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sink rules If the precondition is not met, the rule will trigger In the case of sink rules, a violation of the precondition results in the static analysis tool reporting an instance of the vulnerability the rule represents Postcondition Describes changes to the taint propagation algorithm s state that occur when a method or function the rule matches is encountered Postcondition statements typically taint or cleanse certain variables, such as the return value from the function or any of its arguments, and can also include assignment of taint ags to these variables Postconditions represent source or passthrough information Severity Allows the rule de nition to specify the severity of the issues the taint propagation algorithm produces when a sink rule is triggered In some cases, it is important to be able to differentiate multiple similar results that correspond to the same type of vulnerability
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Most of the academic research effort invested in static analysis tools is spent devising clever new approaches to identifying defects But when the time comes for a tool to be put to work, the way the tool reports results has a major impact on the value the tool provides Unless you have a lab full of PhD candidates ready to interpret raw analyzer output, the results need to be presented in such a way that the user can make a decision about the correctness and importance of the result, and can take an appropriate corrective action That action might be a code change, but it might also be an adjustment of the tool Tool users tend to use the term false positive to refer to anything that might come under the heading unwanted result Although that s not the de nition we use, we certainly understand the sentiment From the user s perspective, it doesn t matter how fancy the underlying analysis algorithms are If you can t make sense of what the tool is telling you, the result is useless In that sense, bad results can just as easily stem from bad presentation as they can from an analysis mistake It is part of the tool s job to present results in such a way that users can divine their potential impact Simple code navigation features such as jumpto-de nition are important If a static analysis tool can be run as a plug-in inside a programmer s integrated development environment (IDE), everyone wins: The programmer gets a familiar code navigation setup, and the static analysis tool developers don t have to reinvent code browsing
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Auditors need at least three features for managing tool output: Grouping and sorting results Eliminating unwanted results Explaining the signi cance of results We use the Fortify audit interface (Audit Workbench) to illustrate these features Figure 49 shows the Audit Workbench main view
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Figure 49 The Audit Workbench interface
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Grouping and Sorting Results If users can group and sort issues in a flexible manner, they can often eliminate large numbers of unwanted results without having to review every issue individually For example, if the program being analyzed takes some of its input from a trusted file, a user reviewing results will benefit greatly from a means by which to eliminate all results that were generated under the assumption that the file was not trusted
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Because static analysis tools can generate a large number of results, users appreciate having results presented in a ranked order so that the most important results will most likely appear early in the review Static analysis tools have two dimensions along which they can rank results Severity gives the gravity of the nding, under the assumption that the tool has not made any mistakes For example, a buffer over ow is usually a more severe security problem than a null pointer dereference Con dence gives an estimate of the likelihood that the nding is correct A tool that ags every call to strcpy() as a potential buffer over ow produces low con dence results A tool that can postulate a method by which a call to strcpy() might be exploited is capable of producing higher con dence results In general, the more assumptions a tool has to make to arrive at a result, the lower the con dence in the result To create a ranking, a tool must combine severity and con dence scores for each result Typically, severity and con dence are collapsed into a simple discrete scale of importance, such as Critical (C), High (H), Medium (M), and Low (L), as shown in Figure 410 This gives auditors an easy way to prioritize their work
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