25 Things I’ve learned in Software Development

1. Developers don’t think they need marketing people. Until they try to market their own products.
2. Developers don’t think they need business people. Until they spend a years writing software no one wants and is eventually abandoned.
3. Developers tend to make user interfaces a reflection of the underlying data structures. The more intuitive user interfaces often involve duplicate sections, intertwined complexity, and the base assumption that the user has no idea what they’re doing (eg, the anti-developer).
4. The more users you have, the more risk you have when deploying new code.
5. The more risk you have, the higher quality you tend to make code.
6. High quality code (design documents, unit tests, integration tests, code reviews…) isn’t usually as fun as high speed hacked together prototypes.
7. At software companies, you’ll be treated like rockstars. At other engineering companies, you may be stuffed in a dark corner with the IT people, second class citizens to the electrical engineers, finance people, or whoever else is a part of the main company’s mission.
8. The popular misquote of Linus’s law, “with many eyeballs, all bugs are shallow,” is bullshit. Reading other people’s code is hard. Understanding other people’s code is even harder. Don’t expect code reviews to catch all the bugs.
9. With many users, all bugs are shallow. If you own enterprise software with 5 clients, it’s probably going to be full of bugs. If you own software with public facing APIs and 2 million people calling them, you’ll find bugs rather quickly when you push to production.
10. Aggressive, disagree and commit style arguments, are stressful. Passive aggressive, disagree and give the silent treatment arguments, are time wasting.
11. Become a master of your chosen source control tools. Your everyday coding work will be easier and your coworkers will thank you when you can rattle off in 4 commands how to move their commits from one branch to another or fix their broken merges.
12. Writing tests is the one time in your life when you’ll be happy you find a bug. Nothing’s worse than spending an hour writing tests only to find your code works just like you expected it to.
13. Keep notes a text file, wiki, zim, etc. It doesn’t have to be pretty, but 3 months after you run an obscure SQL query you’d only need to do once, it’s really useful to be able to search through you notes file and find it again. Organization isn’t important, Ctrl + F will get you where you need to go.
14. You will forget the details of basically everything you work on within a few months. I saw a code review where someone was having trouble getting Joda DateTimes to work with the Jackson JSON serializer. I immediately knew that I had encountered the exact same problem about a month ago, but had absolutely no idea where that code was. Luckily our code review system is easily searchable so I could find my old commit.
15. You will realize that forgetting is irrelevant as long as you can quickly navigate your way back back to what you once knew. Knowing what’s at the end of the road and how to get there is more important than being everywhere at once.
16. Bugs really do become features. Telling people years later, “that’s a bug, not a feature,” doesn’t go over well when you tell them how you “fixed” it.
17. Deprecating internally used software interfaces is hard.
18. Deprecating publicly used  software interfaces is next to impossible.
19. Algorithms are important for the majority job interviews.
20. Algorithms aren’t very important for the majority of programming jobs.
21. Time goes by faster when you have no idea what time it is. Disable your system clocks.
22. Meetings are easily missed when you have no idea what time it is. Use calendar notifications.
23. It’s too easy to get tunnel vision. Walk away from your computer with a sheet of paper and brainstorm every now and then.
24. You can identify who wrote code from the constants they used in their unit tests. I’m personally a fan of 108. Coworkers favor 1984, 42, 69, and 8675309.
25. Beware the 2 minutes of time it takes to build your project. It can easily turn into 20 minutes reading blogs.

Thoughts on Open Source Software Development

The last year I did a lot of work with some small Open Source projects (Nova, Honeyd, Neighbor-Cache Fingerprinter, Node.js Github Issue Bot…). I’ve also used Linux for all of my development and have used a lot of Open Source projects in that time. In some ways I’ve come out being more of on Open Source advocate than ever, and in other ways I’ve come out a bit jaded. What does Open Sourcing a project get you?

Good thing: free feedback on features and project direction

Unless you’re Steve Jobs, you probably don’t know what customers want. If you’re an engineer like most people reading this blog, you really probably don’t know what customers want. Open Sourcing the project can provide free user feedback. If you’re writing a business application, people will tell you they want pretty graphs generated for data that you never thought would be important. If you’re writing something with dependencies, users will tell you they want you to support multiple versions of potentially incompatible libraries that you would never have bothered with on your own.

If you’ve got an IRC channel, you’ll occasionally find a person who’s more than willing to chat about his or her opinions on the project and what features they think would be useful, in addition to the occasional issue tickets and emails.

The Open Source community can be your customers when you don’t have any real customers yet.

Good thing: free testing

Everyone who downloads and uses the project becomes someone that can help with the testing effort. All software has bugs, and if they’re annoying enough, people will report them. I’ve tried to make small contributions to bigger Open Source projects by reporting issues I’ve found in things like Node.js, Express, Backtrack, Gimp, cvv8… As a result, code becomes better tested and more stable.

Good thing: free marketing

Open Sourcing the project, at least in theory, means people will use it. They’ll talk about it to their friends, they’ll write articles and reviews about it, and if the project is actually useful it’ll start gaining popularity.

Misconception: you’ll get herds of developers willing to work on your project for free

I’ve reported dozens of bugs in big Open Source projects. I’ve modified the source code of Nmap and Apache for various data collection reasons. I’ve never submitted a patch bigger than about 3 lines of code to someone else’s Open Source project. That’s depressing to admit, but it’s the norm. People will file bug tickets, sometimes offer suggestions on features, but don’t expect a herd of developers working for free and flocking to your project to make it better. Even the most hardcore Open Source advocates have their own pet projects they would rather work on than fixing bugs or writing features into yours. Not only that, the effort to fix a bug in a foreign code base is significantly higher than the effort required for the original developer of the code to fix it. Why spend 3 hours setting up the development environment and trying to fix a bug, when you can file a ticket and the guy that wrote the code can probably fix it in 3 minutes?

There are large Open Source projects (Linux, Open Office, Apache…) that have a bunch of dedicated developers. They’re the exceptions. From what I’ve seen, most Open Source projects are run by one person or a small core group.

Misconception: the community will take over maintaining projects even if the core developer leaves

We used a Node.js library called Nowjs quite a lot. It’s a wonderful package that takes away all the tedium of manual AJAX or socket.io work and makes Javascript RPC amazingly easy. It has over 2,000 followers on Github, and probably ten times that many people using it. One day the developer decided to abandon the project to work on other things; not that unusual for a pet project. Sadly, that was the death of the project. Github makes it trivial to clone the project, with a single press of a button someone could make a copy of the repository and take over maintaining and extending it. Dozens of people initially made forks of the project in order to do that, and dozens more made forks to fix bugs they found.

What’s left? A mess consisting of dozens of Github forks of the project, all with different bugs being fixed or features added, and the “official” project left abandoned in such a way no one can figure out which fork they should use. There’s no one left to merge in patches or to make project direction decisions. New users can’t figure out which fork to use and old users that actually write patches don’t know where to submit them anymore.

The developer of Nowjs moved on to develop Bridge-js. Then Bridge-js got abandoned too.

Bridge is still open source but the engineers behind Bridge have returned to school.

This pattern is almost an epidemic in Node.js. Someone creates a really amazing module, publishes it to Github and NPM, and then abandons it. Dozens of people try to take over the development, but in the end all fail (partly because Github’s lack of specifying which fork of the project is “official”, and the Open Source problem that there is no “official” fork). A dozen new people create the same basic module from scratch, most of which never become popular, and most of which also become abandoned… You see the picture.

If you sense a hint of frustration, you’d be right. On multiple occasions I had to dig through dozens of half abandoned projects trying to figure out which library I wanted to use to do something as common as SQL in Node.js.

The reason it’s an epidemic with Node is because no one is really sure what they want yet, and projects haven’t become popular enough to have momentum to continue after abandonment by their original authors. Hopefully at some point projects will acquire a big enough user base and enough developers that they can sustain themselves.

Fork is a four letter word

Even the biggest projects aren’t immune to the anarchy of forks. Libreoffice and Openoffice, GNU Emacs vs XEmacs, the list goes on. For the end user of these software suits, this is mainly annoying. I’ve switched between LibreOffice and OpenOffice more than once now, because I keep finding bugs in one but not the other.

Sometimes forks break out for ridiculous reasons. The popular IM client Pidgin was forked into the Carrier project. Why?

As of version 2.4 and later, the ability to manually resize the text input box of conversations has been altered—Pidgin now automatically resizes between a number of lines set in ‘Preferences’ and 50% of the window depending on how much is typed. Some users find this an annoyance rather than a feature and find this solution unacceptable. The inability to manually resize the input area eventually led to a fork, Carrier (originally Funpidgin). – https://en.wikipedia.org/wiki/Pidgin_(software)

You can view the 300+ post argument about the issue on the original Pidgin ticket here.

The fact there’s no single “official” version of a project and the sometimes trivial reasons that forks break out cause a lot of inefficiency as bug are fixed in some forks but not others, and eventually code bases diverge so much that they also develop in one fork or another.

Misconception: people outside of software development understand Open Source

I once heard someone ask in confusion how Open Source software can possibly be secure, because can’t anyone upload backdoors into it? They thought Open Source projects were like Wikipedia, where anyone could edit the code and their changes would be somehow instantly applied without review. After all, people keep telling them, “Open Source is great, anyone can modify the code!”.

A half dozen times, ranging from family members to customers and business people, I’ve had to try and explain how Open Source security products can work even though the code is available. If people can see the code, they can figure out how to break and bypass it, right? Ugh…

And don’t even get me started on the people that will start comparing Open Source to communism.

Concluding thoughts

I believe Open Source software has plenty of advantages, but I also think there’s a lot of hype surrounding it. The vast majority of Open Source projects are hacked together as hobby projects and abandoned shortly after. A study of Sourceforge projects showed that less than 17% of projects actually become successful; the other 83% are abandoned in the early stages. Most projects only have a few core developers and the project won’t outlive their interest in it. The community may submit occasional patches, but are unlikely to do serious feature development.

Why release Open Source software then? I think the answer often becomes, “why not?”. Plenty of developers write code in their spare time. They don’t plan to make money directly from it: selling software is hard. They do it to sharpen their saws. They do it for fun, self improvement, learning, future career opportunities, and to release something into the world that just might be useful and make people’s lives better. If you’re writing code with no intention of making money off it, there’s really no reason not to release it as Open Source.

What if you do want to make money off it? Well, why not dual licence your code and have a free Open Source trial version along with an Enterprise version? You’ll get the advantages of free marketing, testing, and user feedback. There is the risk that someone will come along and extend the Open Source trial version into a program that has all of the same features, or even more features, as your Enterprise version, and this is something that needs to be considered. However, as I mentioned before, it’s hard to find people that will take over the development and maintenance of Open Source projects. I think it’s more likely that someone will steal your idea and create their own implementation than bother with trying to extend your trial version code, but I don’t have any proof or evidence of that.

Jade Mixins (blocks, attributes, and more)

Thought I’d note this down for anyone else having problems with Jade mixins. It’s fairly undocumented at the moment and if you follow the documentation on the Jade github it will actually break with obscure errors which took lot of trial and error to figure it out.

Note: I’m using Jade 0.32. You’ll probably need that version or newer.

What is a mixin?

A mixin is simple method to allow reuse of HTML snippets inside of Jade templates. Lets go ahead and explain with an example. Suppose you have a page of quotes. Each quote is in it’s own section, with the author’s name in bold, and a like button that keeps track of the most liked quotes with some Javascript.

The basic syntax to define a mixin that takes in a couple of arguments is as follows,

mixin section(quote, author)
  div.section
    div.quoteText
      p #{quote} – said by
        b #{author}

  a.likeButton(onclick=”quoteLiked()”)
    img.buttonIconLeft(src=”images/like.png”)
    span.buttonSpan Like this quote

Then to actually use the mixin, we use the (somewhat undocumented) “+” symbol as follows,

+section(“Imagination is more important than knowledge”, “Albert Einstein”)
+section(“Writing, to me, is simply thinking through my fingers.”, “Isaac Asimov”)

This will generate the HTML,

<div class=”contentBox”>
  <div class=”quoteText”>
    <p>Imagination is more important than knowledge. – <b>Albert Einstein</b></p>
  </div>

  <a onclick=”quoteLiked()” class=”likeButton”>
    <img src=”images/like.png” class=”buttonIconLeft”/>
    <span class=”buttonSpan”>Like this quote</span>
  </a>
</div>

…

Mixin arguments can be objects too

You don’t have to just pass in strings to the mixins, but you can use any Javascript objects you passed into the render call or that you created earlier in the template. This can lead to some useful mixins like this one to convert a Javascript array to a select dropdown list.

mixin listData(selectId, options)

  select(id=”#{selectId}”)

    each obj in options>

      option(value=”#{obj}”) #{obj}

 

– var countries= [‘UK’, ‘USA’, ‘CANADA’, ‘MEXICO’]

+listData(“countrySelect”, countries)

What are block mixins?

The need for block mixins came up when I had my page divided into sections, with each section having a title and a few containing divs, as well as a help icon.

mixin headerWithHelp(title, helpAnchor)
  div.cardContainer
    h3.cardHeader
      span #{title}
      a(href=”help##{helpAnchor}”, target=”_blank”)
        img.helpIcon(src=”/images/help.png”, style=”float: right;”)
    div.cardOuter(style=”display: inline-block”)
          block

The important thing to note here is the “block” keyword at the end of the mixin definition. This will make it so the indent block after the mixin will included in that location, so you can do things like,

+headerWithHelp(“Test Section”, “testHelp”)
   p All of my content can go here now

It’s important to note that as of right now, block mixins DO NOT WORK if you use the mixin keyword to use the mixin instead of the “+” symbol shorthand (which is all I showed you in this tutorial). I believe this is a bug, you can track the status of it on the ticket I made here. Trying to use the mixin keyword to use the mixin instead of + when using a block after it will result in “Error at new JS_Parse_Error” and a stack trace.

What are mixin attributes?

Mixins have the ability to let you modify the attributes of one of the tags inside of it when you modify the attributes of the mixin. For example, suppose that you have a mixin to define a section of the page with a header that you use a lot, but you change some of the style attributes like the width and display type a lot.

mixin header(title)
  div.container(attributes)
    h3.sectionHeader #{title}
    div.content(style=”display: block”)
      block

Notice the attributes keyword? Now you can use the mixin like so,

+header(“Section Title”)(style=’text-align:center; display: block; width: 500px;’)

And the style attribute will now be applied to the container div.

Conclusion

A final comment is that you may want to have a mixins folder inside views for the sake of organization. Then in your other jade files, you can just include the mixins you need.

include mixins/headers.jade

include mixins/quotes.jade

Neighbor Cache Fingerprinter: Operating System Version Detection with ARP

I’ve released the first prototype (written in C++) of an Open Source tool called the Neighbor Cache Fingerprinter on Github today. A few months ago, I was watching the output of a lightweight honeypot in a Wireshark capture and noticed that although it had the capability to fool nmap’s operating system scanner into thinking it was a certain operating system, there were subtle differences in the ARP behavior that could be detected. This gave me the idea to explore the possibility of doing OS version detection with nothing except ARP. The holidays provided a perfect time to destroy my sleep schedule and get some work done on this little side project (see commit punchcard, note best work done Sunday at 2:00am).

The tool is currently capable of uniquely identifying the following operating systems,

Windows 7

Windows XP (fingerprint from Service Pack 3)

Linux 3.x (fingerprint from Ubuntu 12.10)

Linux 2.6 (fingerprint from Century Link Q1000 DSL Router)

Linux 2.6 (newer than 2.6.24) (fingerprint from Ubuntu 8.04)

Linux 2.6 (older than 2.6.24) (fingerprint from Knoppix 5)

Linux 2.4 (fingerprint from Damn Small Linux 4.4.10)

FreeBSD 9.0-RELEASE

Android 4.0.4

Android 3.2

Minix 3.2

ReactOS 0.3.13

More operating systems should follow as I get around to spinning up more installs on Virtual Machines and adding to the fingerprints file. Although it’s still a fairly early prototype, I believe it’s already a useful enough tool that it can be beneficial, so install it and let me know via the Github issues page if you find any bugs. There’s very little existing research on this; arp-fingerprint (a perl script that uses arp-scan) is the only thing remotely close, and it attempts to identify the OS only by looking at responses to ARP REQUEST packets. The Neighbor Cache Fingerprinter focuses on sending different types of ARP REPLY packets as well as analyzing several other behavioral quirks of ARP discussed in the notes below.

The main advantage of the Neighbor Cache Fingerprinter versus an Nmap OS scan is that the tool can do OS version detection on a machine that has all closed ports. The next big feature I’m working on is expanding the probe types to allow it to work on machines that respond to ICMP pings, OR have open TCP ports, OR have closed TCP ports, OR have closed UDP ports. The tool just needs the ability to elicit a reply from the target being scanned, and a pong, TCP/RST, TCP/ACK, or ICMP unreachable message will all provide that.

The following are my notes taken from the README file,

Introduction

What is the Neighbor Cache? The Neighbor Cache is an operating system’s mapping of network addresses to link layer addresses maintained and updated via the protocol ARP (Address Resolution Protocol) in IPv4 or NDP (Neighbor Discovery Protocol) in IPv6. The neighbor cache can be as simple as a lookup table updated every time an ARP or NDP reply is seen, to something as complex as a cache that has multiple timeout values for each entry, which are updated based on positive feedback from higher level protocols and usage characteristics of that entry by the operating system’s applications, along with restrictions on malformed or unsolicited update packets.

This tool provides a mechanism for remote operating system detection by extrapolating characteristics of the target system’s underlying Neighbor Cache and general ARP behavior. Given the non-existence of any standard specification for how the Neighbor Cache should behave, there several differences in operating system network stack implementations that can be used for unique identification.

Traditional operating system fingerprinting tools such as Nmap and Xprobe2 rely on creating fingerprints from higher level protocols such as TCP, UDP, and ICMP. The downside of these tools is that they usually require open TCP ports and responses to ICMP probes. This tool works by sending a TCP SYN packet to a port which can be either open or closed. The target machine will either respond with a SYN/ACK packet or a SYN/RST packet, but either way it must first discover the MAC address to send the reply to via queries to the ARP Neighbor Cache. This allows for fingerprinting on target machines that have nothing but closed TCP ports and give no ICMP responses.

The main disadvantage of this tool versus traditional fingerprinting is that because it’s based on a Layer 2 protocol instead of a Layer 3 protocol, the target machine that is being tested must reside on the same Ethernet broadcast domain (usually the same physical network). It also has the disadvantage of being fairly slow compared to other OS scanners (a scan can take ~5 minutes).

Fingerprint Technique: Number of ARP Requests

When an operating system performs an ARP query it will often resend the request multiple times in case the request or the reply was lost. A simple count of the number of requests that are sent can provide a fingerprint feature. In addition, there can be differences in the number of responses to open and closed ports due to multiple retries on the higher level protocols, and attempting to send a probe multiple times can result in different numbers of ARP requests (Android will initially send 2 ARP requests, but the second time it will only send 1).

For example,

Windows XP: Sends 1 request

Windows 7: Sends 3 if probe to closed port (9 if probe to open port)

Linux: Sends 3 requests

Android 3: Sends 2 requests the first probe, then 1 request after
A minimum and maximum number of requests seen is recorded in the fingerprint.

Fingerprint Technique: Timing of ARP Request Retries

On hosts that retry ARP requests, the timing values can be used to deduce more information. Linux hosts generally have a constant retry time of 1 second, while Windows hosts generally back off on the timing, sending their first retry after between 500ms and 1s, and their second retry after 1 second.

The fingerprint contains the minimum time difference between requests seen, maximum time difference, and a boolean value indicating if the time differences are constant or changing.

Fingerprint Technique: Time before cache entry expires

After a proper request/reply ARP exchange, the Neighbor Cache gets an entry put in it for the IP address and for a certain amount of time communication will continue without additional ARP requests. At some point, the operating system will decide the entry in the cache is stale and make an attempt to update it by sending a new ARP request.

To test this a SYN packet is sent, an ARP exchange happens, and then SYN packets are sent once per second until another ARP request is seen.

Operating system response examples,

Windows XP : Timeout after 10 minutes (if referred to)

Windows 7/Vista/Server 2008 : Timeout between 15 seconds and 45 seconds

Freebsd : Timeout after 20 minutes

Linux : Timeout usually around 30 seconds
More research needs to be done on the best way to capture the values of delay_first_probe_time and differences between stale timing and actually falling out of the table and being gc’ed in Linux.

Waiting 20 minutes to finish the OS scan is unfeasible in most cases, so the fingerprinting mode only waits about 60 seconds. This may be changed later to make it easier to detect an oddity in older windows targets where cache entries expire faster if they aren’t used (TODO).

Fingerprint Technique: Response to Gratuitous ARP Replies

A gratuitous or unsolicited ARP reply is an ARP reply for which there was no request. The usual use case for this is notification of machines on the network of IP changes or systems coming online. The problem for implementers is that several of the fields in the ARP packet no longer make much sense.

Who is the Target Protocol Address for the ARP packet? The broadcast address? Zero? The specification surprisingly says neither: the target Protocol address should be the same IP address as the Sender Protocol Address.

When there’s no specific target for the ARP packet, the Target Hardware Address also becomes a confusing field. The specification says it’s value shouldn’t matter, but should be set to zero. However, most implementations will use the Ethernet broadcast address of FF:FF:FF:FF:FF instead, because internally they have some function to send an ARP reply that only takes one argument for the destination MAC address (and is put in both the Ethernet frame destination and the ARP packet’s Target Hardware Address). We can also experiment with setting the Target Hardware Address to the same thing as the Sender Hardware Address (the same method the spec says to use for the Target Protocol field).

Even the ARP opcode becomes confusing in the case of unsolicited ARP packets. Is it a “request” for other machines to update their cache? Or is it a “reply”, even though it isn’t a reply to anything? Most operating systems will update their cache no matter the opcode.

There are several variations of the gratuitous ARP packet that can be generated by changing the following fields,

Ethernet Frame Destination Address : Bcast or the MAC of our target

ARP Target Hardware Address : 0, bcast, or the MAC of our target

ARP Target Protocol Address : 0 or the IP address of our target

ARP Opcode : REPLY or REQUEST
This results in 36 different gratuitous packet permutations.

Most operating systems have the interesting behavior that they will ignore gratuitous ARP packets if the sender is not in the Neighbor Cache already, but if the sender is in the Neighbor Cache, they will update the MAC address, and in some operating systems also update the timeouts.
The following sequence shows the testing technique for this feature,

Send ARP packet that is known to update most caches with srcmac = srcMacArg Send gratuitous ARP packet that is currently being tested with srcmac = srcMacArg + 1 Send probe packet with a source MAC address of srcMacArg in the Ethernet frame

The first packet attempts to get the cache entry into a known state: up to date and storing the source MAC address that is our default or the command line argument –srcmac. The following ARP packet is the actual probe permutation that’s being tested.

If the reply to the probe packet is to (srcMacArg + 1), then we know the gratuitous packet successfully updated the cache entry. If the reply to the probe is just (srcMacArg), then we know the cache was not updated and still contains the old value.

The reason the Ethernet frame source MAC address in the probe is set to the original srcMacArg is to ensure the target isn’t just replying to the MAC address it sees packets from and is really pulling the entry out of ARP.

Sometimes the Neighbor Cache entry will get into a state that makes it ignore gratuitous packets even though, given a normal state, it would accept them and update the entry. This can result in some timing related result changes. For now I haven’t made an attempt to fix this as it’s actually useful as a fingerprinting method in itself.

Fingerprint Technique: Can we get put into the cache with a gratuitous packet?

As mentioned in the last section, most operating systems won’t add a new entry to the cache given a gratuitous ARP packet, but they will update existing entries. One of the few differences between Windows XP and FreeBSD’s fingerprint is that we can place an entry in the cache by sending a certain gratuitous packet to a FreeBSD machine, and test if it was in the cache by seeing if a probe gets a response or not.

Fingerprint Technique: ARP Flood Prevention (Ignored rapid ARP replies)

RFC1122 (Requirements for Internet Hosts) states,

“A mechanism to prevent ARP flooding (repeatedly sending an ARP Request for the same IP address, at a high rate) MUST be included. The recommended maximum rate is 1 per second per destination.”

Linux will not only ignore duplicate REQUEST packets within a certain time, but also duplicate REPLY packets. We can test this by sending a set of unsolicited ARP replies within a short time range with difference MAC addresses being reported by each reply. Sending a probe will reveal in the probe response destination MAC address if the host responds to the first MAC address we ARPed or the last, indicating if it ignored the later rapid replies.

Fingerprint Technique: Correct Reply to RFC5227 ARP Probe

This test sends an “ARP Probe” as defined by RFC 5227 (IPv4 Address Conflict Detection) and checks the response to see if it confirms to the specification. The point of the ARP Probe is to check if an IP address is being used without the risk of accidentally causing someone’s ARP cache to update with your own MAC address when it sees your query. Given that you’re likely trying to tell if an IP address is being used because you want to claim it, you likely don’t have an IP address of your own yet, so the Sender Protocol Address field is set to 0 in the ARP REQUEST.

The RFC specifies the response as,

“(the probed host) MAY elect to attempt to defend its address by … broadcasting one single ARP Announcement, giving its own IP and hardware addresses as the sender addresses of the ARP, with the ‘target IP address’ set to its own IP address, and the ‘target hardware address’ set to all zeroes.”

But any Linux kernel older than 2.6.24 and some other operating systems will respond incorrectly, with a packet that has tpa == spa and tha == sha. Checking if tpa == 0 has proven sufficient for a boolean fingerprint feature.

TODO RESEARCH IN PROGRESS Fingerprint Technique

Feedback from higher protocols extending timeout values

Linux has the ability to extend timeout values if there’s positive feedback from higher level protocols, such as a 3 way TCP handshake. Need to write tests for this and do some source diving in the kernel to see what else counts besides a 3 way handshake for positive feedback.

TODO RESEARCH IN PROGRESS Fingerprint Technique

Infer Neighbor Cache size by flooding to cause entry dumping

Can we fill the ARP table with garbage entries in order for it to start dumping old ones? Can we reliably use this to infer the table size, even with Linux’s near random cache garbage collection rules? Can we do this on class A networks, or do we really need class B network subnets in order to make this a viable test?