We had published a network protocol analysis challenge for free entry to our BlackHat 2012 Vegas training courses and received seven correct answers. We'd like to thank those who attempted this challenge and hope that they find it useful.

The winner, Peter Af Geijerstam managed to respond first, with the correct answer. As a result, he wins a free place on any of our Hacking By Numbers courses. Here is a brief solution for it:

If you start by running the client and server binaries provided in the challenge zip file, you'll observe the following output from the client:

And we can see the same challenge (177) and 16-byte response values in the network traffic:

So, this indicates that the client and server are running a simple challenge-response authentication protocol with a 3-digit random number (R) as the challenge and a 16-byte response value which should be calculated using the R and the shared secret value. We need to figure out the response calculation formula in order to fully understand this simple authentication protocol. This can be done by reading the provided source code. If the source code was not available then we'd have to use a disassembler, such as IDA Pro, in order to find out the formula. The following code snippet shows a function call to HMAC(char *msg, char *key,byte *mac) to calculate the response value:

And inside HMAC function we can see calls to Windows CryptoAPI to calculate MD5 hash value of (msg+secret):

Now, we can summarise the authentication protocol as below and work out our attack strategy:

Client->Server : HELLO Server->Client: R Client->Server: RESP (MD5(R+secret)) Server->Client: OK/Incorrect Response

The attacker had both R and MD5(R+secret) values from the network traffic capture file and he also knew something about the shared secret format (7 alphanumeric excluding uppercase characters). Therefore, he can run a brute force attack on the 16-byte MD5 hash value with a narrowed charset and known message format which would be [448][abcdefghijklmnopqrstuvwxyz0123456789]. There are several public hash cracking tools which support raw md5 hashes, such as hashcat. we can run hashcat with the following options:

cudaHashcat-plus32.exe --attack-mode 3 --custom-charset1 abcdefghijklmnopqrstuvwxyz0123456789 hash.txt 448?1?1?1?1?1?1?1

It would take about 43 minutes for a NVIDIA GeForce 405 graphic card to recover the shared secret:

And the shared secret value is: bm28lg1. In order to calculate the session key value (kc) we can simply set the R to 448 in authentication server source code instead of the random value and compile it. By running the client binary using the recovered secret key value (bm28lg1), we will get the session key:

And the session key value is : 07e0f7a7cbc2d8b3dba6b7d3b69c3236

I saw a similar solution (in Spanish) on the internet posted here . I also received a question not about the challenge itself, but the source code of the authentication client and why I'v set resp buffer size it to 128 bytes while the client response length is always 21 bytes (basically why I've wasted 107 bytes of 1MB default stack). The answer is that the server not only processes RESP messages from the client, but also need to receive and decrypt MSG messages (which is marked as not implemented in both source codes). MSG messages clearly have a bigger size than 21 bytes and in order to use the same RESP buffer for incoming data, I set its size to 128 bytes which is purely an arbitrary number in this case and should be changed to a more suitable size based on the encryption algorithm's block sizes which are not implemented in the current code.

If you have questions or recommendations regarding this challenge (or similar ones), please drop me an email to the address inside the challenge file.

There has been a healthy reaction to our initial post on our research into the RSA SecureID Software Token. A number of readers had questions about certain aspects of the research, and I thought I'd clear up a number of concerns that people have.

The research pointed out two findings; the first of which is in fact a design vulnerability in RSA software's "Token Binding" mechanism. The second finding is another design issue that affects not only RSA software token but also any other software, which generates pseudo-random numbers from a "secret seed" running on traditional computing devices such as laptops, tablets or mobile phones. The correct way of performing this has been approached with hardware tokens, which are often tamper-resistant.

Let me first explain one of the usual use cases of RSA software token deployments:

1. The user applies for a token via a RSA self-service console or a custom web form
2. The user receives an email which contains the "software token download URL", once the software is installed, they should open the program and then choose Token Storage Devices where they would read the "Device Serial Number" and reply back with this device serial number to complete their token request.
3. The second email will contain an attachment of the user's personal RSA SecurID Token Configuration file, which they will import to the RSA software token. This configuration file is bound to the users' laptop or PC.
4. The third email contains an initial password to activate the token.

An attacker who is able to capture the victim's configuration file and initial password (The security of this initial password is subject to future research at SensePost and will be released in the future) would be able to import it into his token using the described method to bypass the token binding. This attack can be launched remotely and does not require a "fully compromised machine" as RSA have stated.

The second finding, as I mentioned before, is a known issue with all software tokens. Our aim at SensePost was to demonstrate how easy/hard it would be for an attacker, who has already compromised a system, to extract RSA token secrets and clone them on another machine. A number of people commented on the fact that we did not disclose the steps required to update the LSA secrets on the cloned system. Whilst this technique is relatively easy to do, it is not required for this attack to function.

If a piece of malware was written for this attack, it does NOT have to grab the DPAPI blobs and replicate them on the attackers machine. It can simply hook into the CryptUnprotectData and steal the decrypted blobs once the RSA software token starts execution. The sole reason I included the steps to replicate the DPAPI on another machine, was that this research was performed during a real world assessment, which was time-limited. We chose to demonstrate the attack to the client by replicating the DPAPI blobs instead of developing a proof of concept malcode.

A real-world malware targeting RSA software tokens would choose the API hooking method or a similar approach to grab the decrypted seed and post it back to the attacker.

"I'm also curious to know whether software token running on smartphones might be vulnerable."

The "Token Binding" bypass attack would be successful on these devices, but with a different device serial ID calculation formula. However, the application sandboxing model deployed on most modern smartphone operating systems, would make it more difficult for a malicious application, deployed on the device, to extract the software token's secret seeds. Obviously, if an attacker has physical access to a device for a short time, they would be able to extract those secrets. This is in contrast to tamper-proof hardware tokens or smart cards, which by design provide a very good level of protection, even if they are in the hands of an attacker for a long time.

"Are the shortcomings you document particular to RSA or applicable to probably applicable to Windows software tokens from rival vendors too?"

All software tokens found to be executing a pseudo-random number generation algorithm that is based on a "secret value", are vulnerable to this type of cloning attack, not because of algorithms vulnerabilities, but simply because the software is running on an operating system and storage that is not designed to be tamper-resistance like modern smart cards, TPM chips and secure memory cards.

One solution for this might be implementing a "trusted execution" environment into CPUs, which has been done before for desktop and laptops by Intel (Intel TXT) and AMD. ARM's "trustzone" technology is a similar implementation, which targets mobile phone devices and secures mobile software's from logical and a range of physical attacks.

Widespread use of smart phones by employees to perform work related activities has introduced the idea of using these devices as an authentication token. As an example of such attempts, RSA SecureID software tokens are available for iPhone, Nokia and the Windows platforms. Obviously, mobile phones would not be able to provide the level of tamper-resistance that hardware tokens would, but I was interested to know how easy/hard it could be for a potential attacker to clone RSA SecureID software tokens. I used the Windows version of the RSA SecurID Software Token for Microsoft Windows version 4.10 for my analysis and discovered the following issues:

Device serial number of tokens can be calculated by a remote attacker :

Every instance of the installed SecurID software token application contains a hard drive plug-in (implemented in tokenstoreplugin.dll) that has a unique device serial number. This serial number can be used for "Device Binding" and the RSA documentation defines it as follows:

“Before the software token is issued by RSA Authentication Manager, an additional extension attribute (<DeviceSerialNumber/>) can be added to the software token record to bind the software token to a specific devicedevice serial number is used to bind a token to a specific device. If the same user installs the application on a different computer, the user cannot import software tokens into the application because the hard drive plug-in on the second computer has a different device serial number from the one to which the user's tokens are bound”.

Reverse engineering the Hard-Disk plugin (tokenstoreplugin.dll) indicated that the device serial number is dependent on the system's host name and current user's windows security identifier (SID). An attacker, with access to these values, can easily calculate the target token's device serial number and bypass the above mentioned protection. Account SIDs can be enumerated in most of the Microsoft active directory based networks using publicly available tools, if the “enumeration of SAM accounts and shares” security setting was not set to disabled. Host names can be easily resolved using internal DNS or Microsoft RPC. The following figures show the device serial number generation code:

The SecureID device serial number calculation can be represented with the following formula:

device_serial_number=Left(SHA1(host_name+user_SID+“RSA Copyright 2008”),10)

Token's copy protection:

The software token information, including the secret seed value, is stored in a SQLite version 3 database file named RSASecurIDStorage under the “%USERPROFILE%\Local Settings\Application Data\RSA\RSA SecurID Software Token Library” directory. This file can be viewed by any SQLite database browser, but sensitive information such as the checksum and seed values are encrypted. RSA documentation states that this database file is both encrypted and copy protected: “RSA SecurID Software Token for Windows uses the following data protection mechanisms to tie the token database to a specific computer:

• Binding the database to the computer's primary hard disk drive

• Implementing the Windows Data Protection API (DPAPI)

These mechanisms ensure that an intruder cannot move the token database to another computer and access the tokens. Even if you disable copy protection, the database is still protected by DPAPI.”

The RSASecurIDStorage database file has two tables: PROPERTIES and TOKENS. The DatabaseKey and CryptoChecksum rows found in the PROPERTIES tables were found to be used for copy protection purpose as shown in the figure below:

Reverse engineering of the copy protection mechanism indicated that:

• The CryptoChecksum value is encrypted using the machine's master key, which can only be decrypted on the same computer system, unless the attacker can find a way to import the machine key and other supporting data to their machine
• The DatabaseKey is encrypted using the current logged-on user's master key and provides token binding to that user account

Previous research on the Microsoft Windows DPAPI internals has made offline decryption of the DPAPI protected data possible. This means that if the attacker was able to copy the RSA token database file along with the encryption master keys to their system (for instance by infecting a victim's machine with a rootkit), then it would be possible to decrypt the token database file on their machine. The detailed attack steps to clone a SecurID software token by copying the token database file from a victim's system are as follows:

1. Copy the token database file, RSASecurIDStorage, from the user profile directory
2. Copy the user's master key from %PROFILEDIR%\Application Data\Microsoft\Protect\%SID%; the current master key's GUID can be read from Preferred file as shown in the figure below:

3. Copy the machine's master key from the %WINDIR%\system32\Microsoft\Protect\ directory. Microsoft Windows protects machine keys against tampering by using SHA1 hash values, which are stored and handled by the Local Security Authority Subsystem Service (LSASS) process in Microsoft Windows operating systems. The attacker should also dump these hash values from LSA using publicly available tools like lsadump.


4. Having all the required master keys and token database file, install and deploy a windows machine and change the machine and user SIDs to the victim's system SID by using available tools such as newSID.
5. Overwrite the token database file, user and machine master keys with the ones copied from victim's system. You would also need to find a way to update the DPAPI_SYSTEM value in LSA secrets of the Windows machine. Currently, this is the only challenge that I was not able to solve , but it should be possible to write a tool similar to lsadump which updates LSA secrets.
6. When the above has been performed, you should have successfully cloned the victim's software token and if they run the SecurID software token program on your computer, it will generate the exact same random numbers that are displayed on the victim's token.

In order to demonstrate the possibility of the above mentioned attack, I installed and activated token A and token B on two separate windows XP virtual machines and attempted to clone token B on the virtual machine that was running token A. Taking the above steps, token B was successfully cloned on the machine running token A as shown in the following figures:

In order to counter the aforementioned issues, I would recommend the use of "trusted platform module" (TPM) bindings, which associates the software token with the TPM chip on the system (TPM chip for mobiles? there are vendors working on it).

While I was evaluating a research idea about a SCADA network router during the past week, I used available tools and resources on the Internet to unpack the device firmware and search for interesting components. During security assessments, you may find interesting embedded devices available on the network. Whilst many don't look at the feasibility of doing firmware analysis, I decided to document the steps I took to analysis my target firmware, so you can take the similar approach in the case of assessing such devices. This could also be a good indication on the feasibility of automating this process (An unfinished project was launched in 2007: http://www.uberwall.org/bin/project/display/85/UWfirmforce).

The following process would be easy for most of you who use *nix systems on a daily bases:

Step 1) Scanning the firmware image

The BinWalk tool is useful for scanning firmware image files to identify embedded file systems and compressed streams inside. It can detect common bootloaders, file systems and compressed archives inside a given firmware image file. Since it works by scanning for signature and magic values, it usually has false positives and the results need to be verified manually.

U-Boot bootloader (yes, it's German :-)) signature was identified at offset 262144 and the uImage header information, such as creation date, CPU type, etc appears to be valid. This bootloader was followed by a gzip compressed stream, which probably is the zImage kernel and a squashfs file system at offset 1522004. We will attempt to extract this file system in the next step. The following are common bootloaders that are used in embedded devices with ARM CPU:

Blob bootloader Bootldr Redboot U-Boot ABLE bootloader

The bootloader's task is to load the kernel image at the correct address and pass initial parameters to it. So in most cases we are not interested in analysing the bootloader itself, but in the root file system.

Step 2) Extracting file systems

First, I extracted the uImage content at offset 262144 by using dd command and then used uboot-mkimage (packages.debian.org/uboot-mkimage) to test if it's a valid uImage file and to discover more information about it:

The image format was valid and it contained two other file system images with 1MB and 2MB sizes, which probably are kernel zImage and root file systems (RAMdisk). If you check the uImage file format, you will notice a 64 bytes long header. There is a “multi-file” image list that contains each image size in bytes and this list is terminated by a 32bit zero. So, I would need to skip 64+2*4+4=76 bytes from start of the uImage file to get to the first Image content that would be kernel zImage:

The file command could not detect kernel image or squshfs in the extracted file systems; this might be due to lack of squashfs (with LZMA compression) in my Ubuntu kernel. I proceed by using Firmware Mod Kit which contains a set of programs to decompress various file system images including squashfs-LZMA. After trying the various unsquashfs version 3.x scripts, I was able extract the rootfs image files successfully:

Step 3) Searching the root file system

Once the root file system files were extracted, we can file and strings search tools to look for interesting files and patterns such as RSA private key files, password and configuration files, SQL database files, SQL query string and etc. In my case, I was looking for RSA certificate or private key files and found the following: (a database of private keys in embedded devices was published in 2011 but it's not actively maintained, you can access it at http://code.google.com/p/littleblackbox/)

One can write shell scripts to automate the file system search process.

Step 4) Running and debugging the Executables

The Qemu emulator supports multiple CPU architectures including ARM, MIPS, PowerPC, etc and can be used to run and debug the interesting executable extracted from the firmware image on your system for dynamic analysis purposes. You would need to build the Qemu with —static and —enable-debug options. The following figure demonstrates how to run the web server (httpd) that was extracted from my target firmware using chroot and Qemu:

As you can see from the above screenshot, the web server was working fine, but was not able to display the bootloader version, because it couldn't read this value from the NVRAM (not volatile RAM) normally mounted by the kernel in a real device (there is an interesting post here about resolving the NVRAM access errors while emulating embedded device executables). Some of the executables, like the remote management agent example below, could have more severe problems running under emulator.

For troubleshooting such cases, or monitoring an emulated process while fuzzing it, we would need to attach a debugger to it. This can be achieved by using —g switch in Qemu and using a debugger out of the emulator process or even on a remote windows machine. I used IDA pro remote GDB debugging tool as shown in the figures below:

Once successfully attached to the remote emulated process, IDA pro can be used to simply trace the execution of the process, placing breakpoints or running IDA scripts.

Often overlooked during assessments, firmware analysis of devices can yield results and often do when we target them at SensePost. Our methodology includes the above steps and we recommend yours does too.

We were asked to contribute an article to PenTest magazine, and chose to write up an introductory how-to on footprinting. We've republished it here for those interested.

Network foot printing is, perhaps, the first active step in the reconnaissance phase of an external network security engagement. This phase is often highly automated with little human interaction as the techniques appear, at first glance, to be easily applied in a general fashion across a broad range of targets. As a security analyst, footprinting is also one of the most enjoyable parts of my job as I attempt to outperform the automatons; it is all about finding that one target that everybody forgot about or did not even know they had, that one old IIS 5 webserver that is not used, but not powered off.

With this article I am going to share some of the steps, tips and tricks that pentesters and hackers alike use when starting on a engagement.

Approach

As with most things in life having a good approach to a problem will yield better results and overtime as your approach is refined you will consume less time while getting better results. By following a methodology, your footprinting will become more repeatable and thus reliable. A basic footprining methodology covers reconnaissance, DNS mining, various information services (e.g. whois, Robtex, routes), network registration information and active steps such as SSL host enumeration.

While the temptation exists to merely feed a domain name into a tool or script and take the output as your completed footprint, this will not yield a passable footprint for two reasons. Firstly, a single tool will not have access to all the disparate information sources that one should consult, and secondly the footprinting process is inherently iterative and continuous. A footprint is almost never complete; instead, a fork of the footprint data provides the best current view of the target, but the information could change tomorrow as new sites are brought online, or old sites are taken offline. As a new piece of data is found that could expand the footprint, a new iteration of the footprinting process triggers with that datum as the seed, and the results are combined with all discovered information.

Know your target

The very first thing to do is to get to know your target organisation. What they do, who they do it for, who does it for them, where they do it from - both online and in the kinetic world, what community or charity work they are involved in. This will give you an insight into what type of network/infrastructure you can expect. Reading public announcements, financial reports and any other documents published on or by the organisation might also yield interesting results. Any organisation that must publish regular reports (e.g. listed companies), provide a treasure trove of information for understanding the target's core business units, corporate hierarchy and lines of business. All these become very useful when selecting targets.

Dumpster diving, if you are up for it and have physical access to the target, means sifting through trash to get useful information, but in recent times social media can provide us with even more. Sites like LinkedIn, Facebook and Twitter can provide you with lists of employees and projects that the organisation is involved with and perhaps even information about third party products and suppliers that are in use.

One should even keep an eye out for evidence of previous breaches or loss of credentials. It has become common place for hackers to post information about security breaches on sites like pastebin.com. The most likely evidence would be credentials in the form of corporate emails and passwords being reused on unrelated sites that are hacked, and have their user databases uploaded. In addition, developers use sites like Pastebin to share code, ideas and patches, and if you are lucky you might just find a little snippet of code sitting out in the open on Pastebin, that will give you the edge.

DNS

“The Domain Name System (DNS) is a hierarchical distributed naming system for computers, services, or any resource connected to the Internet or a private network.” — WikiPedia

In a nutshell, DNS is used to convert computer names to their numeric addresses.

Start by enumerating every possible domain owned by the target. This is where the information from the initial reconnaissance phase comes in handy, as the target's website will likely point to external domains of interest and also help you guess at possible names. With a list of most discovered domains in hand, move on to a TLD (Top level domain) expand. TLDs are the highest level subdomains in DNS; .com, .net, .za, .mobi are all examples of TLDs (The Mozilla Organization maintains a list of TLDs https://wiki.mozilla.org/TLD_List).

In the next step, we take a discovered discovered domain and check to see if there are any other domains with the same name, but with a different TLD. For example, if the target has the domain victim.com, test whether the domains victim.net, victim.info, victim.org etc. exist and if they exist check to see if they are owned by our target organization. To determine whether a domain exists or not, one should examine the SOA (start of authority) DNS record for the domain. Using commands like nslookup under Microsoft Windows or the dig/host commands under most of the *nix family will reveal SOA records.

Using dig, “dig zonetransfer.me soa”.

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Figure 1: Using dig to get the SOA (Start of authority) record for a domain

If, by verifying the SOA, it is confirmed that the domain exists, then the next step is to track down who it belongs to. At this point the whois service is called upon. ‘Whois' is simply a registry that contains the information of the owner of a domain. Note that it is not entirely reliable and certainly not consistent. The following very simple query “whois zonetransfer.me” provides us with the owner of the domain “zonetransfer.me” detail.

Figure 2: Using whois to get the domain owner detail

After finding domains, running them through a TLD expansion and verifying their whois information, it is time to track down hosts. First we need to get the NS or name server records for the domains. Again using “dig zonetransfer.me ns” returns a list of all the name servers used by this domain. In many cases the name server will not be part of the target's network and is often out-of-scope, but they will still be used in the next step.

DNS yields much interesting information, but the default methods for extracting information from foreign servers effectively relies on a brute force. However, DNS supports a trick where all DNS information for a zone can be downloaded if the server allows it, and this is called a “zone transfer”. When enabled, they are extremely useful as they negate the need for guessing or brute-forcing; sadly they are commonly disabled. Still, given the usefulness of zone transfers it is always worth testing for. Zone transfers should be performed against all the name servers that are specified in the NS records of a domain as the data contained in each name server should be the same, but the security configuration might be different. Using dig, the following command will attempt to perform a zone transfer “dig axfr @ns12.zoneedit.com zonetransfer.me”

Figure 3: Performing a zone transfer using dig

As mentioned previously, zone transfers are not that common. When we cannot download the zone file, there are a couple of other tricks that might work. One is to brute force or guess host names: by using a long list of common hostnames one can test for names such as “fw.victim.com”, “intranet.victim.com”, “mail.victim.com” and so on. The names can be commonly seen hostnames, generated names when computers are assigned numeric or algorithmic names, or from sets of related names such as characters from a book series. When brute forcing DNS, be sure to check the following DNS records: CNAME, A and AAAA. Again this is easy using a tool like dig. “dig www.google.com a” produces the DNS configuration for www.google.com, note that the hostname www.google.com actually has multiple DNS entries, one CNAME record, and multiple A records. Looking at the IP addresses it is clear that there are several different hosts (2 in the screenshot below).

Figure 4: Using dig to get the a record for a host entry

Doing this manually seems easy and quick, (and it is) but if we want to brute force or guess many host names, then this will take too long. Of course, it is easy enough to script these commands to automate the process; however there are existing tools written specifically for this purpose. One of the most popular tools, Fierce, is a perl script written by RSnake (http://ha.ckers.org/fierce/), which is easy to use and has many useful functions. Additionally, there are tools like Paterva's Maltego and SensePost's Yeti (a tool I wrote) which provide graphical tools for this purpose.

If we happen to have a list of IP addresses or IP netblocks of the target, then a further DNS trick is to convert the addresses into hostnames using reverse lookups to get the PTR record entry. This is useful since reverse records are easily brute forced in IPv4. Bear in mind that DNS does not require a PTR record (reverse entry) or that entries in the reverse zone must match entries in the forward zone. But the result can give you an idea of whether the host is a shared host, owned and hosted by the company or just remote hosted.

To test once more, try using dig, “dig 104.66.194.173.in-addr.arpa ptr”. While this too can be easily automated, the previously mentioned tools will also handle PTR records.

Search engines:

DNS interrogation and mining forms the bulk foot printing, but thanks to modern search engines like Google and Bing, finding targets has become much easier.

Apart from the normal searching for your target, as you would do in your initial phase, you can actually use the data that you discovered during the course of the DNS mining to try and get further information using search engines. Bing from Microsoft provides us with two really useful search operators: “ip:” and “site:”. When using the “ip:” operator, Bing will return a list of hosts that it has indexed that resolve to the IP address that you have specified. Alternatively the “site:” operator when used with a domain name, will return a list of host names that have been indexed by the search engine and belong to the domain specified. Quick and easy, and Bing also provides you with a very simple free API that you can use to automate these searches.

Address mapping

All this fuss with DNS is important, but it is only useful insofar as they lead us to addresses. The next step is discovering where the target exists within the IP address space. Luckily useful tools and resources exist to help us uncover these ranges, by automating a combination of manual techniques such as whois querying, traceroute and netblock calculators. In the previous section the whois tool was used to get the domain owner information. The same tool can be used to discover the ownership/assignment details of a specific IP address. Let's take www.facebook.com; one of the IP addresses that it resolves to is 69.63.190.10. “whois 69.63.190.10” produces the following output.

Figure 5: Getting the netblock and owner using whois

From the whois output we get really useful information. First is a netblock range 69.63.176.0-69.63.190.255 as well as the owner of this net block, namely Facebook, Inc. In this case we are lucky and the netblock is registered to facebook, but often you will only get the network service provider to which the netblock is allocated to. In that case, you will have to query the service provider in order to gain more info about the specific netblock. Online resources can also be very useful, for example ARIN (American Registry for Internet Numbers) or any of the other regional registries (RIPE, AfriNIC, APNIC and LACNIC) provides a reverse whois search interface where one can search for organisation names and other terms, even performing wild card searches. Giving Facebook a second look, we try a search on the reverse whois interface found at http://whois.arin.net/ with the term “facebook”, and get a list of five additional network ranges.

Figure 6: Search results for ARIN reverse whois

SSL Certificates

Lastly, we turn to SSL. SSL may be more familiar as a “protection” against nasty eavesdroppers and men-in-the-middle, but it is useful for footprinters. How? It is really simple actually, one of the security checks performed by browsers when deciding on the validity of a SSL certificate is whether the Common Name contained in the certificate matches the DNS name of the host requested from the browser. How does this help? Say a list of IP addresses has been produced; the next step would be to perform a reverse lookup of all these addresses. However, if no reverse entry is present and Bing has no record of the IP, then some creativity is called for. If an HTTPS website is hosted on that address then simply browse to that IP address and, when presented with the invalid certificate error, message, look for the “real” host name.

Figure 7: Firefox reporting the common name contained in a SSL certificate for a host

Again, this is something that is easily automated, so we have included a module in Yeti to actually do this for you.

Conclusion

Foot printing might at first glance appear to be simple and mundane, but the more you do it, the more you will realise that very few organisations have a handle on exactly what they have and what they present to the Internet. As the Internet and networks evolve so will the way companies and organisations use it, and so will their footprint. A year-old footprint could be hopelessly outdated, and ongoing footprinting helps organisations maintain a current view of their threat landscape.

With the ongoing move away from local infrastructure to hosted infrastructure, the footprint expands, spreads and grows, and so will our quest to find as much as possible.