Tag Archives: Open Source

Removing the PowerShell curl alias?

PowerShell is a spiced up command line shell made by Microsoft. According to some people, it is a really useful and good shell alternative.

Already a long time ago, we got bug reports from confused users who couldn’t use curl from their PowerShell prompts and it didn’t take long until we figured out that Microsoft had added aliases for both curl and wget. The alias had the shell instead invoke its own command called “Invoke-WebRequest” whenever curl or wget was entered. Invoke-WebRequest being PowerShell’s own version of a command line tool for fiddling with URLs.

Invoke-WebRequest is of course not anywhere near similar to neither curl nor wget and it doesn’t support any of the command line options or anything. The aliases really don’t help users. No user who would want the actual curl or wget is helped by these aliases, and user who don’t know about the real curl and wget won’t use the aliases. They were and remain pointless. But they’ve remained a thorn in my side ever since. Me knowing that they are there and confusing users every now and then – not me personally, since I’m not really a Windows guy.

Fast forward to modern days: Microsoft released PowerShell as open source on github yesterday. Without much further ado, I filed a Pull-Request, asking the aliases to be removed. It is a minuscule, 4 line patch. It took way longer to git clone the repo than to make the actual patch and submit the pull request!

It took 34 minutes for them to close the pull request:

“Those aliases have existed for multiple releases, so removing them would be a breaking change.”

To be honest, I didn’t expect them to merge it easily. I figure they added those aliases for a reason back in the day and it seems unlikely that I as an outsider would just make them change that decision just like this out of the blue.

But the story didn’t end there. Obviously more Microsoft people gave the PR some attention and more comments were added. Like this:

“You bring up a great point. We added a number of aliases for Unix commands but if someone has installed those commands on WIndows, those aliases screw them up.

We need to fix this.”

So, maybe it will trigger a change anyway? The story is ongoing…

On billions and “users”

At times when I’ve gone out (yes it happens), faced an audience and talked about my primary spare time project curl, I’ve said a few times in the past that we have one billion users.

Users?

many devices

OK, as this is open source I’m talking about, I can’t actually count my users and what really constitutes “a user” anyway?

If the same human runs multiple copies of curl (in different devices and applications), is that human then counted once or many times? If a single developer writes an application that uses libcurl and that application is used by millions of humans, is that one user or are they millions of curl users?

What about pure machine “users”? In the subway in one of the world’s largest cities, there’s an automated curl transfer being done for every person passing the ticket check point. Yet I don’t think we can count the passing (and unknowing) passengers as curl users…

I’ve had a few people approach me to object to my “curl has one billion users” statement. Surely not one in every seven humans on earth are writing curl command lines! We’re engineers and we’re picky with the definitions.

Because of this, I’m trying to stop talking about “number of users”. That’s not a proper metric for a project whose primary product is a library that is used by applications or within devices. I’m instead trying to assess the number of humans that are using services, tools or devices that are powered by curl. Fun challenge, right?

Who isn’t using?

a userI’ve tried to imagine of what kind of person that would not have or use any piece of hardware or applications that include curl during a typical day. I certainly can’t properly imagine all humans in this vast globe and how they all live their lives, but I quite honestly think that most internet connected humans in the world own or use something that runs my code. Especially if we include people who use online services that use curl.

curl is used in basically all modern TVs, a large percentage of all car infotainment systems, routers, printers, set top boxes, mobile phones and apps on them, tablets, video games, audio equipment, Blu-ray players, hundreds of applications, even in fridges and more. Apple alone have said they have one billion active devices, devices that use curl! Facebook uses curl extensively and they have 1.5 billion users every month. libcurl is commonly used by PHP sites and PHP empowers no less than 82% of the sites w3techs.com has figured out what they run (out of the 10 million most visited sites in the world).

There are about 3 billion internet users worldwide. I seriously believe that most of those use something that is running curl, every day. Where Internet is less used, so is of course curl.

Every human in the connected world, use something powered by curl every day

Frigging Amazing

It is an amazing feeling when I stop and really think about it. When I pause to let it sink in properly. My efforts and code have spread to almost every little corner of the connected world. What an amazing feat and of course I didn’t think it would reach even close to this level. I still have hard time fully absorbing it! What a collaborative success story, because I could never have gotten close to this without the help from others and the community we have around the project.

But it isn’t something I think about much or that make me act very different in my every day life. I still work on the bug reports we get, respond to emails and polish off rough corners here and there as we go forward and keep releasing new curl releases every 8 weeks. Like we’ve done for years. Like I expect us and me to continue doing for the foreseeable future.

It is also a bit scary at times to think of the massive impact it could have if or when a really terrible security flaw is discovered in curl. We’ve had our fair share of security vulnerabilities so far through our history, but we’ve so far been spared from the really terrible ones.

So I’m rich, right?

coins

If I ever start to describe something like this to “ordinary people” (and trust me, I only very rarely try that), questions about money is never far away. Like how come I give it away free and the inevitable “what if everyone using curl would’ve paid you just a cent, then…“.

I’m sure I don’t need to tell you this, but I’ll do it anyway: I give away curl for free as open source and that is a primary reason why it has reached to the point where it is today. It has made people want to help out and bring the features that made it attractive and it has made companies willing to use and trust it. Hadn’t it been open source, it would’ve died off already in the 90s. Forgotten and ignored. And someone else would’ve made the open source version and instead filled the void a curlless world would produce.

No more heartbleeds please

caution-quarantine-areaAs a reaction to the whole Heartbleed thing two years ago, The Linux Foundation started its Core Infrastructure Initiative (CII for short) with the intention to help track down well used but still poorly maintained projects or at least detect which projects that might need help. Where the next Heartbleed might occur.

A bunch of companies putting in money to improve projects that need help. Sounds almost like a fairy tale to me!

Census

In order to identify which projects to help, they run their Census Project: “The Census represents CII’s current view of the open source ecosystem and which projects are at risk.

The Census automatically extracts a lot of different meta data about open source projects in order to deduce a “Risk Index” for each project. Once you’ve assembled such a great data trove for a busload of projects, you can sort them all based on that risk index number and then you basically end up with a list of projects in a priority order that you can go through and throw code at. Or however they deem the help should be offered.

Which projects will fail?

The old blog post How you know your Free or Open Source Software Project is doomed to FAIL provides such a way, but it isn’t that easy to follow programmatically. The foundation has its own 88 page white paper detailing its methods and algorithm.

Risk Index

  • A project without a web site gets a point
  • If the project has had four or more CVEs (publicly disclosed security vulnerabilities) since 2010, it receives 3 points and if fewer than four there’s a diminishing scale.
  • The number of contributors the last 12 months is a rather heavy factor, which thus could make the index grow old fairly quick. 3 contributors still give 4 points.
  • Popular packages based on Debian’s popcon get points.
  • If the project’s main language is C or C++, it gets two points.
  • Network “exposed” projects get points.
  • some additional details like dependencies and how many outstanding patches not accepted upstream that exist

All combined, this grades projects’ “risk” between 0 and 15.

Not high enough resolution

Assuming that a larger number of CVEs means anything bad is just wrong. Even the most careful and active projects can potentially have large amounts of CVEs. It means they disclose what they find and that people are actually reviewing code, finding problems and are reporting problems. All good things.

Sure, security problems are not good but the absence of CVEs in a project doesn’t say that the project is one bit more secure. It could just mean that nobody ever looked closely enough or that the project doesn’t deal with responsible disclosure of the problems.

When I look through the projects they have right now, I get the feeling the resolution (0-15) is too low and they’ve shied away from more aggressively handing out penalty based on factors we all recognize in abandoned/dead projects (some of which are decently specified in Tom Calloway’s blog post mentioned above).

The result being that the projects get a score that is mostly based on what kind of project it is.

But this said, they have several improvements to their algorithm already suggested in their issue tracker. I firmly believe this will improve over time.

The riskiest ?

The top three projects, the only ones that scores 13 right now are expat, procmail and unzip. All of them really small projects (source code wise) that have been around since a very long time.

curl, being the project I of course look out for, scores a 9: many CVEs (3), written in C (2), network exposure (2), 5+ apps depend on it (2). Seriously, based on these factors, how would you say the project is situated?

In the sorted list with a little over 400 projects, curl is rated #73 (at the time of this writing at least). Just after reportbug but before libattr1. [curl summary – which is mentioning a very old curl release]

But the list of projects mysteriously lack many projects. Like I couldn’t find neither c-ares nor libssh2. They may not be super big, but they’re used by a bunch of smaller and bigger projects at least, including curl itself.

The full list of projects, their meta-data and scores are hosted in their repository on github.

Benefits for projects near me

I can see how projects in my own backyard have gotten some good out of this effort.

I’ve received some really great bug reports and gotten handed security problems in curl by an individual who did his digging funded by this project.

I’ve seen how the foundation sponsored a test suite for c-ares since the project lacked one. Now it doesn’t anymore!

Badges!

In addition to that, the Linux Foundation has also just launched the CII Best Practices Badge Program, to allow open source projects to fill in a bunch of questions and if meeting enough requirements, they will get a “badge” to boast to the world as a “well run project” that meets current open source project best practices.

I’ve joined their mailing list and provided some of my thoughts on the current set of questions, as I consider a few of them to be, well, lets call them “less than optimal”. But then again, which project doesn’t have bugs? We can fix them!

curl is just now marked as “100% compliance” with all the best practices listed. I hope to be able to keep it like that even with future and more best practices added.

Turn many pictures into a movie

Challenge: you have 90 pictures of various sizes, taken in different formats and shapes. Using all sorts strange file names. Make a movie out of all of them, with the images using the correct aspect ratio. And add music. Use only command line tools on Linux.

Solution: this is a solution, you can most likely solve this in 22 other ways as well. And by posting it here, I can find it myself if I ever want to do the same stunt again…

#!/bin/sh

j=0
# convert options
pic="-resize 1920x1080 -background black -gravity center -extent 1920x1080"

# loop over the images
for i in `ls *jpg | sort -R`; do
 echo "Convert $i"
 convert $pic $i "pic-$j.jpg"
 j=`expr $j + 1`
done

# now generate the movie
mp3="file.mp3"
echo "make movie"
ffmpeg -framerate 3 -i pic-%d.jpg -i $mp3 -acodec copy -c:v libx264 -r 30 -pix_fmt yuv420p -s 1920x1080 -shortest out.mp4

Explained

This is a shell script.

The ‘pic’ variable holds command line options for the ImageMagick ‘convert‘ tool. It resizes each picture to 1920×1080 while maintaining aspect ratio and if the pic gets smaller, it is centered and gets a black border.

The loop goes through all files matching *,jpg, randomizes the order with ‘sort’ and then runs ‘convert’ on them one by one and calls the output files pic-[number].jpg where number is increased by one for each image.

Once all images have the correct and same size, ‘ffmpeg‘ is invoked. It is told to produce a movie with 3 photos per second, how to find all the images, to include an mp3 file into the output and to stop encoding when one of the streams ends – this assumes the playing time of the mp3 file is longer than the total time the images are shown so the movie stops when we run out of images to show.

Result

The ‘out.mp4’ file, uploaded to youtube could then look like this:

(music by Bensound.com)

Hosting RMS again!

I’m thrilled to once again have to honor to organize a lecture and talk in Stockholm by the legendary RMS himself. (Remember the last time?)

On January 25 2016, RMS will talk about “For a Free Digital Society” in the large Aula Magna room at Stockholm University that seats almost 1200 persons.

See http://www.foss-sthlm.se/rms2016.html for the full invitation and sign-up. Registration is voluntary, but it helps us understand the interest and size of the audience.

Aula MagnaPhoto by Kjell Ericson, taken just before the event started the last time.

Bug finding is slow in spite of many eyeballs

“given enough eyeballs, all bugs are shallow”

The saying (also known as Linus’ law) doesn’t say that the bugs are found fast and neither does it say who finds them. My version of the law would be much more cynical, something like: “eventually, bugs are found“, emphasizing the ‘eventually’ part.

(Jim Zemlin apparently said the other day that it can work the Linus way, if we just fund the eyeballs to watch. I don’t think that’s the way the saying originally intended.)

Because in reality, many many bugs are never really found by all those given “eyeballs” in the first place. They are found when someone trips over a problem and is annoyed enough to go searching for the culprit, the reason for the malfunction. Even if the code is open and has been around for years it doesn’t necessarily mean that any of all the people who casually read the code or single-stepped over it will actually ever discover the flaws in the logic. The last few years several world-shaking bugs turned out to have existed for decades until discovered. In code that had been read by lots of people – over and over.

So sure, in the end the bugs were found and fixed. I would argue though that it wasn’t because the projects or problems were given enough eyeballs. Some of those problems were found in extremely popular and widely used projects. They were found because eventually someone accidentally ran into a problem and started digging for the reason.

Time until discovery in the curl project

I decided to see how it looks in the curl project. A project near and dear to me. To take it up a notch, we’ll look only at security flaws. Not only because they are the probably most important bugs we’ve had but also because those are the ones we have the most carefully noted meta-data for. Like when they were reported, when they were introduced and when they were fixed.

We have no less than 30 logged vulnerabilities for curl and libcurl so far through-out our history, spread out over the past 16 years. I’ve spent some time going through them to see if there’s a pattern or something that sticks out that we should put some extra attention to in order to improve our processes and code. While doing this I gathered some random info about what we’ve found so far.

On average, each security problem had been present in the code for 2100 days when fixed – that’s more than five and a half years. On average! That means they survived about 30 releases each. If bugs truly are shallow, it is still certainly not a fast processes.

Perhaps you think these 30 bugs are really tricky, deeply hidden and complicated logic monsters that would explain the time they took to get found? Nope, I would say that every single one of them are pretty obvious once you spot them and none of them take a very long time for a reviewer to understand.

Vulnerability ages

This first graph (click it for the large version) shows the period each problem remained in the code for the 30 different problems, in number of days. The leftmost bar is the most recent flaw and the bar on the right the oldest vulnerability. The red line shows the trend and the green is the average.

The trend is clearly that the bugs are around longer before they are found, but since the project is also growing older all the time it sort of comes naturally and isn’t necessarily a sign of us getting worse at finding them. The average age of flaws is aging slower than the project itself.

Reports per year

How have the reports been distributed over the years? We have a  fairly linear increase in number of lines of code but yet the reports were submitted like this (now it goes from oldest to the left and most recent on the right – click for the large version):

vuln-trend

Compare that to this chart below over lines of code added in the project (chart from openhub and shows blanks in green, comments in grey and code in blue, click it for the large version):

curl source code growth

We received twice as many security reports in 2014 as in 2013 and we got half of all our reports during the last two years. Clearly we have gotten more eyes on the code or perhaps users pay more attention to problems or are generally more likely to see the security angle of problems? It is hard to say but clearly the frequency of security reports has increased a lot lately. (Note that I here count the report year, not the year we announced the particular problems, as they sometimes were done on the following year if the report happened late in the year.)

On average, we publish information about a found flaw 19 days after it was reported to us. We seem to have became slightly worse at this over time, the last two years the average has been 25 days.

Did people find the problems by reading code?

In general, no. Sure people read code but the typical pattern seems to be that people run into some sort of problem first, then dive in to investigate the root of it and then eventually they spot or learn about the security problem.

(This conclusion is based on my understanding from how people have reported the problems, I have not explicitly asked them about these details.)

Common patterns among the problems?

I went over the bugs and marked them with a bunch of descriptive keywords for each flaw, and then I wrote up a script to see how the frequent the keywords are used. This turned out to describe the flaws more than how they ended up in the code. Out of the 30 flaws, the 10 most used keywords ended up like this, showing number of flaws and the keyword:

9 TLS
9 HTTP
8 cert-check
8 buffer-overflow

6 info-leak
3 URL-parsing
3 openssl
3 NTLM
3 http-headers
3 cookie

I don’t think it is surprising that TLS, HTTP or certificate checking are common areas of security problems. TLS and certs are complicated, HTTP is huge and not easy to get right. curl is mostly C so buffer overflows is a mistake that sneaks in, and I don’t think 27% of the problems tells us that this is a problem we need to handle better. Also, only 2 of the last 15 flaws (13%) were buffer overflows.

The discussion following this blog post is on hacker news.

Keyboard key frequency

A while ago I wrote about my hunt for a new keyboard, and in my follow-up conversations with friends around that subject I quickly came to the conclusion I should get myself better analysis and data on how I actually use a keyboard and the individual keys on it. And if you know me, you know I like (useless) statistics.

Func KB-460 keyboardSo, I tried out the popular and widely used Linux key-logger software ‘logkeys‘ and immediately figured out that it doesn’t really support the precision and detail level I wanted so I forked the project and modified the code to work the way I want it: keyfreq was born. Code on github. (I forked it because I couldn’t find any way to send back my modifications to the upstream project, I don’t really feel a need for another project.)

Then I fired up the logging process and it has been running in the background for a while now, logging every key stroke with a time stamp.

Counting key frequency and how it gets distributed very quickly turns into basically seeing when I’m active in front of the computer and it also gave me thoughts around what a high key frequency actually means in terms of activity and productivity. Does a really high key frequency really mean that I was working intensely or isn’t that purpose more a sign of mail sending time? When I debug problems or research details, won’t those periods result in slower key activity?

In the end I guess that over time, the key frequency chart basically says that if I have pressed a lot of keys during a period, I was working on something then. Hours or days with a very low average key frequency are probably times when I don’t work as much.

The weekend key frequency is bound to be slightly wrong due to me sometimes doing weekend hacking on other computers where I don’t log the keys since my results are recorded from a single specific keyboard only.

Conclusions

So what did I learn? Here are some conclusions and results from 1276614 keystrokes done over a period of the most recent 52 calendar days.

I have a 105-key keyboard, but during this period I only pressed 90 unique keys. Out of the 90 keys I pressed, 3 were pressed more than 5% of the time – each. In fact, those 3 keys are more than 20% of all keystrokes. Those keys are: <Space>, <Backspace> and the letter ‘e’.

<Space> stands out from all the rest as it has been used more than 10%.

Only 29 keys were used more than 1% of the presses, giving this a really long tail with lots of keys hardly ever used.

Over this logged time, I have registered key strokes during 46% of all hours. Counting only the hours in which I actually used the keyboard, the average number of key strokes were 2185/hour, 36 keys/minute.

The average week day (excluding weekend days), I registered 32486 key presses. The most active sinngle minute during this logging period, I hit 405 keys. The most active single hour I managed to do 7937 key presses. During weekends my activity is much lower, and then I average at 5778 keys/day (7.2% of all activity were weekends).

When counting most active hours over the day, there are 14 hours that have more than 1% activity and there are 5 with less than 1%, leaving 5 hours with no keyboard activity at all (02:00- 06:59). Interestingly, the hour between 23-24 at night is the single most busy hour for me, with 12.5% of all keypresses during the period.

Random “anecdotes”

Longest contiguous time without keys: 26.4 hours

Longest key sequence without backspace: 946

There are 7 keys I only pressed once during this period; 4 of them are on the numerical keypad and the other three are F10, F3 and <Pause>.

More

I’ll try to keep the logging going and see if things change over time or if there later might end up things that can be seen in the data when looked over a longer period.

FOSS them students

On October 16th, I visited DSV at Stockholm University where I had the pleasure of holding a talk and discussion with students (and a few teachers) under the topic Contribute to Open Source. Around 30 persons attended.

Here are the slides I use, as usual possibly not perfectly telling stand-alone without the talk but there was no recording made and I talked in Swedish anyway…