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What’s the Diff: Programs, Processes, and Threads

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/whats-the-diff-programs-processes-and-threads/

let's talk about Threads

How often have you heard the term threading in relation to a computer program, but you weren’t exactly sure what it meant? How about processes? You likely understand that a thread is somehow closely related to a program and a process, but if you’re not a computer science major, maybe that’s as far as your understanding goes.

Knowing what these terms mean is absolutely essential if you are a programmer, but an understanding of them also can be useful to the average computer user. Being able to look at and understand the Activity Monitor on the Macintosh, the Task Manager on Windows, or Top on Linux can help you troubleshoot which programs are causing problems on your computer, or whether you might need to install more memory to make your system run better.

Let’s take a few minutes to delve into the world of computer programs and sort out what these terms mean. We’ll simplify and generalize some of the ideas, but the general concepts we cover should help clarify the difference between the terms.

Programs

First of all, you probably are aware that a program is the code that is stored on your computer that is intended to fulfill a certain task. There are many types of programs, including programs that help your computer function and are part of the operating system, and other programs that fulfill a particular job. These task-specific programs are also known as “applications,” and can include programs such as word processing, web browsing, or emailing a message to another computer.

Program

Programs are typically stored on disk or in non-volatile memory in a form that can be executed by your computer. Prior to that, they are created using a programming language such as C, Lisp, Pascal, or many others using instructions that involve logic, data and device manipulation, recurrence, and user interaction. The end result is a text file of code that is compiled into binary form (1’s and 0’s) in order to run on the computer. Another type of program is called “interpreted,” and instead of being compiled in advance in order to run, is interpreted into executable code at the time it is run. Some common, typically interpreted programming languages, are Python, PHP, JavaScript, and Ruby.

The end result is the same, however, in that when a program is run, it is loaded into memory in binary form. The computer’s CPU (Central Processing Unit) understands only binary instructions, so that’s the form the program needs to be in when it runs.

Perhaps you’ve heard the programmer’s joke, “There are only 10 types of people in the world, those who understand binary, and those who don’t.”

Binary is the native language of computers because an electrical circuit at its basic level has two states, on or off, represented by a one or a zero. In the common numbering system we use every day, base 10, each digit position can be anything from 0 to 9. In base 2 (or binary), each position is either a 0 or a 1. (In a future blog post we might cover quantum computing, which goes beyond the concept of just 1’s and 0’s in computing.)

Decimal—Base 10 Binary—Base 2
0 0000
1 0001
2 0010
3 0011
4 0100
5 0101
6 0110
7 0111
8 1000
9 1001

How Processes Work

The program has been loaded into the computer’s memory in binary form. Now what?

An executing program needs more than just the binary code that tells the computer what to do. The program needs memory and various operating system resources that it needs in order to run. A “process” is what we call a program that has been loaded into memory along with all the resources it needs to operate. The “operating system” is the brains behind allocating all these resources, and comes in different flavors such as macOS, iOS, Microsoft Windows, Linux, and Android. The OS handles the task of managing the resources needed to turn your program into a running process.

Some essential resources every process needs are registers, a program counter, and a stack. The “registers” are data holding places that are part of the computer processor (CPU). A register may hold an instruction, a storage address, or other kind of data needed by the process. The “program counter,” also called the “instruction pointer,” keeps track of where a computer is in its program sequence. The “stack” is a data structure that stores information about the active subroutines of a computer program and is used as scratch space for the process. It is distinguished from dynamically allocated memory for the process that is known as “the heap.”

diagram of how processes work

There can be multiple instances of a single program, and each instance of that running program is a process. Each process has a separate memory address space, which means that a process runs independently and is isolated from other processes. It cannot directly access shared data in other processes. Switching from one process to another requires some time (relatively) for saving and loading registers, memory maps, and other resources.

This independence of processes is valuable because the operating system tries its best to isolate processes so that a problem with one process doesn’t corrupt or cause havoc with another process. You’ve undoubtedly run into the situation in which one application on your computer freezes or has a problem and you’ve been able to quit that program without affecting others.

How Threads Work

So, are you still with us? We finally made it to threads!

A thread is the unit of execution within a process. A process can have anywhere from just one thread to many threads.

Process vs. Thread

diagram of threads in a process over time

When a process starts, it is assigned memory and resources. Each thread in the process shares that memory and resources. In single-threaded processes, the process contains one thread. The process and the thread are one and the same, and there is only one thing happening.

In multithreaded processes, the process contains more than one thread, and the process is accomplishing a number of things at the same time (technically, it’s almost at the same time—read more on that in the “What about Parallelism and Concurrency?” section below).

diagram of single and multi-treaded process

We talked about the two types of memory available to a process or a thread, the stack and the heap. It is important to distinguish between these two types of process memory because each thread will have its own stack, but all the threads in a process will share the heap.

Threads are sometimes called lightweight processes because they have their own stack but can access shared data. Because threads share the same address space as the process and other threads within the process, the operational cost of communication between the threads is low, which is an advantage. The disadvantage is that a problem with one thread in a process will certainly affect other threads and the viability of the process itself.

Threads vs. Processes

So to review:

  1. The program starts out as a text file of programming code,
  2. The program is compiled or interpreted into binary form,
  3. The program is loaded into memory,
  4. The program becomes one or more running processes.
  5. Processes are typically independent of each other,
  6. While threads exist as the subset of a process.
  7. Threads can communicate with each other more easily than processes can,
  8. But threads are more vulnerable to problems caused by other threads in the same process.

Processes vs. Threads — Advantages and Disadvantages

Process Thread
Processes are heavyweight operations Threads are lighter weight operations
Each process has its own memory space Threads use the memory of the process they belong to
Inter-process communication is slow as processes have different memory addresses Inter-thread communication can be faster than inter-process communication because threads of the same process share memory with the process they belong to
Context switching between processes is more expensive Context switching between threads of the same process is less expensive
Processes don’t share memory with other processes Threads share memory with other threads of the same process

What about Concurrency and Parallelism?

A question you might ask is whether processes or threads can run at the same time. The answer is: it depends. On a system with multiple processors or CPU cores (as is common with modern processors), multiple processes or threads can be executed in parallel. On a single processor, though, it is not possible to have processes or threads truly executing at the same time. In this case, the CPU is shared among running processes or threads using a process scheduling algorithm that divides the CPU’s time and yields the illusion of parallel execution. The time given to each task is called a “time slice.” The switching back and forth between tasks happens so fast it is usually not perceptible. The terms parallelism (true operation at the same time) and concurrency (simulated operation at the same time), distinguish between the two type of real or approximate simultaneous operation.

diagram of concurrency and parallelism

Why Choose Process over Thread, or Thread over Process?

So, how would a programmer choose between a process and a thread when creating a program in which she wants to execute multiple tasks at the same time? We’ve covered some of the differences above, but let’s look at a real world example with a program that many of us use, Google Chrome.

When Google was designing the Chrome browser, they needed to decide how to handle the many different tasks that needed computer, communications, and network resources at the same time. Each browser window or tab communicates with multiple servers on the internet to retrieve text, programs, graphics, audio, video, and other resources, and renders that data for display and interaction with the user. In addition, the browser can open many windows, each with many tasks.

Google had to decide how to handle that separation of tasks. They chose to run each browser window in Chrome as a separate process rather than a thread or many threads, as is common with other browsers. Doing that brought Google a number of benefits. Running each window as a process protects the overall application from bugs and glitches in the rendering engine and restricts access from each rendering engine process to others and to the rest of the system. Isolating JavaScript programs in a process prevents them from running away with too much CPU time and memory, and making the entire browser non-responsive.

Google made the calculated trade-off with a multi-processing design as starting a new process for each browser window has a higher fixed cost in memory and resources than using threads. They were betting that their approach would end up with less memory bloat overall.

Using processes instead of threads provides better memory usage when memory gets low. An inactive window is treated as a lower priority by the operating system and becomes eligible to be swapped to disk when memory is needed for other processes, helping to keep the user-visible windows more responsive. If the windows were threaded, it would be more difficult to separate the used and unused memory as cleanly, wasting both memory and performance.

You can read more about Google’s design decisions on Google’s Chromium Blog or on the Chrome Introduction Comic.

The screen capture below shows the Google Chrome processes running on a MacBook Air with many tabs open. Some Chrome processes are using a fair amount of CPU time and resources, and some are using very little. You can see that each process also has many threads running as well.

activity monitor of Google Chrome

The Activity Monitor or Task Manager on your system can be a valuable ally in helping fine-tune your computer or troubleshooting problems. If your computer is running slowly, or a program or browser window isn’t responding for a while, you can check its status using the system monitor. Sometimes you’ll see a process marked as “Not Responding.” Try quitting that process and see if your system runs better. If an application is a memory hog, you might consider choosing a different application that will accomplish the same task.

Windows Task Manager view

Made it This Far?

We hope this Tron-like dive into the fascinating world of computer programs, processes, and threads has helped clear up some questions you might have had.

The next time your computer is running slowly or an application is acting up, you know your assignment. Fire up the system monitor and take a look under the hood to see what’s going on. You’re in charge now.

We love to hear from you

Are you still confused? Have questions? If so, please let us know in the comments. And feel free to suggest topics for future blog posts.

The post What’s the Diff: Programs, Processes, and Threads appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Launch – AWS Glue Now Generally Available

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/launch-aws-glue-now-generally-available/

Today we’re excited to announce the general availability of AWS Glue. Glue is a fully managed, serverless, and cloud-optimized extract, transform and load (ETL) service. Glue is different from other ETL services and platforms in a few very important ways.

First, Glue is “serverless” – you don’t need to provision or manage any resources and you only pay for resources when Glue is actively running. Second, Glue provides crawlers that can automatically detect and infer schemas from many data sources, data types, and across various types of partitions. It stores these generated schemas in a centralized Data Catalog for editing, versioning, querying, and analysis. Third, Glue can automatically generate ETL scripts (in Python!) to translate your data from your source formats to your target formats. Finally, Glue allows you to create development endpoints that allow your developers to use their favorite toolchains to construct their ETL scripts. Ok, let’s dive deep with an example.

In my job as a Developer Evangelist I spend a lot of time traveling and I thought it would be cool to play with some flight data. The Bureau of Transportations Statistics is kind enough to share all of this data for anyone to use here. We can easily download this data and put it in an Amazon Simple Storage Service (S3) bucket. This data will be the basis of our work today.

Crawlers

First, we need to create a Crawler for our flights data from S3. We’ll select Crawlers in the Glue console and follow the on screen prompts from there. I’ll specify s3://crawler-public-us-east-1/flight/2016/csv/ as my first datasource (we can add more later if needed). Next, we’ll create a database called flights and give our tables a prefix of flights as well.

The Crawler will go over our dataset, detect partitions through various folders – in this case months of the year, detect the schema, and build a table. We could add additonal data sources and jobs into our crawler or create separate crawlers that push data into the same database but for now let’s look at the autogenerated schema.

I’m going to make a quick schema change to year, moving it from BIGINT to INT. Then I can compare the two versions of the schema if needed.

Now that we know how to correctly parse this data let’s go ahead and do some transforms.

ETL Jobs

Now we’ll navigate to the Jobs subconsole and click Add Job. Will follow the prompts from there giving our job a name, selecting a datasource, and an S3 location for temporary files. Next we add our target by specifying “Create tables in your data target” and we’ll specify an S3 location in Parquet format as our target.

After clicking next, we’re at screen showing our various mappings proposed by Glue. Now we can make manual column adjustments as needed – in this case we’re just going to use the X button to remove a few columns that we don’t need.

This brings us to my favorite part. This is what I absolutely love about Glue.

Glue generated a PySpark script to transform our data based on the information we’ve given it so far. On the left hand side we can see a diagram documenting the flow of the ETL job. On the top right we see a series of buttons that we can use to add annotated data sources and targets, transforms, spigots, and other features. This is the interface I get if I click on transform.

If we add any of these transforms or additional data sources, Glue will update the diagram on the left giving us a useful visualization of the flow of our data. We can also just write our own code into the console and have it run. We can add triggers to this job that fire on completion of another job, a schedule, or on demand. That way if we add more flight data we can reload this same data back into S3 in the format we need.

I could spend all day writing about the power and versatility of the jobs console but Glue still has more features I want to cover. So, while I might love the script editing console, I know many people prefer their own development environments, tools, and IDEs. Let’s figure out how we can use those with Glue.

Development Endpoints and Notebooks

A Development Endpoint is an environment used to develop and test our Glue scripts. If we navigate to “Dev endpoints” in the Glue console we can click “Add endpoint” in the top right to get started. Next we’ll select a VPC, a security group that references itself and then we wait for it to provision.


Once it’s provisioned we can create an Apache Zeppelin notebook server by going to actions and clicking create notebook server. We give our instance an IAM role and make sure it has permissions to talk to our data sources. Then we can either SSH into the server or connect to the notebook to interactively develop our script.

Pricing and Documentation

You can see detailed pricing information here. Glue crawlers, ETL jobs, and development endpoints are all billed in Data Processing Unit Hours (DPU) (billed by minute). Each DPU-Hour costs $0.44 in us-east-1. A single DPU provides 4vCPU and 16GB of memory.

We’ve only covered about half of the features that Glue has so I want to encourage everyone who made it this far into the post to go read the documentation and service FAQs. Glue also has a rich and powerful API that allows you to do anything console can do and more.

We’re also releasing two new projects today. The aws-glue-libs provide a set of utilities for connecting, and talking with Glue. The aws-glue-samples repo contains a set of example jobs.

I hope you find that using Glue reduces the time it takes to start doing things with your data. Look for another post from me on AWS Glue soon because I can’t stop playing with this new service.
Randall

AWS Migration Hub – Plan & Track Enterprise Application Migration

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-migration-hub-plan-track-enterprise-application-migration/

About once a week, I speak to current and potential AWS customers in our Seattle Executive Briefing Center. While I generally focus on our innovation process, we sometimes discuss other topics, including application migration. When enterprises decide to migrate their application portfolios they want to do it in a structured, orderly fashion. These portfolios typically consist of hundreds of complex Windows and Linux applications, relational databases, and more. Customers find themselves eager yet uncertain as to how to proceed. After spending time working with these customers, we have learned that their challenges generally fall in to three major categories:

Discovery – They want to make sure that they have a deep and complete understanding of all of the moving parts that power each application.

Server & Database Migration – They need to transfer on-premises workloads and database tables to the cloud.

Tracking / Management – With large application portfolios and multiple migrations happening in parallel, they need to track and manage progress in an application-centric fashion.

Over the last couple of years we have launched a set of tools that address the first two challenges. The AWS Application Discovery Service automates the process of discovering and collecting system information, the AWS Server Migration Service takes care of moving workloads to the cloud, and the AWS Database Migration Service moves relational databases, NoSQL databases, and data warehouses with minimal downtime. Partners like Racemi and CloudEndure also offer migration tools of their own.

New AWS Migration Hub
Today we are bringing this collection of AWS and partner migration tools together in the AWS Migration Hub. The hub provides access to the tools that I mentioned above, guides you through the migration process, and tracks the status of each migration, all in accord with the methodology and tenets described in our Migration Acceleration Program (MAP).

Here’s the main screen. It outlines the migration process (discovery, migration, and tracking):

Clicking on Start discovery reveals the flow of the migration process:

It is also possible to skip the Discovery step and begin the migration immediately:

The Servers list is populated using data from an AWS migration service (Server Migration Service or Database Migration Service), partner tools, or using data collected by the AWS Application Discovery Service:

I can on Group as application to create my first application:

Once I identify some applications to migrate, I can track them in the Migrations section of the Hub:

The migration tools, if authorized, automatically send status updates and results back to Migration Hub, for display on the migration status page for the application. Here you can see that Racemi DynaCenter and CloudEndure Migration have played their parts in the migration:

I can track the status of my migrations by checking the Migration Hub Dashboard:

Migration Hub works with migration tools from AWS and our Migration Partners; see the list of integrated partner tools to learn more:

Available Now
AWS Migration Hub can manage migrations in any AWS Region that has the necessary migration tools available; the hub itself runs in the US West (Oregon) Region. There is no charge for the Hub; you pay only for the AWS services that you consume in the course of the migration.

If you are ready to begin your migration to the cloud and are in need of some assistance, please take advantage of the services offered by our Migration Acceleration Partners. These organizations have earned their migration competency by repeatedly demonstrating their ability to deliver large-scale migration.

Jeff;

Growing up alongside tech

Post Syndicated from Eevee original https://eev.ee/blog/2017/08/09/growing-up-alongside-tech/

IndustrialRobot asks… or, uh, asked last month:

industrialrobot: How has your views on tech changed as you’ve got older?

This is so open-ended that it’s actually stumped me for a solid month. I’ve had a surprisingly hard time figuring out where to even start.


It’s not that my views of tech have changed too much — it’s that they’ve changed very gradually. Teasing out and explaining any one particular change is tricky when it happened invisibly over the course of 10+ years.

I think a better framework for this is to consider how my relationship to tech has changed. It’s gone through three pretty distinct phases, each of which has strongly colored how I feel and talk about technology.

Act I

In which I start from nothing.

Nothing is an interesting starting point. You only really get to start there once.

Learning something on my own as a kid was something of a magical experience, in a way that I don’t think I could replicate as an adult. I liked computers; I liked toying with computers; so I did that.

I don’t know how universal this is, but when I was a kid, I couldn’t even conceive of how incredible things were made. Buildings? Cars? Paintings? Operating systems? Where does any of that come from? Obviously someone made them, but it’s not the sort of philosophical point I lingered on when I was 10, so in the back of my head they basically just appeared fully-formed from the æther.

That meant that when I started trying out programming, I had no aspirations. I couldn’t imagine how far I would go, because all the examples of how far I would go were completely disconnected from any idea of human achievement. I started out with BASIC on a toy computer; how could I possibly envision a connection between that and something like a mainstream video game? Every new thing felt like a new form of magic, so I couldn’t conceive that I was even in the same ballpark as whatever process produced real software. (Even seeing the source code for GORILLAS.BAS, it didn’t quite click. I didn’t think to try reading any of it until years after I’d first encountered the game.)

This isn’t to say I didn’t have goals. I invented goals constantly, as I’ve always done; as soon as I learned about a new thing, I’d imagine some ways to use it, then try to build them. I produced a lot of little weird goofy toys, some of which entertained my tiny friend group for a couple days, some of which never saw the light of day. But none of it felt like steps along the way to some mountain peak of mastery, because I didn’t realize the mountain peak was even a place that could be gone to. It was pure, unadulterated (!) playing.

I contrast this to my art career, which started only a couple years ago. I was already in my late 20s, so I’d already spend decades seeing a very broad spectrum of art: everything from quick sketches up to painted masterpieces. And I’d seen the people who create that art, sometimes seen them create it in real-time. I’m even in a relationship with one of them! And of course I’d already had the experience of advancing through tech stuff and discovering first-hand that even the most amazing software is still just code someone wrote.

So from the very beginning, from the moment I touched pencil to paper, I knew the possibilities. I knew that the goddamn Sistine Chapel was something I could learn to do, if I were willing to put enough time in — and I knew that I’m not, so I’d have to settle somewhere a ways before that. I knew that I’d have to put an awful lot of work in before I’d be producing anything very impressive.

I did it anyway (though perhaps waited longer than necessary to start), but those aren’t things I can un-know, and so I can never truly explore art from a place of pure ignorance. On the other hand, I’ve probably learned to draw much more quickly and efficiently than if I’d done it as a kid, precisely because I know those things. Now I can decide I want to do something far beyond my current abilities, then go figure out how to do it. When I was just playing, that kind of ambition was impossible.


So, I played.

How did this affect my views on tech? Well, I didn’t… have any. Learning by playing tends to teach you things in an outward sprawl without many abrupt jumps to new areas, so you don’t tend to run up against conflicting information. The whole point of opinions is that they’re your own resolution to a conflict; without conflict, I can’t meaningfully say I had any opinions. I just accepted whatever I encountered at face value, because I didn’t even know enough to suspect there could be alternatives yet.

Act II

That started to seriously change around, I suppose, the end of high school and beginning of college. I was becoming aware of this whole “open source” concept. I took classes that used languages I wouldn’t otherwise have given a second thought. (One of them was Python!) I started to contribute to other people’s projects. Eventually I even got a job, where I had to work with other people. It probably also helped that I’d had to maintain my own old code a few times.

Now I was faced with conflicting subjective ideas, and I had to form opinions about them! And so I did. With gusto. Over time, I developed an idea of what was Right based on experience I’d accrued. And then I set out to always do things Right.

That’s served me decently well with some individual problems, but it also led me to inflict a lot of unnecessary pain on myself. Several endeavors languished for no other reason than my dissatisfaction with the architecture, long before the basic functionality was done. I started a number of “pure” projects around this time, generic tools like imaging libraries that I had no direct need for. I built them for the sake of them, I guess because I felt like I was improving some niche… but of course I never finished any. It was always in areas I didn’t know that well in the first place, which is a fine way to learn if you have a specific concrete goal in mind — but it turns out that building a generic library for editing images means you have to know everything about images. Perhaps that ambition went a little haywire.

I’ve said before that this sort of (self-inflicted!) work was unfulfilling, in part because the best outcome would be that a few distant programmers’ lives are slightly easier. I do still think that, but I think there’s a deeper point here too.

In forgetting how to play, I’d stopped putting any of myself in most of the work I was doing. Yes, building an imaging library is kind of a slog that someone has to do, but… I assume the people who work on software like PIL and ImageMagick are actually interested in it. The few domains I tried to enter and revolutionize weren’t passions of mine; I just happened to walk through the neighborhood one day and decided I could obviously do it better.

Not coincidentally, this was the same era of my life that led me to write stuff like that PHP post, which you may notice I am conspicuously not even linking to. I don’t think I would write anything like it nowadays. I could see myself approaching the same subject, but purely from the point of view of language design, with more contrasts and tradeoffs and less going for volume. I certainly wouldn’t lead off with inflammatory puffery like “PHP is a community of amateurs”.

Act III

I think I’ve mellowed out a good bit in the last few years.

It turns out that being Right is much less important than being Not Wrong — i.e., rather than trying to make something perfect that can be adapted to any future case, just avoid as many pitfalls as possible. Code that does something useful has much more practical value than unfinished code with some pristine architecture.

Nowhere is this more apparent than in game development, where all code is doomed to be crap and the best you can hope for is to stem the tide. But there’s also a fixed goal that’s completely unrelated to how the code looks: does the game work, and is it fun to play? Yes? Ship the damn thing and forget about it.

Games are also nice because it’s very easy to pour my own feelings into them and evoke feelings in the people who play them. They’re mine, something with my fingerprints on them — even the games I’ve built with glip have plenty of my own hallmarks, little touches I added on a whim or attention to specific details that I care about.

Maybe a better example is the Doom map parser I started writing. It sounds like a “pure” problem again, except that I actually know an awful lot about the subject already! I also cleverly (accidentally) released some useful results of the work I’ve done thusfar — like statistics about Doom II maps and a few screenshots of flipped stock maps — even though I don’t think the parser itself is far enough along to release yet. The tool has served a purpose, one with my fingerprints on it, even without being released publicly. That keeps it fresh in my mind as something interesting I’d like to keep working on, eventually. (When I run into an architecture question, I step back for a while, or I do other work in the hopes that the solution will reveal itself.)

I also made two simple Pokémon ROM hacks this year, despite knowing nothing about Game Boy internals or assembly when I started. I just decided I wanted to do an open-ended thing beyond my reach, and I went to do it, not worrying about cleanliness and willing to accept a bumpy ride to get there. I played, but in a more experienced way, invoking the stuff I know (and the people I’ve met!) to help me get a running start in completely unfamiliar territory.


This feels like a really fine distinction that I’m not sure I’m doing justice. I don’t know if I could’ve appreciated it three or four years ago. But I missed making toys, and I’m glad I’m doing it again.

In short, I forgot how to have fun with programming for a little while, and I’ve finally started to figure it out again. And that’s far more important than whether you use PHP or not.

Hackers Leak More Confidential Game of Thrones Files

Post Syndicated from Ernesto original https://torrentfreak.com/hackers-leak-more-confidential-game-of-thrones-files-170808/

Last week, news broke that a hacker, or hackers, had compromised the network of the American cable and television network HBO.

Those responsible sent out an email to reporters, announcing the prominent breach, and leaked files surfaced on the dedicated website Winter-leak.com.

While the latter is no longer accessible, the hackers are not done yet. Another curated batch of leaked files has now appeared online, revealing more Game of Thrones spoilers, marketing plans, and other confidential HBO files.

The first leak put a preliminary outline of the fourth episode of the current Game of Thrones season in the spotlight, and the second batch follows up with the same for the upcoming fifth episode.

Although the outline was prepared over a year ago, it likely contains various accurate spoilers, which we won’t repeat here.

Preliminary outline S07E05

The new data dump, which is a subsection of the 1.5 terabytes of data the hackers claimed to have in their possession, also lists a variety of other Game of Thrones related files.

Among other items, there’s a confidential cast list for the current season, a highly confidential “Game of Ideas” brief, an outline of GoT marketing strategies, and a Game of Thrones roadmap. The information all appears to be a few months old.

The hackers took a screenshot of several folders, where the files may have been taken from, as seen below.

Folders screenshot

In addition, the hackers provided ‘proof’ that they have emails, which according to AP point to HBO’s vice president for film programming Leslie Cohen.

Finally, the new batch contains a video letter to HBO CEO Richard Plepler, titled “First letter to HBO,” where a certain Mr. Smith takes credit for the hack. The letter offered to keep the information away from the public, in exchange for a ransom payment.

First letter to HBO

For spoiler-eager Game of Thrones fans the hack is a true treasure trove. However, like the first batch, no leaked episodes are included. And, based on another screenshot, these are probably not on the way either.

A “Series Screenshot” includes a list of likely compromised titles, such as The Deviant Ones and the previously leaked Barry, Ballers, and Room 104, but no Game of Thrones.

A leak of the fourth GoT episode did appear online late last week, but this wasn’t linked to the breach of HBO’s network. Still, HBO is likely not amused and will do everything in its power to catch those responsible.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

Query name minimization

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/08/query-name-minimization.html

One new thing you need to add your DNS security policies is “query name minimizations” (RFC 7816). I thought I’d mention it since many haven’t heard about it.

Right now, when DNS resolvers lookup a name like “www.example.com.”, they send the entire name to the root server (like a.root-servers.net.). When it gets back the answer to the .com DNS server a.gtld-servers.net), it then resends the full “www.example.com” query to that server.

This is obviously unnecessary. The first query should be just .com. to the root server, then example.com. to the next server — the minimal amount needed for each query, not the full query.

The reason this is important is that everyone is listening in on root name server queries. Universities and independent researchers do this to maintain the DNS system, and to track malware. Security companies do this also to track malware, bots, command-and-control channels, and so forth. The world’s biggest spy agencies do this in order just to spy on people. Minimizing your queries prevents them from spying on you.

An example where this is important is that story of lookups from AlfaBank in Russia for “mail1.trump-emails.com”. Whatever you think of Trump, this was an improper invasion of privacy, where DNS researchers misused their privileged access in order to pursue their anti-Trump political agenda. If AlfaBank had used query name minimization, none of this would have happened.

It’s also critical for not exposing internal resources. Even when you do “split DNS”, when the .com record expires, you resolver will still forward the internal DNS record to the outside world. All those Russian hackers can map out the internal names of your network simply by eavesdropping on root server queries.

Servers that support this are Knot resolver and Unbound 1.5.7+ and possibly others. It’s a relatively new standard, so it make take a while for other DNS servers to support this.

Deploying an NGINX Reverse Proxy Sidecar Container on Amazon ECS

Post Syndicated from Nathan Peck original https://aws.amazon.com/blogs/compute/nginx-reverse-proxy-sidecar-container-on-amazon-ecs/

Reverse proxies are a powerful software architecture primitive for fetching resources from a server on behalf of a client. They serve a number of purposes, from protecting servers from unwanted traffic to offloading some of the heavy lifting of HTTP traffic processing.

This post explains the benefits of a reverse proxy, and explains how to use NGINX and Amazon EC2 Container Service (Amazon ECS) to easily implement and deploy a reverse proxy for your containerized application.

Components

NGINX is a high performance HTTP server that has achieved significant adoption because of its asynchronous event driven architecture. It can serve thousands of concurrent requests with a low memory footprint. This efficiency also makes it ideal as a reverse proxy.

Amazon ECS is a highly scalable, high performance container management service that supports Docker containers. It allows you to run applications easily on a managed cluster of Amazon EC2 instances. Amazon ECS helps you get your application components running on instances according to a specified configuration. It also helps scale out these components across an entire fleet of instances.

Sidecar containers are a common software pattern that has been embraced by engineering organizations. It’s a way to keep server side architecture easier to understand by building with smaller, modular containers that each serve a simple purpose. Just like an application can be powered by multiple microservices, each microservice can also be powered by multiple containers that work together. A sidecar container is simply a way to move part of the core responsibility of a service out into a containerized module that is deployed alongside a core application container.

The following diagram shows how an NGINX reverse proxy sidecar container operates alongside an application server container:

In this architecture, Amazon ECS has deployed two copies of an application stack that is made up of an NGINX reverse proxy side container and an application container. Web traffic from the public goes to an Application Load Balancer, which then distributes the traffic to one of the NGINX reverse proxy sidecars. The NGINX reverse proxy then forwards the request to the application server and returns its response to the client via the load balancer.

Reverse proxy for security

Security is one reason for using a reverse proxy in front of an application container. Any web server that serves resources to the public can expect to receive lots of unwanted traffic every day. Some of this traffic is relatively benign scans by researchers and tools, such as Shodan or nmap:

[18/May/2017:15:10:10 +0000] "GET /YesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScann HTTP/1.1" 404 1389 - Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.86 Safari/537.36
[18/May/2017:18:19:51 +0000] "GET /clientaccesspolicy.xml HTTP/1.1" 404 322 - Cloud mapping experiment. Contact [email protected]

But other traffic is much more malicious. For example, here is what a web server sees while being scanned by the hacking tool ZmEu, which scans web servers trying to find PHPMyAdmin installations to exploit:

[18/May/2017:16:27:39 +0000] "GET /mysqladmin/scripts/setup.php HTTP/1.1" 404 391 - ZmEu
[18/May/2017:16:27:39 +0000] "GET /web/phpMyAdmin/scripts/setup.php HTTP/1.1" 404 394 - ZmEu
[18/May/2017:16:27:39 +0000] "GET /xampp/phpmyadmin/scripts/setup.php HTTP/1.1" 404 396 - ZmEu
[18/May/2017:16:27:40 +0000] "GET /apache-default/phpmyadmin/scripts/setup.php HTTP/1.1" 404 405 - ZmEu
[18/May/2017:16:27:40 +0000] "GET /phpMyAdmin-2.10.0.0/scripts/setup.php HTTP/1.1" 404 397 - ZmEu
[18/May/2017:16:27:40 +0000] "GET /mysql/scripts/setup.php HTTP/1.1" 404 386 - ZmEu
[18/May/2017:16:27:41 +0000] "GET /admin/scripts/setup.php HTTP/1.1" 404 386 - ZmEu
[18/May/2017:16:27:41 +0000] "GET /forum/phpmyadmin/scripts/setup.php HTTP/1.1" 404 396 - ZmEu
[18/May/2017:16:27:41 +0000] "GET /typo3/phpmyadmin/scripts/setup.php HTTP/1.1" 404 396 - ZmEu
[18/May/2017:16:27:42 +0000] "GET /phpMyAdmin-2.10.0.1/scripts/setup.php HTTP/1.1" 404 399 - ZmEu
[18/May/2017:16:27:44 +0000] "GET /administrator/components/com_joommyadmin/phpmyadmin/scripts/setup.php HTTP/1.1" 404 418 - ZmEu
[18/May/2017:18:34:45 +0000] "GET /phpmyadmin/scripts/setup.php HTTP/1.1" 404 390 - ZmEu
[18/May/2017:16:27:45 +0000] "GET /w00tw00t.at.blackhats.romanian.anti-sec:) HTTP/1.1" 404 401 - ZmEu

In addition, servers can also end up receiving unwanted web traffic that is intended for another server. In a cloud environment, an application may end up reusing an IP address that was formerly connected to another service. It’s common for misconfigured or misbehaving DNS servers to send traffic intended for a different host to an IP address now connected to your server.

It’s the responsibility of anyone running a web server to handle and reject potentially malicious traffic or unwanted traffic. Ideally, the web server can reject this traffic as early as possible, before it actually reaches the core application code. A reverse proxy is one way to provide this layer of protection for an application server. It can be configured to reject these requests before they reach the application server.

Reverse proxy for performance

Another advantage of using a reverse proxy such as NGINX is that it can be configured to offload some heavy lifting from your application container. For example, every HTTP server should support gzip. Whenever a client requests gzip encoding, the server compresses the response before sending it back to the client. This compression saves network bandwidth, which also improves speed for clients who now don’t have to wait as long for a response to fully download.

NGINX can be configured to accept a plaintext response from your application container and gzip encode it before sending it down to the client. This allows your application container to focus 100% of its CPU allotment on running business logic, while NGINX handles the encoding with its efficient gzip implementation.

An application may have security concerns that require SSL termination at the instance level instead of at the load balancer. NGINX can also be configured to terminate SSL before proxying the request to a local application container. Again, this also removes some CPU load from the application container, allowing it to focus on running business logic. It also gives you a cleaner way to patch any SSL vulnerabilities or update SSL certificates by updating the NGINX container without needing to change the application container.

NGINX configuration

Configuring NGINX for both traffic filtering and gzip encoding is shown below:

http {
  # NGINX will handle gzip compression of responses from the app server
  gzip on;
  gzip_proxied any;
  gzip_types text/plain application/json;
  gzip_min_length 1000;
 
  server {
    listen 80;
 
    # NGINX will reject anything not matching /api
    location /api {
      # Reject requests with unsupported HTTP method
      if ($request_method !~ ^(GET|POST|HEAD|OPTIONS|PUT|DELETE)$) {
        return 405;
      }
 
      # Only requests matching the whitelist expectations will
      # get sent to the application server
      proxy_pass http://app:3000;
      proxy_http_version 1.1;
      proxy_set_header Upgrade $http_upgrade;
      proxy_set_header Connection 'upgrade';
      proxy_set_header Host $host;
      proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
      proxy_cache_bypass $http_upgrade;
    }
  }
}

The above configuration only accepts traffic that matches the expression /api and has a recognized HTTP method. If the traffic matches, it is forwarded to a local application container accessible at the local hostname app. If the client requested gzip encoding, the plaintext response from that application container is gzip-encoded.

Amazon ECS configuration

Configuring ECS to run this NGINX container as a sidecar is also simple. ECS uses a core primitive called the task definition. Each task definition can include one or more containers, which can be linked to each other:

 {
  "containerDefinitions": [
     {
       "name": "nginx",
       "image": "<NGINX reverse proxy image URL here>",
       "memory": "256",
       "cpu": "256",
       "essential": true,
       "portMappings": [
         {
           "containerPort": "80",
           "protocol": "tcp"
         }
       ],
       "links": [
         "app"
       ]
     },
     {
       "name": "app",
       "image": "<app image URL here>",
       "memory": "256",
       "cpu": "256",
       "essential": true
     }
   ],
   "networkMode": "bridge",
   "family": "application-stack"
}

This task definition causes ECS to start both an NGINX container and an application container on the same instance. Then, the NGINX container is linked to the application container. This allows the NGINX container to send traffic to the application container using the hostname app.

The NGINX container has a port mapping that exposes port 80 on a publically accessible port but the application container does not. This means that the application container is not directly addressable. The only way to send it traffic is to send traffic to the NGINX container, which filters that traffic down. It only forwards to the application container if the traffic passes the whitelisted rules.

Conclusion

Running a sidecar container such as NGINX can bring significant benefits by making it easier to provide protection for application containers. Sidecar containers also improve performance by freeing your application container from various CPU intensive tasks. Amazon ECS makes it easy to run sidecar containers, and automate their deployment across your cluster.

To see the full code for this NGINX sidecar reference, or to try it out yourself, you can check out the open source NGINX reverse proxy reference architecture on GitHub.

– Nathan
 @nathankpeck

Weekly roundup: Downtime

Post Syndicated from Eevee original https://eev.ee/dev/2017/08/01/downtime/

I had a few inexplicably rough days, which I mostly spent playing Splatoon, but I’m good now!

  • blog: I got a preposterous amount of work done on a blog post, but unfortunately it’s turning out to be fairly ambitious, so it’s taking a while to finish. Now it’s August and I didn’t post anything at all in July. Oops.

  • vidya: I streamed three hours of playing randomly-chosen fan maps from the entire history of Doom. And for once I turned on VODs, so you can still watch it on Twitch until it’s automatically deleted in a few days.

    I also played, uh, one of the GAMES MADE QUICK??? 1½ entries, which is very shameful. I’ll make an effort to play a few more, soon, I guess?

  • fox flux: I significantly improved and finished grassy slopes, hooray. Drew some more player sprites; only like, uh, 120 to go. Played with particle effects, to (imo) great success.

Top 10 Most Obvious Hacks of All Time (v0.9)

Post Syndicated from Robert Graham original http://blog.erratasec.com/2017/07/top-10-most-obvious-hacks-of-all-time.html

For teaching hacking/cybersecurity, I thought I’d create of the most obvious hacks of all time. Not the best hacks, the most sophisticated hacks, or the hacks with the biggest impact, but the most obvious hacks — ones that even the least knowledgeable among us should be able to understand. Below I propose some hacks that fit this bill, though in no particular order.

The reason I’m writing this is that my niece wants me to teach her some hacking. I thought I’d start with the obvious stuff first.

Shared Passwords

If you use the same password for every website, and one of those websites gets hacked, then the hacker has your password for all your websites. The reason your Facebook account got hacked wasn’t because of anything Facebook did, but because you used the same email-address and password when creating an account on “beagleforums.com”, which got hacked last year.

I’ve heard people say “I’m sure, because I choose a complex password and use it everywhere”. No, this is the very worst thing you can do. Sure, you can the use the same password on all sites you don’t care much about, but for Facebook, your email account, and your bank, you should have a unique password, so that when other sites get hacked, your important sites are secure.

And yes, it’s okay to write down your passwords on paper.

Tools: HaveIBeenPwned.com

PIN encrypted PDFs

My accountant emails PDF statements encrypted with the last 4 digits of my Social Security Number. This is not encryption — a 4 digit number has only 10,000 combinations, and a hacker can guess all of them in seconds.
PIN numbers for ATM cards work because ATM machines are online, and the machine can reject your card after four guesses. PIN numbers don’t work for documents, because they are offline — the hacker has a copy of the document on their own machine, disconnected from the Internet, and can continue making bad guesses with no restrictions.
Passwords protecting documents must be long enough that even trillion upon trillion guesses are insufficient to guess.

Tools: Hashcat, John the Ripper

SQL and other injection

The lazy way of combining websites with databases is to combine user input with an SQL statement. This combines code with data, so the obvious consequence is that hackers can craft data to mess with the code.
No, this isn’t obvious to the general public, but it should be obvious to programmers. The moment you write code that adds unfiltered user-input to an SQL statement, the consequence should be obvious. Yet, “SQL injection” has remained one of the most effective hacks for the last 15 years because somehow programmers don’t understand the consequence.
CGI shell injection is a similar issue. Back in early days, when “CGI scripts” were a thing, it was really important, but these days, not so much, so I just included it with SQL. The consequence of executing shell code should’ve been obvious, but weirdly, it wasn’t. The IT guy at the company I worked for back in the late 1990s came to me and asked “this guy says we have a vulnerability, is he full of shit?”, and I had to answer “no, he’s right — obviously so”.

XSS (“Cross Site Scripting”) [*] is another injection issue, but this time at somebody’s web browser rather than a server. It works because websites will echo back what is sent to them. For example, if you search for Cross Site Scripting with the URL https://www.google.com/search?q=cross+site+scripting, then you’ll get a page back from the server that contains that string. If the string is JavaScript code rather than text, then some servers (thought not Google) send back the code in the page in a way that it’ll be executed. This is most often used to hack somebody’s account: you send them an email or tweet a link, and when they click on it, the JavaScript gives control of the account to the hacker.

Cross site injection issues like this should probably be their own category, but I’m including it here for now.

More: Wikipedia on SQL injection, Wikipedia on cross site scripting.
Tools: Burpsuite, SQLmap

Buffer overflows

In the C programming language, programmers first create a buffer, then read input into it. If input is long than the buffer, then it overflows. The extra bytes overwrite other parts of the program, letting the hacker run code.
Again, it’s not a thing the general public is expected to know about, but is instead something C programmers should be expected to understand. They should know that it’s up to them to check the length and stop reading input before it overflows the buffer, that there’s no language feature that takes care of this for them.
We are three decades after the first major buffer overflow exploits, so there is no excuse for C programmers not to understand this issue.

What makes particular obvious is the way they are wrapped in exploits, like in Metasploit. While the bug itself is obvious that it’s a bug, actually exploiting it can take some very non-obvious skill. However, once that exploit is written, any trained monkey can press a button and run the exploit. That’s where we get the insult “script kiddie” from — referring to wannabe-hackers who never learn enough to write their own exploits, but who spend a lot of time running the exploit scripts written by better hackers than they.

More: Wikipedia on buffer overflow, Wikipedia on script kiddie,  “Smashing The Stack For Fun And Profit” — Phrack (1996)
Tools: bash, Metasploit

SendMail DEBUG command (historical)

The first popular email server in the 1980s was called “SendMail”. It had a feature whereby if you send a “DEBUG” command to it, it would execute any code following the command. The consequence of this was obvious — hackers could (and did) upload code to take control of the server. This was used in the Morris Worm of 1988. Most Internet machines of the day ran SendMail, so the worm spread fast infecting most machines.
This bug was mostly ignored at the time. It was thought of as a theoretical problem, that might only rarely be used to hack a system. Part of the motivation of the Morris Worm was to demonstrate that such problems was to demonstrate the consequences — consequences that should’ve been obvious but somehow were rejected by everyone.

More: Wikipedia on Morris Worm

Email Attachments/Links

I’m conflicted whether I should add this or not, because here’s the deal: you are supposed to click on attachments and links within emails. That’s what they are there for. The difference between good and bad attachments/links is not obvious. Indeed, easy-to-use email systems makes detecting the difference harder.
On the other hand, the consequences of bad attachments/links is obvious. That worms like ILOVEYOU spread so easily is because people trusted attachments coming from their friends, and ran them.
We have no solution to the problem of bad email attachments and links. Viruses and phishing are pervasive problems. Yet, we know why they exist.

Default and backdoor passwords

The Mirai botnet was caused by surveillance-cameras having default and backdoor passwords, and being exposed to the Internet without a firewall. The consequence should be obvious: people will discover the passwords and use them to take control of the bots.
Surveillance-cameras have the problem that they are usually exposed to the public, and can’t be reached without a ladder — often a really tall ladder. Therefore, you don’t want a button consumers can press to reset to factory defaults. You want a remote way to reset them. Therefore, they put backdoor passwords to do the reset. Such passwords are easy for hackers to reverse-engineer, and hence, take control of millions of cameras across the Internet.
The same reasoning applies to “default” passwords. Many users will not change the defaults, leaving a ton of devices hackers can hack.

Masscan and background radiation of the Internet

I’ve written a tool that can easily scan the entire Internet in a short period of time. It surprises people that this possible, but it obvious from the numbers. Internet addresses are only 32-bits long, or roughly 4 billion combinations. A fast Internet link can easily handle 1 million packets-per-second, so the entire Internet can be scanned in 4000 seconds, little more than an hour. It’s basic math.
Because it’s so easy, many people do it. If you monitor your Internet link, you’ll see a steady trickle of packets coming in from all over the Internet, especially Russia and China, from hackers scanning the Internet for things they can hack.
People’s reaction to this scanning is weirdly emotional, taking is personally, such as:
  1. Why are they hacking me? What did I do to them?
  2. Great! They are hacking me! That must mean I’m important!
  3. Grrr! How dare they?! How can I hack them back for some retribution!?

I find this odd, because obviously such scanning isn’t personal, the hackers have no idea who you are.

Tools: masscan, firewalls

Packet-sniffing, sidejacking

If you connect to the Starbucks WiFi, a hacker nearby can easily eavesdrop on your network traffic, because it’s not encrypted. Windows even warns you about this, in case you weren’t sure.

At DefCon, they have a “Wall of Sheep”, where they show passwords from people who logged onto stuff using the insecure “DefCon-Open” network. Calling them “sheep” for not grasping this basic fact that unencrypted traffic is unencrypted.

To be fair, it’s actually non-obvious to many people. Even if the WiFi itself is not encrypted, SSL traffic is. They expect their services to be encrypted, without them having to worry about it. And in fact, most are, especially Google, Facebook, Twitter, Apple, and other major services that won’t allow you to log in anymore without encryption.

But many services (especially old ones) may not be encrypted. Unless users check and verify them carefully, they’ll happily expose passwords.

What’s interesting about this was 10 years ago, when most services which only used SSL to encrypt the passwords, but then used unencrypted connections after that, using “cookies”. This allowed the cookies to be sniffed and stolen, allowing other people to share the login session. I used this on stage at BlackHat to connect to somebody’s GMail session. Google, and other major websites, fixed this soon after. But it should never have been a problem — because the sidejacking of cookies should have been obvious.

Tools: Wireshark, dsniff

Stuxnet LNK vulnerability

Again, this issue isn’t obvious to the public, but it should’ve been obvious to anybody who knew how Windows works.
When Windows loads a .dll, it first calls the function DllMain(). A Windows link file (.lnk) can load icons/graphics from the resources in a .dll file. It does this by loading the .dll file, thus calling DllMain. Thus, a hacker could put on a USB drive a .lnk file pointing to a .dll file, and thus, cause arbitrary code execution as soon as a user inserted a drive.
I say this is obvious because I did this, created .lnks that pointed to .dlls, but without hostile DllMain code. The consequence should’ve been obvious to me, but I totally missed the connection. We all missed the connection, for decades.

Social Engineering and Tech Support [* * *]

After posting this, many people have pointed out “social engineering”, especially of “tech support”. This probably should be up near #1 in terms of obviousness.

The classic example of social engineering is when you call tech support and tell them you’ve lost your password, and they reset it for you with minimum of questions proving who you are. For example, you set the volume on your computer really loud and play the sound of a crying baby in the background and appear to be a bit frazzled and incoherent, which explains why you aren’t answering the questions they are asking. They, understanding your predicament as a new parent, will go the extra mile in helping you, resetting “your” password.

One of the interesting consequences is how it affects domain names (DNS). It’s quite easy in many cases to call up the registrar and convince them to transfer a domain name. This has been used in lots of hacks. It’s really hard to defend against. If a registrar charges only $9/year for a domain name, then it really can’t afford to provide very good tech support — or very secure tech support — to prevent this sort of hack.

Social engineering is such a huge problem, and obvious problem, that it’s outside the scope of this document. Just google it to find example after example.

A related issue that perhaps deserves it’s own section is OSINT [*], or “open-source intelligence”, where you gather public information about a target. For example, on the day the bank manager is out on vacation (which you got from their Facebook post) you show up and claim to be a bank auditor, and are shown into their office where you grab their backup tapes. (We’ve actually done this).

More: Wikipedia on Social Engineering, Wikipedia on OSINT, “How I Won the Defcon Social Engineering CTF” — blogpost (2011), “Questioning 42: Where’s the Engineering in Social Engineering of Namespace Compromises” — BSidesLV talk (2016)

Blue-boxes (historical) [*]

Telephones historically used what we call “in-band signaling”. That’s why when you dial on an old phone, it makes sounds — those sounds are sent no differently than the way your voice is sent. Thus, it was possible to make tone generators to do things other than simply dial calls. Early hackers (in the 1970s) would make tone-generators called “blue-boxes” and “black-boxes” to make free long distance calls, for example.

These days, “signaling” and “voice” are digitized, then sent as separate channels or “bands”. This is call “out-of-band signaling”. You can’t trick the phone system by generating tones. When your iPhone makes sounds when you dial, it’s entirely for you benefit and has nothing to do with how it signals the cell tower to make a call.

Early hackers, like the founders of Apple, are famous for having started their careers making such “boxes” for tricking the phone system. The problem was obvious back in the day, which is why as the phone system moves from analog to digital, the problem was fixed.

More: Wikipedia on blue box, Wikipedia article on Steve Wozniak.

Thumb drives in parking lots [*]

A simple trick is to put a virus on a USB flash drive, and drop it in a parking lot. Somebody is bound to notice it, stick it in their computer, and open the file.

This can be extended with tricks. For example, you can put a file labeled “third-quarter-salaries.xlsx” on the drive that required macros to be run in order to open. It’s irresistible to other employees who want to know what their peers are being paid, so they’ll bypass any warning prompts in order to see the data.

Another example is to go online and get custom USB sticks made printed with the logo of the target company, making them seem more trustworthy.

We also did a trick of taking an Adobe Flash game “Punch the Monkey” and replaced the monkey with a logo of a competitor of our target. They now only played the game (infecting themselves with our virus), but gave to others inside the company to play, infecting others, including the CEO.

Thumb drives like this have been used in many incidents, such as Russians hacking military headquarters in Afghanistan. It’s really hard to defend against.

More: “Computer Virus Hits U.S. Military Base in Afghanistan” — USNews (2008), “The Return of the Worm That Ate The Pentagon” — Wired (2011), DoD Bans Flash Drives — Stripes (2008)

Googling [*]

Search engines like Google will index your website — your entire website. Frequently companies put things on their website without much protection because they are nearly impossible for users to find. But Google finds them, then indexes them, causing them to pop up with innocent searches.
There are books written on “Google hacking” explaining what search terms to look for, like “not for public release”, in order to find such documents.

More: Wikipedia entry on Google Hacking, “Google Hacking” book.

URL editing [*]

At the top of every browser is what’s called the “URL”. You can change it. Thus, if you see a URL that looks like this:

http://www.example.com/documents?id=138493

Then you can edit it to see the next document on the server:

http://www.example.com/documents?id=138494

The owner of the website may think they are secure, because nothing points to this document, so the Google search won’t find it. But that doesn’t stop a user from manually editing the URL.
An example of this is a big Fortune 500 company that posts the quarterly results to the website an hour before the official announcement. Simply editing the URL from previous financial announcements allows hackers to find the document, then buy/sell the stock as appropriate in order to make a lot of money.
Another example is the classic case of Andrew “Weev” Auernheimer who did this trick in order to download the account email addresses of early owners of the iPad, including movie stars and members of the Obama administration. It’s an interesting legal case because on one hand, techies consider this so obvious as to not be “hacking”. On the other hand, non-techies, especially judges and prosecutors, believe this to be obviously “hacking”.

DDoS, spoofing, and amplification [*]

For decades now, online gamers have figured out an easy way to win: just flood the opponent with Internet traffic, slowing their network connection. This is called a DoS, which stands for “Denial of Service”. DoSing game competitors is often a teenager’s first foray into hacking.
A variant of this is when you hack a bunch of other machines on the Internet, then command them to flood your target. (The hacked machines are often called a “botnet”, a network of robot computers). This is called DDoS, or “Distributed DoS”. At this point, it gets quite serious, as instead of competitive gamers hackers can take down entire businesses. Extortion scams, DDoSing websites then demanding payment to stop, is a common way hackers earn money.
Another form of DDoS is “amplification”. Sometimes when you send a packet to a machine on the Internet it’ll respond with a much larger response, either a very large packet or many packets. The hacker can then send a packet to many of these sites, “spoofing” or forging the IP address of the victim. This causes all those sites to then flood the victim with traffic. Thus, with a small amount of outbound traffic, the hacker can flood the inbound traffic of the victim.
This is one of those things that has worked for 20 years, because it’s so obvious teenagers can do it, yet there is no obvious solution. President Trump’s executive order of cyberspace specifically demanded that his government come up with a report on how to address this, but it’s unlikely that they’ll come up with any useful strategy.

More: Wikipedia on DDoS, Wikipedia on Spoofing

Conclusion

Tweet me (@ErrataRob) your obvious hacks, so I can add them to the list.

CrackMapExec – Active Directory Post-Exploitation Tool

Post Syndicated from Darknet original http://feedproxy.google.com/~r/darknethackers/~3/r7Om66Zbxck/

CrackMapExec (a.k.a CME) is a post-exploitation tool that helps automate assessing the security of large Active Directory networks. Built with stealth in mind, CME follows the concept of “Living off the Land”: abusing built-in Active Directory features/protocols to achieve its functionality and allowing it to evade most endpoint protection/IDS/IPS…

Read the full post at darknet.org.uk

Time-lapse Visualizes Game of Thrones Piracy Around The Globe

Post Syndicated from Ernesto original https://torrentfreak.com/time-lapse-visualizes-game-of-thrones-piracy-around-the-globe-17-730/

Game of Thrones has been the most pirated TV-show online for years, and this isn’t expected to change anytime soon.

While most of today’s piracy takes place through streaming services, BitTorrent traffic remains significant as well. The show’s episodes are generally downloaded millions of times each, by people from all over the world.

In recent years there have been several attempts to quantify this piracy bonanza. While MILLIONS of downloads make for a good headline, there are some other trends worth looking at as well.

TorrentFreak spoke to Abigail De Kosnik, an Associate Professor at the University of California, Berkeley. Together with computer scientist and artist Benjamin De Kosnik, she runs the BitTorrent-oriented research project “alpha60.”

The goal of alpha60 is to quantify and map BitTorrent activity around various media titles, to make this “shadow economy” visible to media scholars and the general public. Over the past two weeks, they’ve taken a close look at Game of Thrones downloads.

Their tracking software collected swarm data from 72 torrents that were released shortly after the first episode premiered. Before being anonymized, the collected IP-addresses were first translated to geographical locations, to reveal various traffic patterns.

The results, summarized in a white paper, reveal that during the first five days, alpha60 registered an estimated 1.77 million downloads. Of particular interest is the five-day time-lapse of the worldwide swarm activity.

Five-day Game of Thrones piracy timelapse

The time-lapse shows that download patterns vary depending on the time of the day. There is a lot of activity in Asia, but cities such as Athens, Toronto, and Sao Paulo also pop up regularly.

When looking at the absolute numbers, Seoul comes out on top as the Game of Thrones download capital of the world, followed by Athens, São Paulo, Guangzhou, Mumbai, and Bangalore.

Perhaps more interesting is the view of the number of downloads relative to the population, or the “over-pirating” cities, as alpha60 calls them. Here, Dallas comes out on top, before Brisbane, Chicago, Riyadh, Saudi Arabia, Seattle, and Perth.

Of course, VPNs may skew the results somewhat, but overall the data should give a pretty accurate impression of the download traffic around the globe.

Below are the complete top tens of most active cities, both in absolute numbers and relative to the population. Further insights and additional information is available in the full whitepaper, which can be accessed here.

Note: The download totals reported by alpha60 are significantly lower than the MUSO figures that came out last week. Alpha60 stresses, however, that their methods and data are accurate. MUSO, for its part, has made some dubious claims in the past.

Most downloads (absolute)

1 Seoul, Rep. of Korea
2 Athens, Greece
3 São Paulo, Brazil
4 Guangzhou, China
5 Mumbai, India
6 Bangalore, India
7 Shanghai, China
8 Riyadh, Saudi Arabia
9 Delhi, India
10 Beijing, China

Most downloads (relative)

1 Dallas, USA
2 Brisbane, Australia
3 Chicago, USA
4 Riyadh, Saudi Arabia
5 Seattle, USA
6 Perth, Australia
7 Phoenix, USA
8 Toronto, Canada
9 Athens, Greece
10 Guangzhou, China

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

Announcing the Raspberry Jam Big Birthday Weekend 2018

Post Syndicated from Ben Nuttall original https://www.raspberrypi.org/blog/raspberry-jam-big-birthday-weekend-2018/

For the last few years, we have held a big Raspberry Pi community event in Cambridge around Raspberry Pi’s birthday, where people have come together for a huge party with talks, workshops, and more. We want more people to have the chance to join in with our birthday celebrations next year, so we’re going to be coordinating Raspberry Jams all over the world to take place over the Raspberry Jam Big Birthday Weekend, 3–4 March 2018.

Raspberry Pi Big Birthday Weekend 2018. GIF with confetti and bopping JAM balloons

Big Birthday fun!

Whether you’ve run a Raspberry Jam before, or you’d like to start a new Jam in your area, we invite you to join us for our Big Birthday Weekend, wherever you are in the world. This event will be a community-led, synchronised, global mega-Jam in celebration of our sixth birthday and the digital making community! Members of the Raspberry Pi Foundation team will be attending Jams far and wide to celebrate with you during the weekend.

Jams across the world will receive a special digital pack – be sure to register your interest so we can get your pack to you! We’ll also be sending out party kits to registered Jams – more info on this below.

Need help getting started?

First of all, check out the Raspberry Jam page to read all about Jams, and take a look at our recent blog post explaining the support for Jams that we offer.

If there’s no Jam near you yet, the Raspberry Jam Big Birthday Weekend is the perfect opportunity to start one yourself! If you’d like some help getting your Jam off the ground, there are a few places you can get support:

  • The Raspberry Jam Guidebook is full of advice gathered from the amazing people who run Jams in the UK.
  • The Raspberry Jam Slack team is available for Jam organisers to chat, share ideas, and get help from each other. Just email jam [at] raspberrypi.org and ask to be invited.
  • Attend a Jam! Find an upcoming Jam near you, and go along to get an idea of what it’s like.
  • Email us – if you have more queries, you can email jam [at] raspberrypi.org and we’ll do what we can to help.

Raspberry Jam

Get involved

If you’re keen to start a new Jam, there’s no need to wait until March – why not get up and running over the summer? Then you’ll be an expert by the time the Raspberry Jam Big Birthday Weekend comes around. Check out the guidebook, join the Jam Slack, and submit your event to the map when you’re ready.

Like the idea of running a Jam, but don’t want to do it by yourself? Then feel free to email us, and we’ll try and help you find someone to co-organise it.

If you don’t fancy organising a Jam for our Big Birthday Weekend, but would like to celebrate with us, keep an eye on our website for an update early next year. We’ll publish a full list of Jams participating in the festivities so you can find one near you. And if you’ve never attended a Jam before, there’s no need to wait: find one to join on the map here.

Raspberry Jam

Register your interest

If you think you’d like to run a Jam as part of the Big Birthday Weekend, register your interest now, and you’ll be the first to receive updates. Don’t worry if you don’t have the venue or logistics in place yet – this is just to let us know you’re keen, and to give us an idea about how big our party is going to be.

We will contact you in autumn to give you more information, as well as some useful resources. On top of our regular Raspberry Jam branding pack, we’ll provide a special digital Big Birthday Weekend pack to help you celebrate and tell everyone about your Jam!

Then, once you have confirmed you’re taking part, you’ll be able to register your Jam on our website. This will make sure that other people interested in joining the party can find your event. If your Jam is among the first 150 to be registered for a Big Birthday Weekend event, we will send you a free pack of goodies to use on the big day!

Go fill in the form, and we’ll be in touch!

 

PS: We’ll be running a big Cambridge event in the summer on the weekend of 30 June–1 July 2018. Put it in your diary – we’ll say more about it as we get closer to the date.

The post Announcing the Raspberry Jam Big Birthday Weekend 2018 appeared first on Raspberry Pi.

Run Common Data Science Packages on Anaconda and Oozie with Amazon EMR

Post Syndicated from John Ohle original https://aws.amazon.com/blogs/big-data/run-common-data-science-packages-on-anaconda-and-oozie-with-amazon-emr/

In the world of data science, users must often sacrifice cluster set-up time to allow for complex usability scenarios. Amazon EMR allows data scientists to spin up complex cluster configurations easily, and to be up and running with complex queries in a matter of minutes.

Data scientists often use scheduling applications such as Oozie to run jobs overnight. However, Oozie can be difficult to configure when you are trying to use popular Python packages (such as “pandas,” “numpy,” and “statsmodels”), which are not included by default.

One such popular platform that contains these types of packages (and more) is Anaconda. This post focuses on setting up an Anaconda platform on EMR, with an intent to use its packages with Oozie. I describe how to run jobs using a popular open source scheduler like Oozie.

Walkthrough

For this post, you walk through the following tasks:

  • Create an EMR cluster.
  • Download Anaconda on your master node.
  • Configure Oozie.
  • Test the steps.

Create an EMR cluster

Spin up an Amazon EMR cluster using the console or the AWS CLI. Use the latest release, and include Apache Hadoop, Apache Spark, Apache Hive, and Oozie.

To create a three-node cluster in the us-east-1 region, issue an AWS CLI command such as the following. This command must be typed as one line, as shown below. It is shown here separated for readability purposes only.

aws emr create-cluster \ 
--release-label emr-5.7.0 \ 
 --name '<YOUR-CLUSTER-NAME>' \
 --applications Name=Hadoop Name=Oozie Name=Spark Name=Hive \ 
 --ec2-attributes '{"KeyName":"<YOUR-KEY-PAIR>","SubnetId":"<YOUR-SUBNET-ID>","EmrManagedSlaveSecurityGroup":"<YOUR-EMR-SLAVE-SECURITY-GROUP>","EmrManagedMasterSecurityGroup":"<YOUR-EMR-MASTER-SECURITY-GROUP>"}' \ 
 --use-default-roles \ 
 --instance-groups '[{"InstanceCount":1,"InstanceGroupType":"MASTER","InstanceType":"<YOUR-INSTANCE-TYPE>","Name":"Master - 1"},{"InstanceCount":<YOUR-CORE-INSTANCE-COUNT>,"InstanceGroupType":"CORE","InstanceType":"<YOUR-INSTANCE-TYPE>","Name":"Core - 2"}]'

One-line version for reference:

aws emr create-cluster --release-label emr-5.7.0 --name '<YOUR-CLUSTER-NAME>' --applications Name=Hadoop Name=Oozie Name=Spark Name=Hive --ec2-attributes '{"KeyName":"<YOUR-KEY-PAIR>","SubnetId":"<YOUR-SUBNET-ID>","EmrManagedSlaveSecurityGroup":"<YOUR-EMR-SLAVE-SECURITY-GROUP>","EmrManagedMasterSecurityGroup":"<YOUR-EMR-MASTER-SECURITY-GROUP>"}' --use-default-roles --instance-groups '[{"InstanceCount":1,"InstanceGroupType":"MASTER","InstanceType":"<YOUR-INSTANCE-TYPE>","Name":"Master - 1"},{"InstanceCount":<YOUR-CORE-INSTANCE-COUNT>,"InstanceGroupType":"CORE","InstanceType":"<YOUR-INSTANCE-TYPE>","Name":"Core - 2"}]'

Download Anaconda

SSH into your EMR master node instance and download the official Anaconda installer:

wget https://repo.continuum.io/archive/Anaconda2-4.4.0-Linux-x86_64.sh

At the time of publication, Anaconda 4.4 is the most current version available. For the download link location for the latest Python 2.7 version (Python 3.6 may encounter issues), see https://www.continuum.io/downloads.  Open the context (right-click) menu for the Python 2.7 download link, choose Copy Link Location, and use this value in the previous wget command.

This post used the Anaconda 4.4 installation. If you have a later version, it is shown in the downloaded filename:  “anaconda2-<version number>-Linux-x86_64.sh”.

Run this downloaded script and follow the on-screen installer prompts.

chmod u+x Anaconda2-4.4.0-Linux-x86_64.sh
./Anaconda2-4.4.0-Linux-x86_64.sh

For an installation directory, select somewhere with enough space on your cluster, such as “/mnt/anaconda/”.

The process should take approximately 1–2 minutes to install. When prompted if you “wish the installer to prepend the Anaconda2 install location”, select the default option of [no].

After you are done, export the PATH to include this new Anaconda installation:

export PATH=/mnt/anaconda/bin:$PATH

Zip up the Anaconda installation:

cd /mnt/anaconda/
zip -r anaconda.zip .

The zip process may take 4–5 minutes to complete.

(Optional) Upload this anaconda.zip file to your S3 bucket for easier inclusion into future EMR clusters. This removes the need to repeat the previous steps for future EMR clusters.

Configure Oozie

Next, you configure Oozie to use Pyspark and the Anaconda platform.

Get the location of your Oozie sharelibupdate folder. Issue the following command and take note of the “sharelibDirNew” value:

oozie admin -sharelibupdate

For this post, this value is “hdfs://ip-192-168-4-200.us-east-1.compute.internal:8020/user/oozie/share/lib/lib_20170616133136”.

Pass in the required Pyspark files into Oozies sharelibupdate location. The following files are required for Oozie to be able to run Pyspark commands:

  • pyspark.zip
  • py4j-0.10.4-src.zip

These are located on the EMR master instance in the location “/usr/lib/spark/python/lib/”, and must be put into the Oozie sharelib spark directory. This location is the value of the sharelibDirNew parameter value (shown above) with “/spark/” appended, that is, “hdfs://ip-192-168-4-200.us-east-1.compute.internal:8020/user/oozie/share/lib/lib_20170616133136/spark/”.

To do this, issue the following commands:

hdfs dfs -put /usr/lib/spark/python/lib/py4j-0.10.4-src.zip hdfs://ip-192-168-4-200.us-east-1.compute.internal:8020/user/oozie/share/lib/lib_20170616133136/spark/
hdfs dfs -put /usr/lib/spark/python/lib/pyspark.zip hdfs://ip-192-168-4-200.us-east-1.compute.internal:8020/user/oozie/share/lib/lib_20170616133136/spark/

After you’re done, Oozie can use Pyspark in its processes.

Pass the anaconda.zip file into HDFS as follows:

hdfs dfs -put /mnt/anaconda/anaconda.zip /tmp/myLocation/anaconda.zip

(Optional) Verify that it was transferred successfully with the following command:

hdfs dfs -ls /tmp/myLocation/

On your master node, execute the following command:

export PYSPARK_PYTHON=/mnt/anaconda/bin/python

Set the PYSPARK_PYTHON environment variable on the executor nodes. Put the following configurations in your “spark-opts” values in your Oozie workflow.xml file:

–conf spark.executorEnv.PYSPARK_PYTHON=./anaconda_remote/bin/python
–conf spark.yarn.appMasterEnv.PYSPARK_PYTHON=./anaconda_remote/bin/python

This is referenced from the Oozie job in the following line in your workflow.xml file, also included as part of your “spark-opts”:

--archives hdfs:///tmp/myLocation/anaconda.zip#anaconda_remote

Your Oozie workflow.xml file should now look something like the following:

<workflow-app name="spark-wf" xmlns="uri:oozie:workflow:0.5">
<start to="start_spark" />
<action name="start_spark">
    <spark xmlns="uri:oozie:spark-action:0.1">
        <job-tracker>${jobTracker}</job-tracker>
        <name-node>${nameNode}</name-node>
        <prepare>
            <delete path="/tmp/test/spark_oozie_test_out3"/>
        </prepare>
        <master>yarn-cluster</master>
        <mode>cluster</mode>
        <name>SparkJob</name>
        <class>clear</class>
        <jar>hdfs:///user/oozie/apps/myPysparkProgram.py</jar>
        <spark-opts>--queue default
            --conf spark.ui.view.acls=*
            --executor-memory 2G --num-executors 2 --executor-cores 2 --driver-memory 3g
            --conf spark.executorEnv.PYSPARK_PYTHON=./anaconda_remote/bin/python
            --conf spark.yarn.appMasterEnv.PYSPARK_PYTHON=./anaconda_remote/bin/python
            --archives hdfs:///tmp/myLocation/anaconda.zip#anaconda_remote
        </spark-opts>
    </spark>
    <ok to="end"/>
    <error to="kill"/>
</action>
        <kill name="kill">
                <message>Action failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
        </kill>
        <end name="end"/>
</workflow-app>

Test steps

To test this out, you can use the following job.properties and myPysparkProgram.py file, along with the following steps:

job.properties

masterNode ip-xxx-xxx-xxx-xxx.us-east-1.compute.internal
nameNode hdfs://${masterNode}:8020
jobTracker ${masterNode}:8032
master yarn
mode cluster
queueName default
oozie.libpath ${nameNode}/user/oozie/share/lib
oozie.use.system.libpath true
oozie.wf.application.path ${nameNode}/user/oozie/apps/

Note: You can get your master node IP address (denoted as “ip-xxx-xxx-xxx-xxx” here) from the value for the sharelibDirNew parameter noted earlier.

myPysparkProgram.py

from pyspark import SparkContext, SparkConf
import numpy
import sys

conf = SparkConf().setAppName('myPysparkProgram')
sc = SparkContext(conf=conf)

rdd = sc.textFile("/user/hadoop/input.txt")

x = numpy.sum([3,4,5]) #total = 12

rdd = rdd.map(lambda line: line + str(x))
rdd.saveAsTextFile("/user/hadoop/output")

Put the “myPysparkProgram.py” into the location mentioned between the “<jar>xxxxx</jar>” tags in your workflow.xml. In this example, the location is “hdfs:///user/oozie/apps/”. Use the following command to move the “myPysparkProgram.py” file to the correct location:

hdfs dfs -put myPysparkProgram.py /user/oozie/apps/

Put the above workflow.xml file into the “/user/oozie/apps/” location in hdfs:

hdfs dfs –put workflow.xml /user/oozie/apps/

Note: The job.properties file is run locally from the EMR master node.

Create a sample input.txt file with some data in it. For example:

input.txt

This is a sentence.
So is this. 
This is also a sentence.

Put this file into hdfs:

hdfs dfs -put input.txt /user/hadoop/

Execute the job in Oozie with the following command. This creates an Oozie job ID.

oozie job -config job.properties -run

You can check the Oozie job state with the command:

oozie job -info <Oozie job ID>

  1. When the job is successfully finished, the results are located at:
/user/hadoop/output/part-00000
/user/hadoop/output/part-00001

  1. Run the following commands to view the output:
hdfs dfs -cat /user/hadoop/output/part-00000
hdfs dfs -cat /user/hadoop/output/part-00001

The output will be:

This is a sentence. 12
So is this 12
This is also a sentence 12

Summary

The myPysparkProgram.py has successfully imported the numpy library from the Anaconda platform and has produced some output with it. If you tried to run this using standard Python, you’d encounter the following error:

Now when your Python job runs in Oozie, any imported packages that are implicitly imported by your Pyspark script are imported into your job within Oozie directly from the Anaconda platform. Simple!

If you have questions or suggestions, please leave a comment below.


Additional Reading

Learn how to use Apache Oozie workflows to automate Apache Spark jobs on Amazon EMR.

 


About the Author

John Ohle is an AWS BigData Cloud Support Engineer II for the BigData team in Dublin. He works to provide advice and solutions to our customers on their Big Data projects and workflows on AWS. In his spare time, he likes to play music, learn, develop tools and write documentation to further help others – both colleagues and customers alike.

 

 

 

Roombas will Spy on You

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/07/roombas_will_sp.html

The company that sells the Roomba autonomous vacuum wants to sell the data about your home that it collects.

Some questions:

What happens if a Roomba user consents to the data collection and later sells his or her home — especially furnished — and now the buyers of the data have a map of a home that belongs to someone who didn’t consent, Mr. Gidari asked. How long is the data kept? If the house burns down, can the insurance company obtain the data and use it to identify possible causes? Can the police use it after a robbery?

EDITED TO ADD (6/29): Roomba is backtracking — for now.

[email protected] – Intelligent Processing of HTTP Requests at the Edge

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/lambdaedge-intelligent-processing-of-http-requests-at-the-edge/

Late last year I announced a preview of [email protected] and talked about how you could use it to intelligently process HTTP requests at locations that are close (latency-wise) to your customers. Developers who applied and gained access to the preview have been making good use of it, and have provided us with plenty of very helpful feedback. During the preview we added the ability to generate HTTP responses and support for CloudWatch Logs, and also updated our roadmap based on the feedback.

Now Generally Available
Today I am happy to announce that [email protected] is now generally available! You can use it to:

  • Inspect cookies and rewrite URLs to perform A/B testing.
  • Send specific objects to your users based on the User-Agent header.
  • Implement access control by looking for specific headers before passing requests to the origin.
  • Add, drop, or modify headers to direct users to different cached objects.
  • Generate new HTTP responses.
  • Cleanly support legacy URLs.
  • Modify or condense headers or URLs to improve cache utilization.
  • Make HTTP requests to other Internet resources and use the results to customize responses.

[email protected] allows you to create web-based user experiences that are rich and personal. As is rapidly becoming the norm in today’s world, you don’t need to provision or manage any servers. You simply upload your code (Lambda functions written in Node.js) and pick one of the CloudFront behaviors that you have created for the distribution, along with the desired CloudFront event:

In this case, my function (the imaginatively named EdgeFunc1) would run in response to origin requests for image/* within the indicated distribution. As you can see, you can run code in response to four different CloudFront events:

Viewer Request – This event is triggered when an event arrives from a viewer (an HTTP client, generally a web browser or a mobile app), and has access to the incoming HTTP request. As you know, each CloudFront edge location maintains a large cache of objects so that it can efficiently respond to repeated requests. This particular event is triggered regardless of whether the requested object is already cached.

Origin Request – This event is triggered when the edge location is about to make a request back to the origin, due to the fact that the requested object is not cached at the edge location. It has access to the request that will be made to the origin (often an S3 bucket or code running on an EC2 instance).

Origin Response – This event is triggered after the origin returns a response to a request. It has access to the response from the origin.

Viewer Response – This is event is triggered before the edge location returns a response to the viewer. It has access to the response.

Functions are globally replicated and requests are automatically routed to the optimal location for execution. You can write your code once and with no overt action on your part, have it be available at low latency to users all over the world.

Your code has full access to requests and responses, including headers, cookies, the HTTP method (GET, HEAD, and so forth), and the URI. Subject to a few restrictions, it can modify existing headers and insert new ones.

[email protected] in Action
Let’s create a simple function that runs in response to the Viewer Request event. I open up the Lambda Console and create a new function. I choose the Node.js 6.10 runtime and search for cloudfront blueprints:

I choose cloudfront-response-generation and configure a trigger to invoke the function:

The Lambda Console provides me with some information about the operating environment for my function:

I enter a name and a description for my function, as usual:

The blueprint includes a fully operational function. It generates a “200” HTTP response and a very simple body:

I used this as the starting point for my own code, which pulls some interesting values from the request and displays them in a table:

'use strict';
exports.handler = (event, context, callback) => {

    /* Set table row style */
    const rs = '"border-bottom:1px solid black;vertical-align:top;"';
    /* Get request */
    const request = event.Records[0].cf.request;
   
    /* Get values from request */ 
    const httpVersion = request.httpVersion;
    const clientIp    = request.clientIp;
    const method      = request.method;
    const uri         = request.uri;
    const headers     = request.headers;
    const host        = headers['host'][0].value;
    const agent       = headers['user-agent'][0].value;
    
    var sreq = JSON.stringify(event.Records[0].cf.request, null, '&nbsp;');
    sreq = sreq.replace(/\n/g, '<br/>');

    /* Generate body for response */
    const body = 
     '<html>\n'
     + '<head><title>Hello From [email protected]</title></head>\n'
     + '<body>\n'
     + '<table style="border:1px solid black;background-color:#e0e0e0;border-collapse:collapse;" cellpadding=4 cellspacing=4>\n'
     + '<tr style=' + rs + '><td>Host</td><td>'        + host     + '</td></tr>\n'
     + '<tr style=' + rs + '><td>Agent</td><td>'       + agent    + '</td></tr>\n'
     + '<tr style=' + rs + '><td>Client IP</td><td>'   + clientIp + '</td></tr>\n'
     + '<tr style=' + rs + '><td>Method</td><td>'      + method   + '</td></tr>\n'
     + '<tr style=' + rs + '><td>URI</td><td>'         + uri      + '</td></tr>\n'
     + '<tr style=' + rs + '><td>Raw Request</td><td>' + sreq     + '</td></tr>\n'
     + '</table>\n'
     + '</body>\n'
     + '</html>'

    /* Generate HTTP response */
    const response = {
        status: '200',
        statusDescription: 'HTTP OK',
        httpVersion: httpVersion,
        body: body,
        headers: {
            'vary':          [{key: 'Vary',          value: '*'}],
            'last-modified': [{key: 'Last-Modified', value:'2017-01-13'}]
        },
    };

    callback(null, response);
};

I configure my handler, and request the creation of a new IAM Role with Basic Edge Lambda permissions:

On the next page I confirm my settings (as I would do for a regular Lambda function), and click on Create function:

This creates the function, attaches the trigger to the distribution, and also initiates global replication of the function. The status of my distribution changes to In Progress for the duration of the replication (typically 5 to 8 minutes):

The status changes back to Deployed as soon as the replication completes:

Then I access the root of my distribution (https://dogy9dy9kvj6w.cloudfront.net/), the function runs, and this is what I see:

Feel free to click on the image (it is linked to the root of my distribution) to run my code!

As usual, this is a very simple example and I am sure that you can do a lot better. Here are a few ideas to get you started:

Site Management – You can take an entire dynamic website offline and replace critical pages with [email protected] functions for maintenance or during a disaster recovery operation.

High Volume Content – You can create scoreboards, weather reports, or public safety pages and make them available at the edge, both quickly and cost-effectively.

Create something cool and share it in the comments or in a blog post, and I’ll take a look.

Things to Know
Here are a couple of things to keep in mind as you start to think about how to put [email protected] to use in your application:

Timeouts – Functions that handle Origin Request and Origin Response events must complete within 3 seconds. Functions that handle Viewer Request and Viewer Response events must complete within 1 second.

Versioning – After you update your code in the Lambda Console, you must publish a new version and set up a fresh set of triggers for it, and then wait for the replication to complete. You must always refer to your code using a version number; $LATEST and aliases do not apply.

Headers – As you can see from my code, the HTTP request headers are accessible as an array. The headers fall in to four categories:

  • Accessible – Can be read, written, deleted, or modified.
  • Restricted – Must be passed on to the origin.
  • Read-only – Can be read, but not modified in any way.
  • Blacklisted – Not seen by code, and cannot be added.

Runtime Environment – The runtime environment provides each function with 128 MB of memory, but no builtin libraries or access to /tmp.

Web Service Access – Functions that handle Origin Request and Origin Response events must complete within 3 seconds can access the AWS APIs and fetch content via HTTP. These requests are always made synchronously with request to the original request or response.

Function Replication – As I mentioned earlier, your functions will be globally replicated. The replicas are visible in the “other” regions from the Lambda Console:

CloudFront – Everything that you already know about CloudFront and CloudFront behaviors is relevant to [email protected]. You can use multiple behaviors (each with up to four [email protected] functions) from each behavior, customize header & cookie forwarding, and so forth. You can also make the association between events and functions (via ARNs that include function versions) while you are editing a behavior:

Available Now
[email protected] is available now and you can start using it today. Pricing is based on the number of times that your functions are invoked and the amount of time that they run (see the [email protected] Pricing page for more info).

Jeff;