  We present progress on the experimental validation of a fundamental and
universally applicable vulnerability analysis framework that is capable of
identifying new types of vulnerabilities before attackers innovate attacks.
This new framework proactively identifies system components that are vulnerable
based upon their Kolmogorov Complexity estimates and it facilitates prediction
of previously unknown vulnerabilities that are likely to be exploited by future
attack methods. A tool that utilizes a growing library of complexity estimators
is presented. This work is an incremental step towards validation of the
concept of complexity-based vulnerability analysis. In particular, results
indicate that data types (semantic types) can be identified by estimates of
their complexity. Thus, a map of complexity can identify suspicious types, such
as executable data embedded within passive data types, without resorting to
predefined headers, signatures, or other limiting a priori information.
