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SATE 2010 Background Vadim Okun, NIST October 1, 2010 The SAMATE Project

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Presentation on theme: "SATE 2010 Background Vadim Okun, NIST October 1, 2010 The SAMATE Project"— Presentation transcript:

1 SATE 2010 Background Vadim Okun, NIST October 1, 2010 The SAMATE Project

2 2 Cautions on Using SATE Data Our analysis procedure has limitations In practice, users write special rules, suppress false positives, and write code in certain ways to minimize tool warnings. There are many other factors that we did not consider: user interface, integration, etc. So do NOT use our analysis to rate/choose tools

3 Overview 3 X X X X Human findings CVE entries Program Buf Leak Race … Security? Quality? Insignificant? False? ? Tool A Tool B Tool C X Tools that work on source code

4 4 SATE 2010 timeline Choose test programs (2 C, 1 C++, 2 Java). Provide them to tool makers (28 June) Teams run their tools, return reports (30 July) Analyze the tool reports (22 Sept) Report at the workshop (1 Oct) Teams submit a research paper (Dec) Publish data (between Feb and May 2011)

5 5 Participating teams Armorize CodeSecure Concordia University Marfcat Coverity Static Analysis for C/C++ Cppcheck Grammatech CodeSonar LDRA Testbed Red Lizard Software Goanna Seoul National University Sparrow SofCheck Inspector Veracode –a service company

6 6 Test cases Dovecot: secure IMAP and POP3 server – C Pebble: weblog server – Java Wireshark - C Google Chrome – C++ Apache Tomcat - Java All are open source programs All have aspects relevant to security From 30k LoC (Pebble) to 4.7M LoC (Chrome) CVE-based pairs (vulnerable and fixed)

7 7 Tool reports XMLHTML DB Original tool formats Teams converted reports to SATE format –Several teams also provided original reports Described environment in which they ran tool Some teams tuned their tools Several teams provided analysis of their tool warnings … SATE format

8 8 Analysis procedure Tool warnings ~60K warnings Select randomly Related to human findings 3 Selection Methods Analyze for correctness and associate Analyze the data Selected warnings Related to CVEs

9 9 Method 1 – Warning Selection For Dovecot and Pebble only We assigned severity if a tool did not Mostly avoid warnings with severity 5 (lowest) Statistically select from each warning class Select more warnings from higher severities Select 30 warnings from each of 10 tool reports –1 report had only 6 warnings –Did not analyze Marfcat warnings Total is 276

10 10 Method 2 – Human findings For Dovecot and Pebble only Security experts analyze test cases A small number of findings –Root cause, with an example trace Find related warnings from tools Goal: focus our analysis on weaknesses found most important by security experts

11 11 Method 3 - CVEs For Wireshark, Chrome, and Tomcat Identify the CVEs –Locations in code Find related warnings from tools Goal: focus our analysis on real-life exploitable vulnerabilities

12 12 Correctness categories True security weakness True quality weakness True but insignificant weakness Weakness status unknown Not a weakness

13 13 Differences from SATE 2009 Add CVE-selected test cases Include a C++ test case Larger test cases: Chrome - 4.7 MLOC More correctness categories (true quality) More detailed guidelines for analysis Still, much can be improved…

14 14 Thanks Romain Gaucher, Ram Sugasi Aurelien Delaitre, Sue Wang, Paul Black, Charline Cleraux, and other SAMATE team members Most of all, the participating teams!

15 15

16 16 Questions What weaknesses exist in real programs? What do tools report for real programs? Do tools find important weaknesses? Focus on tools that work on source code Defects that may affect security Goal is NOT too choose the “best” tools This is the 3 rd SATE (1 st in 2008)

17 17 SATE goals To enable empirical research based on large test sets To encourage improvement and adoption of tools NOT to choose the “best” tools

18 18 SATE common tool output format SQL Injection … Query is constructed with user supplied input … … one or more traces 1 to 5, with 1 – the highest optional that it is true …and other annotation

19 19 Lessons learned Guidelines for analysis often ambiguous – need to be refined even more Our analysis has inconsistencies and lapses Careful analysis takes longer than expected –We do not know the code well Tool interface is important to understand a weakness

20 20 Analysis procedure We cannot know all weaknesses in the test cases Impractical to analyze all tool warnings So analyze the following: Method 1. A subset of warnings from each tool report Method 2. Tool warnings related to manually identified weaknesses

21 21 SATE tool output format Common format in XML For each weakness –One or more trace - locations - line number and pathname –Name of weakness and (optional) CWE id –Severity: 1 to 5 (ordinal scale), with 1 – the highest –Probability that the problem is true positive –Original message from the tool –And other annotation

22 22 Our analysis Correctness of warning Associate warnings that refer to the same (or similar) weakness

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