Message-ID: <Pine.LNX.4.58.0701261830530.30421@dione>
Date: Sat, 27 Jan 2007 13:29:08 +0100 (CET)
From: Michal Zalewski <lcamtuf@...ne.ids.pl>
To: webappsec@...urityfocus.com, pentest@...urityfocus.com
Cc: bugtraq@...urityfocus.com, full-disclosure@...ts.grok.org.uk
Subject: stompy the session stomper - tool availability

Hi all,

I'd like to announce the availability of 'stompy', a free tool to perform
a fairly detailed black-box assessment of WWW session identifier
generation algorithms. Session IDs are commonly used to track
authenticated users, and as such, whenever they're predictable or simply
vulnerable to brute-force attacks, we do have a problem.

[ The reason I'm cc:ing BUGTRAQ is that this tool already revealed several
  new, potential weaknesses in application platforms, and can be readily
  used to find more - for example, it is my impression that BEA WebLogic
  and Sun Java System Web Server both have problems with their JSESSIONIDs
  [1]; proprietary solutions by some of the larger portals / e-commerce
  sites didn't always earn a passing grade, either. ]

Why bother?
===========

Some session ID cookie generation mechanisms are well-studied and
well-documented, and believed to be cryptographically secure (example:
Apache Tomcat, PHP, ASP.NET builtins). This is not necessarily so for
certain less researched enterprise web platforms - and almost never so for
custom solutions that are frequently implemented inside the web
application itself.

Yet, while there are several nice GUI-based tools designed to analyze HTTP
cookies for common problems (Daves' WebScarab, SPI Cookie Cruncher,
Foundstone CookieDigger, etc), they all seem to rely on very trivial, if
any, tests when it comes to unpredictability ("alphabet distribution" or
"average bits changed" are top shelf); this functionality is often not
better than a quick pen-and-paper analysis, and can't be routinely used to
tell a highly vulnerable linear congruent PRNG (rand())  from a
well-implemented MD5 hash system (/dev/urandom).

As far as I can tell, today's super-bored pen-testers can at best collect
data by hand, determine its encoding, write conversion scripts, and then
feed it to NIST Statistical Test Suide or alike - but few will.

What's cool?
============

In order to have a fully automated, hands-off tool to reliably detect
anomalies that are not readily apparent at a first glance, I devised an
utility that:

  - Automatically finds session IDs encoded as URLs, cookies, and
    in form inputs, then collects a statistically significant sample
    of data,

  - Determines alphabet structure to transparently handle base64,
    uuencode, base32, hex, and any other sane encoding scheme
    without user intervention,

  - Translates the data to isolated time-domain bitstreams to
    examine how SID bits at each position change in time,

  - Runs a suite of FIPS-140-2 PRNG evaluation tests on the sample,

  - Runs an array of n-dimensional phase space tests to find
    deterministic correlations, PRNG hyperplanes, etc, etc.

Of course, the tool cannot prove the correctness of an implementation, and
it is possible to devise predictable, cryptographically unsafe PRNGs that
would pass these tests; still, the tool can find plenty of problems and
oddities.

Well, that's it. For more, see the included README file. The application,
in a fairly decent shape (not a wobbly PoC) and tested under Linux,
FreeBSD, and CYGWIN, can be downloaded here:

  http://lcamtuf.coredump.cx/stompy.tgz

Cheers,
/mz

[1] BEA Weblogic test output: http://lcamtuf.coredump.cx/BEA.log; in
    response to WebScarab analysis, BEA stated some time ago that the
    beginning of the identifier might be deterministic at MSB positions:
    http://dev2dev.bea.com/blog/neilsmithline/archive/2006/03/jsessionid_valu_1.html
    ...but 'stompy' output seems to clearly indicate that all the
    data exhibits strong biases, irregularities, and correlation
    patterns, and as such, the randomness of their "very large random
    number" is questionable at best.

.

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