Automated exploit detection using path profiling The disposition should matter, not the position

Abstract: Recent advances in static and dynamic program analysis resulted in tools capable to detect various types of security bugs in the Applications under Test (AUTs). However, any such analysis is designed for a priori specified types of bugs and it is characterized by some rate of false positives or even false negatives and cer-tain scalability limitations. We present a new analysis and source code classification technique, and a proto-type tool aiming to aid code reviews in the detection of general information flow dependent bugs. Our ap-proach is based on classifying the criticality of likely exploits in the source code using two measuring func-tions, namely Severity and Vulnerability. For an AUT, we analyse every single pair of input vector and pro-gram sink in an execution path, which we call an Information Block (IB). A classification technique is in-troduced for quantifying the Severity (danger level) of an IB by static analysis and computation of its Entro-py Loss. An IB’s Vulnerability is quantified using a tainted object propagation analysis along with a Fuzzy Logic system. Possible exploits are then characterized with respect to their Risk by combining the computed Severity and Vulnerability measurements through an aggregation operation over two fuzzy sets. An IB is characterized of a high risk, when both its Severity and Vulnerability rankings have been found to be above the low zone. In this case, a detected code exploit is reported by our prototype tool, called Entroine. The ef-fectiveness of our approach has been tested by analysing 45 Java programs of NIST’s Juliet Test Suite, which implement three different common weakness exploits. All existing code exploits were detected with-out any false positive.