Machine learned model for generating opinionated threat assessments of security vulnerabilities
US-2024411898-A1 · Dec 12, 2024 · US
US9971897B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-9971897-B2 |
| Application number | US-201213431808-A |
| Country | US |
| Kind code | B2 |
| Filing date | Mar 27, 2012 |
| Priority date | Dec 30, 2011 |
| Publication date | May 15, 2018 |
| Grant date | May 15, 2018 |
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Source code of a plurality of web pages including script code is statically analyzed. A page including a potential vulnerability is identified based on the static analysis. A page not including a potential vulnerability is identified based on the static analysis. The web page including the potential vulnerability is dynamically analyzed using a set of test payloads. The page not including the potential vulnerability is dynamically analyzed using a subset of the set of test payloads, the subset including fewer test payloads than the set of test payloads.
Opening claim text (preview).
What is claimed is: 1. A computer implemented method comprising: statically analyzing, on a computing device, source code of a plurality of web pages including script code; identifying, on the computing device, a first web page of the plurality of web pages as including a potential vulnerability based on the static analysis; identifying, on the computing device, a second web page of the plurality of web pages as not including a potential vulnerability based on the static analysis; flagging, based on the static analysis, the first web page of the plurality of web pages as including the potential vulnerability; flagging, based on the static analysis, the second web page of the plurality of web pages as not including the potential vulnerability; dynamically analyzing, on the computing device, the first web page of the plurality of web pages using a set of test payloads per parameter tested based upon flagging the first web page as including the potential vulnerability to verify whether the potential vulnerability is a false positive; and dynamically analyzing, on the computing device, the second web page of the plurality of web pages using a subset of the set of test payloads based upon flagging the second web page as not including the potential vulnerability, the subset including fewer test payloads per parameter tested than the set of test payloads per parameter tested used for the first web page of the plurality of web pages based upon flagging the first web page as including the potential vulnerability, and wherein the subset tests for one or more common sources of vulnerabilities; wherein a ratio of the test payloads per parameter in the subset of the set of test payloads to the test payloads per parameter in the set of test payloads is between 1:10 and 1:200. 2. The computer implemented method of claim 1 , further comprising receiving the plurality of web pages from a web server. 3. The computer implemented method of claim 1 , wherein the plurality of web pages are associated with a web application. 4. The computer implemented method of claim 1 , wherein statically analyzing the source code of the plurality of web pages, includes building a model representing a flow of data through the source code of the plurality of web pages. 5. The computer implemented method of claim 1 , wherein the potential vulnerability includes a data-flow violation based on a set of security rules. 6. The computer implemented method of claim 1 , wherein statically analyzing the source code of the plurality of web pages includes static taint analysis. 7. The computer implemented method of claim 1 , wherein dynamically analyzing includes executing the script code on a script engine. 8. The computer implemented method of claim 1 , wherein the subset of the set of test payloads includes between 1 to 10 test payloads per parameter and the set of test payloads includes between 100 to 200 test payloads per parameter.
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