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Automated Whitebox Fuzz Testing Patrice Godefroid (Microsoft Research)‏ Michael Y. Levin (Microsoft Center for Software Excellence)‏ David Molnar (UC-Berkeley,

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Presentation on theme: "Automated Whitebox Fuzz Testing Patrice Godefroid (Microsoft Research)‏ Michael Y. Levin (Microsoft Center for Software Excellence)‏ David Molnar (UC-Berkeley,"— Presentation transcript:

1 Automated Whitebox Fuzz Testing Patrice Godefroid (Microsoft Research)‏ Michael Y. Levin (Microsoft Center for Software Excellence)‏ David Molnar (UC-Berkeley, visiting MSR)

2 Fuzz Testing Send “random” data to application – B. Miller et al.; inspired by line noise Fuzzing well-formed “seed” Heavily used in security testing – e.g. July 2006 “Month of Browser Bugs”

3 Whitebox Fuzzing Combine fuzz testing with dynamic test generation – Run the code with some initial input – Collect constraints on input with symbolic execution – Generate new constraints – Solve constraints with constraint solver – Synthesize new inputs – Leverages Directed Automated Random Testing (DART)‏ ( [Godefroid-Klarlund-Sen-05,…])‏ – See also previous talk on EXE [Cadar-Engler-05, Cadar- Ganesh-Pawlowski-Engler-Dill-06, Dunbar-Cadar-Pawlowski- Engler-08,…]

4 Dynamic Test Generation void top(char input[4]) { int cnt = 0; if (input[0] == ‘b’) cnt++; if (input[1] == ‘a’) cnt++; if (input[2] == ‘d’) cnt++; if (input[3] == ‘!’) cnt++; if (cnt >= 3) crash(); } input = “good”

5 Dynamic Test Generation void top(char input[4]) { int cnt = 0; if (input[0] == ‘b’) cnt++; if (input[1] == ‘a’) cnt++; if (input[2] == ‘d’) cnt++; if (input[3] == ‘!’) cnt++; if (cnt >= 3) crash(); } input = “good” I 0 != ‘b’ I 1 != ‘a’ I 2 != ‘d’ I 3 != ‘!’ Collect constraints from trace Create new constraints Solve new constraints  new input.

6 Depth-First Search void top(char input[4]) { int cnt = 0; if (input[0] == ‘b’) cnt++; if (input[1] == ‘a’) cnt++; if (input[2] == ‘d’) cnt++; if (input[3] == ‘!’) cnt++; if (cnt >= 3) crash(); } I 0 != ‘b’ I 1 != ‘a’ I 2 != ‘d’ I 3 != ‘!’ good

7 Depth-First Search goo!good void top(char input[4]) { int cnt = 0; if (input[0] == ‘b’) cnt++; if (input[1] == ‘a’) cnt++; if (input[2] == ‘d’) cnt++; if (input[3] == ‘!’) cnt++; if (cnt >= 3) crash(); } I 0 != ‘b’ I 1 != ‘a’ I 2 != ‘d’ I 3 == ‘!’

8 Depth-First Search godd void top(char input[4]) { int cnt = 0; if (input[0] == ‘b’) cnt++; if (input[1] == ‘a’) cnt++; if (input[2] == ‘d’) cnt++; if (input[3] == ‘!’) cnt++; if (cnt >= 3) crash(); } I 0 != ‘b’ I 1 != ‘a’ I 2 == ‘d’ I 3 != ‘!’ good

9 Key Idea: One Trace, Many Tests Office 2007 application: Time to gather constraints: 25m30s Tainted branches/trace: ~1000 Time per branch to solve, generate new test, check for crashes: ~1s Therefore, solve+check all branches for each trace!

10 Generational Search goo! godd gaod bood “Generation 1” test cases good void top(char input[4]) { int cnt = 0; if (input[0] == ‘b’) cnt++; if (input[1] == ‘a’) cnt++; if (input[2] == ‘d’) cnt++; if (input[3] == ‘!’) cnt++; if (cnt >= 3) crash(); } I 0 == ‘b’ I 1 == ‘a’ I 2 == ‘d’ I 3 == ‘!’

11 The Search Space void top(char input[4]) { int cnt = 0; if (input[0] == ‘b’) cnt++; if (input[1] == ‘a’) cnt++; if (input[2] == ‘d’) cnt++; if (input[3] == ‘!’) cnt++; if (cnt >= 3) crash(); }

12 SAGE Architecture (Scalable Automated Guided Execution) Check for Crashes (AppVerifier)‏ Trace Program (Nirvana)‏ Gather Constraints (Truscan)‏ Solve Constraints (Disolver) Input0Trace File Constraints Input1 Input2 … InputN

13 Since 1 st MS internal release in April’07: dozens of new security bugs found (most missed by blackbox fuzzers, static analysis)‏ Apps: image processors, media players, file decoders,… Confidential ! Many bugs found rated as “security critical, severity 1, priority 1” Now used by several teams regularly as part of QA process Credit is due to the entire SAGE team and users: – CSE: Michael Levin (DevLead), Christopher Marsh, Dennis Jeffries (intern’06), Adam Kiezun (intern’07) Plus Nirvana/iDNA/TruScan contributors. – MSR: Patrice Godefroid, David Molnar (intern’07) (+ constraint solver Disolver)‏ – Plus work of many beta users who found and filed most of these bugs! Initial Experiences with SAGE

14 ANI Parsing - MS RIFF...ACONLIST B...INFOINAM.... 3D Blue Alternat e v1.1..IART anih$...$ rate seq LIST....framic on RIFF...ACONB B...INFOINAM.... 3D Blue Alternat e v1.1..IART anih$...$ rate seq anih....framic on Initial input Crashing test case

15 ANI Parsing - MS RIFF...ACONLIST B...INFOINAM.... 3D Blue Alternat e v1.1..IART anih$...$ rate seq LIST....framic on RIFF...ACONB B...INFOINAM.... 3D Blue Alternat e v1.1..IART anih$...$ rate seq anih....framic on Only 1 in 2 32 chance at random! Initial input Crashing test case

16 Initial Experiments #Instructions and Input size largest seen so far App Tested#TestsMean DepthMean #Instr.Mean Size ANI ,066,0875,400 Media ,409,37665,536 Media ,432,48927,335 Media ,644,65230,833 Media ,685,24022,209 Compression , Office ,731,24845,064

17 Zero to Crash in 10 Generations Starting with 100 zero bytes … SAGE generates a crashing test: h: ; h: ; h: ; h: ; h: ; h: ; h: ;.... Generation 0 – initial input – 100 bytes of “00”

18 Zero to Crash in 10 Generations Starting with 100 zero bytes … SAGE generates a crashing test: h: ; RIFF h: ; h: ; h: ; h: ; h: ; h: ;.... Generation 1

19 Zero to Crash in 10 Generations Starting with 100 zero bytes … SAGE generates a crashing test: h: ** ** ** ; RIFF....*** h: ; h: ; h: ; h: ; h: ; h: ;.... Generation 2

20 Zero to Crash in 10 Generations Starting with 100 zero bytes … SAGE generates a crashing test: h: D ** ** ** ; RIFF=...*** h: ; h: ; h: ; h: ; h: ; h: ;.... Generation 3

21 Zero to Crash in 10 Generations Starting with 100 zero bytes … SAGE generates a crashing test: h: D ** ** ** ; RIFF=...*** h: ; h: ; h: ;....strh h: ; h: ; h: ;.... Generation 4

22 Zero to Crash in 10 Generations Starting with 100 zero bytes … SAGE generates a crashing test: h: D ** ** ** ; RIFF=...*** h: ; h: ; h: ;....strh....vids h: ; h: ; h: ;.... Generation 5

23 Zero to Crash in 10 Generations Starting with 100 zero bytes … SAGE generates a crashing test: h: D ** ** ** ; RIFF=...*** h: ; h: ; h: ;....strh....vids h: ;....strf h: ; h: ;.... Generation 6

24 Zero to Crash in 10 Generations Starting with 100 zero bytes … SAGE generates a crashing test: h: D ** ** ** ; RIFF=...*** h: ; h: ; h: ;....strh....vids h: ;....strf....( h: ; h: ;.... Generation 7

25 Zero to Crash in 10 Generations Starting with 100 zero bytes … SAGE generates a crashing test: h: D ** ** ** ; RIFF=...*** h: ; h: ; h: ;....strh....vids h: ;....strf....( h: C9 9D E4 4E ; ÉäN h: ;.... Generation 8

26 Zero to Crash in 10 Generations Starting with 100 zero bytes … SAGE generates a crashing test: h: D ** ** ** ; RIFF=...*** h: ; h: ; h: ;....strh....vids h: ;....strf....( h: ; h: ;.... Generation 9

27 Zero to Crash in 10 Generations Starting with 100 zero bytes … SAGE generates a crashing test: h: D ** ** ** ; RIFF=...*** h: ; h: ; h: ;....strh....vids h: B A ;....strf²uv:( h: ; h: ;.... Generation 10 – bug ID ! Found after only 3 generations starting from “well-formed” seed file

28 Different Crashes From Different Initial Input Files 100 zero bytes X XXXX XXXXX XX XX XX XXX X XXX X X X Media 1: 60 machine-hours, 44,598 total tests, 357 crashes, 12 bugs Bug ID seed1seed2seed3seed4 seed5seed6seed7

29 Most Bugs Found are “Shallow” Generation

30 Blackbox vs. Whitebox Fuzzing Cost/precision tradeoffs – Blackbox is lightweight, easy and fast, but poor coverage – Whitebox is smarter, but complex and slower – Recent “semi-whitebox” approaches Less smart but more lightweight: Flayer (taint-flow analysis, may generate false alarms), Bunny-the-fuzzer (taint-flow, source- based, heuristics to fuzz based on input usage), autodafe, etc. Which is more effective at finding bugs? It depends… – Many apps are so buggy, any form of fuzzing finds bugs! – Once low-hanging bugs are gone, fuzzing must become smarter: use whitebox and/or user-provided guidance (grammars, etc.)‏ Bottom-line: in practice, use both!

31 Related Work Dynamic test generation (Korel, Gupta-Mathur-Soffa, etc.)‏ – Target specific statement; DART tries to cover “most” code Static Test Generation: hard when symbolic execution imprecise Other “DART implementations” / symbolic execution tools: – EXE/EGT (Stanford): independent [’05-’06] closely related work – CUTE/jCUTE (UIUC/Berkeley): similar to Bell Labs DART implementation – PEX (MSR) applies to.NET binaries in conjunction with “parameterized-unit tests” for unit testing of.NET programs – YOGI (MSR) use DART to check the feasibility of program paths generated statically using a SLAM-like tool – Vigilante (MSR) generate worm filters – BitBlaze/BitScope (CMU/Berkeley) malware analysis, more – Others...

32 Work-In-Progress Catchconv (Molnar-Wagner) – Linux binaries Leverage Valgrind dynamic binary instrumentation – Signed/unsigned conversion, other errors – Current target: Linux media players Four bugs to mplayer developers so far Comparison against zzuf black-box fuzz tester – Use STP to generate new test cases – Code repository on Sourceforge

33 SAGE Summary Symbolic execution scales – SAGE most successful “DART implementation” – Dozens of serious bugs, used regularly at MSFT Existing test suites become security tests Future of fuzz testing?

34 Thank you! Questions?

35 Backup Slides

36 Most bugs are “shallow”

37 Code Coverage and New Bugs

38 Some of the Challenges Path explosion When to stop testing? Gathering initial inputs Processing new test cases – Triage: which are real crashes? – Reporting: prioritize test cases, communicate to developers


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