a resource (like a SQL Server), the client re-ups with the store you got your initial ticket from (to make sure it’s still valid), and you get a “key” to access the resource. That key is then forwarded onto the resource, allowing you to access the thing you were trying to connect to. It’s way, way more complex than this, with lots of complicated terms and moving parts, so I’m doing a lot of hand-waving, but that’s the core of the system. If that kind of stuff excites you, go Google it, and I promise you’ll get more than you ever bargained for.

Kerberos is a scary beast to me, mostly because I don’t spend enough time working directly with it.

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Deploying Azure Databricks in a Custom VNET

Published 2019-03-29 by Kevin Feasel

Abhinav Garg and Anna Shrestinian explain how you can use VNET injection with Azure Databricks:

To make the above possible, we provide a Bring Your Own VNET (also called VNET Injection) feature, which allows customers to deploy the Azure Databricks clusters (data plane) in their own-managed VNETs. Such workspaces could be deployed using Azure Portal, or in an automated fashion using ARM Templates, which could be run using Azure CLI, Azure Powershell, Azure Python SDK, etc.

With this capability, the Databricks workspace NSG is also managed by the customer. We manage a set of inbound and outbound NSG rules using a Network Intent Policy, as those are required for secure, bidirectional communication with the control/management plane. 

This is a good article if the defaults won’t get past corporate security.

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Backing Up SSRS Encryption Keys

Published 2019-03-25 by Kevin Feasel

Jonathan Kehayias reminds us to back up those SQL Server Reporting Services encryption keys:

If you run SQL Server Reporting Services, part of your DR plan needs to include a backup of the encryption key for SSRS. This sadly is an all to often overlooked part of the solution, even though it is incredibly easy to do. If you don’t have a backup of the encryption key during a restore, the report server will never be able to decrypt the encrypted content (connection strings, passwords, etc) stored in the database, and your only recourse would be to delete the encrypted content and recreate it manually or through a redeployment of datasources.

Jonathan includes a couple of links to good resources. Your backups are only good if they include all of the keys and certificates you used. But keep those certificates stored someplace other than where the backups are stored.

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L-Diversity versus K-Anonymity

Published 2019-03-15 by Kevin Feasel

Duncan Greaves explains the concepts behind l-diversity:

There are problems with K-anonymous datasets, namely the homogeneous pattern attack, and the background knowledge attack, details of which are in my original post. A slightly different approach to anonymising public datasets comes in the form of ℓ -diversity, a way of introducing further entropy/diversity into a dataset.

A sensitive data record is made of the following microdata types: the ID; any Key Attributes; and the confidential outcome attribute(s). ℓ -diversity seeks to extend the equivalence classes that we created using K-anonymity by generalisation and masking of the quasi-identifiers (the QI groups) to the confidential attributes in the record as well. The ℓ -diversity principle demands that, in each QI-group, at most 1/ ℓ of its tuples can have an identical sensitive attribute value.

L-diversity is not perfect either, but Duncan gives a good explanation of the topic.

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