The short one: "A Look at Terrorist Behavior: How They Prepare, Where They Strike," by Brent Smith, National Institute of Justice Journal, No. 260, 2008.

The long one: How Terrorist Groups End: Lessons for Countering al Qa'ida, by Seth G. Jones and Martin C. Libicki, RAND Corporation, 2008.

During the presentation I mentioned in some depth how to perform fuzz testing, and what parts of an application should be fuzz testing targets. I also introduced an idea (that's not new) to help people who have never performed fuzz testing begin fuzz testing with very little cost and friction. The idea is to add a small layer of code to an application to automatically mutate untrusted data as it comes into an application; I called that code layer "a layer of hurt."

Before I continue, I want to point out that fuzzing is an SDL requirement, but the idea in this blog post is not an SDL requirement, it's just another way to help meet SDL fuzzing requirements.

Adding a layer of hurt, as shown in the picture below, is pretty simple as it involves adding code to an application to tweak data as it comes into an application. You can work out where to place the fuzzing code by looking at your threat models to see where data crosses trust boundaries. You could also simply grep the code looking for APIs that read data, for example:

• Read from files: fread, ReadFile
• Reading from sockets: recv, recvfrom
• For .NET code, any stream.Read

You get the picture.

The fuzzing code should appear right after the API that reads that data.

For example, C or C++ code that reads from a UDP socket and then fuzzes the data before it's consumed by the rest of the application might look like this:

Or, in C#, code that reads from an untrusted file:

In both code examples, Fuzz() mutates the incoming data. In the C++ case, the fuzzing code looks like this:

The sample C# and C++ fuzzing code is available as a ZIP file at the end of this post.

This code is an example of dumb-fuzzing, which is fuzzing with little or no regard for the data structure being manipulated. If you've never performed any kind of fuzz testing in the past, then you will probably find bugs with this simple fuzzing technique. Once you have weeded out the low-hanging bugs, you may need to turn your attention to smarter fuzzers. For example, in theory, this code would find few if any bugs in a PNG parser, because PNG files have a built in check-sum, so if you fuzz a PNG file, you'd have to recalculate the checksum to get decent code coverage.

When I showed this code during my presentation, I urged people to add it to their applications today if they currently don't do fuzz testing, and simply run their applications through their normal testing processes. Within three days of my presentation I received emails from people saying they had found bugs. I have no doubt others did too.

One of the comments I made during the session was,"If you can't spend the time on great fuzzing, fuzz anyway" and adding a "layer of hurt" is a reasonable start.

Please feel free to sound off if you have ideas to help improve the code and let us know what you think, either through email or comments to this post.

On May 13th, 2008 the Debian project announced that Luciano Bello found an interesting vulnerability in the OpenSSL package they were distributing. The bug in question was caused by the removal of the following line of code from md_rand.c

	MD_Update(&m,buf,j);
	[ .. ]
	MD_Update(&m,buf,j); /* purify complains */

These lines were removed because they caused the Valgrind and Purify tools to produce warnings about the use of uninitialized data in any code that was linked to OpenSSL. You can see one such report to the OpenSSL team here. Removing this code has the side effect of crippling the seeding process for the OpenSSL PRNG. Instead of mixing in random data for the initial seed, the only "random" value that was used was the current process ID. On the Linux platform, the default maximum process ID is 32,768, resulting in a very small number of seed values being used for all PRNG operations.

More info, from Debian, here. And from the hacker community here. Seems that the bug was introduced in September 2006.

More analysis here. And a cartoon.

Random numbers are used everywhere in cryptography, for both short- and long-term security. And, as we've seen here, security flaws in random number generators are really easy to accidently create and really hard to discover after the fact. Back when the NSA was routinely weakening commercial cryptography, their favorite technique was reducing the entropy of the random number generator.

(FYI, for the impatient, click here.)

There are two ways to generate random numbers on computers: (1) use a software program called a Pseudorandom Number Generator (PRNG) or (2) use a hardware random number generator. A Pseudorandom Number Generator uses a seed value to generate a sequence of numbers that appear random. The problem is that the same seed generates the same random sequence. The hardware based RNG observes and samples some physical phenomenon which is random, such as cosmic rays, RF noise, etc. (aka Entropy).

RNGs are important in Information Security because they are used to generate encryption keys, salts, etc. Historically, attacking RNGs has proven effective, such as the defeat of Netscape’s HTTPS sessions.

Most operating systems utilize a hybrid approach, implementing a PseudoRandom Number Generator that has a seed that is regularly updated through the collection of random hardware events. This process is called Entropy Collection or Entropy Harvesting. For most applications, this approach should be completely sufficient. However, one of the key assumptions is that the operating system has been up and running long enough for the seed value itself to become hard to predict through the collection of Entropy. Also, many of the Entropy collecting events come from properties of hardware devices, such as the minor variations in hard drive rate of rotation. As such, there are a few circumstances where the OS RNG may not be good enough for strong cryptographic key generation:

• Live Boot CD ( The start state of the RNG may be predictable. )
• Virtualized Hosts ( OS may be dependent on simulated events for randomness. )

( Given the exploding popularity of virtualization, this is an area worthy of research. Stay tuned. )

Design of the Got Entropy Service

Many RNGs (such as the one included in Linux, as well as OpenSSL’s) allow the addition of entropy from outside sources. So I started looking to Entropy sources I could use to bolster the RNGs on my virtual hosts (and other uses…). While I was looking into this, it occurred to me that I had an unused TV tuner card, a PVR-350.

When a TV is tuned to a channel with no local station, the ’snow’ on the screen is RF noise (the same as the static between stations on AM radios). But, for reasons beyond our scope, you never use a direct physical observation as the RNG. You have to ‘de-skew and whiten’ the data prior to sampling it. Here is the process that I use:

1. Collect about 3 minutes of video ( about 130 MB data ).
2. Using a random key and IV, encrypt the data ( using openssl & AES-128-CBC ).
3. Discard the first 32k of the file.
4. Use each of the following 32k blocks as samples.
5. Compress each sample with SHA-256.
6. Discard the last block.

• Steps 2 and 3 remove any patterns, such as MPEG file formatting, from the data.
• Steps 4 and 5 generate a 32-byte random value ( 1024 to 1 compression in the hash ).

Check it out at http://gotentropy.artofinfosec.com

Can an Attacker Broadcast a Signal to Undermine This?

Such an attacker could not remove RF noise from the received signal. Our eyes and brains are good at filtering out the noise in the TV video, but there is a lot of it. Part of the noise comes from the atmospheric background RF, but there are also flaws (noise) in the tuner’s radio and analog-to-digital capture circuitry.

I think this is a pretty strong RNG, and I have provided an interface for pulling just the values.

Also, I have written a script ( getEntropy.sh ) that will pull Entropy from the service and seed it into /dev/random on Linux.

Results from ENT

Here are results, from a sample run of the Got Entropy, analyzed by ENT ( A Pseudorandom Number Sequence Test Program provided by John Walker of www.fourmilab.ch - Thanks, John! ).

• Entropy = 7.999987 bits per byte
• Optimum compression would reduce the size of this 13366112 byte file by 0 percent.
• Chi square distribution for 13366112 samples is 233.85, and randomly would exceed this value 82.48 percent of the time.
• Arithmetic mean value of data bytes is 127.4767 (127.5 = random).
• Monte Carlo value for Pi is 3.143054786 (error = 0.05 percent).
• Serial correlation coefficient is -0.000078 (totally uncorrelated = 0.0).

Resources for the Curious…

• Wikipedia - Pseudo-random Number Generator
• Wikipedia - Hardware Random Number Generator
• NIST - Random Numbers Page
• Netscape RNG Attack
• van Heusden Video Rand

Cheers, Erik

Art of Information Security would love your feedback !

Got Entropy ?

• File defects against the old code for any use of a banned function, without investigating the specific use
• File defects against old code only after verifying that in the context you have a potential vulnerability
• Get a dedicated team together to just go and clean up old code