Automated Whitebox Fuzz Testing

Automated Whitebox Fuzz Testing

Patrice Godefroid (Microsoft Research)Michael Y. Levin (Microsoft Center for

Software Excellence)David Molnar (UC-Berkeley, visiting MSR)

Presented by Yuyan Bao

Acknowledgement

• This presentation is extended and modified from• The presentation by David Molnar

Automated Whitebox Fuzz Testing• The presentation by Corina Păsăreanu

(Kestrel) Symbolic Execution for Model Checking and

Testing

Agenda• Fuzz Testing• Symbolic Execution• Dynamic Testing Generation• Generational Search• SAGE Architecture• Conclusion• Contribution• Weakness• Improvement

Fuzz Testing• An effective technique for finding security vulnerabilities in software• Apply invalid, unexpected, or random data to the inputs of a program.• If the program fails(crashing, access violation

exception), the defects can be noted.

Whitebox Fuzzing• This paper present an alternative whitebox fuzz testing

approach inspired by recent advances in symbolic execution and dynamic test generation– Run the code with some initial well-formed input– Collect constraints on input with symbolic

execution– Generate new constraints (by negating constraints

one by one)– Solve constraints with constraint solver– Synthesize new inputs

Symbolic Execution• A method for deriving test cases which satisfy a given path• Performed as a simulation of the computation on the path• Initial path function = Identity function, Initial path condition = true• Each vertex on the path induce a symbolic composition on the path

function and a logical constraint on the path condition:If an assignment was made:

If a conditional decision was made:path condition path condition branch condition

Output: path function and path condition for the given path

White Box Testing and Symbolic Execution 6

f g f

( )X g X

[ ( )]X f X

Slide from “White Box Testing and Symbolic Execution” by Michael Beder

Symbolic Execution

7

Code:

(PC=“path condition”)

- “Simulate” the code using symbolic values instead of program numeric data

Symbolic execution tree: x:X,y:YPC: true

x:X,y:YPC: X>Y

x:X,y:YPC: X<=Y

true false

x:X+Y,y:YPC: X>Y

x:X+Y,y:XPC: X>Y

x:Y,y:XPC: X>Y

x:Y,y:XPC: X>Y Y-X>0

x:Y,y:XPC:X>Y Y-X<=0

true false

FALSE!

Not reachable

int x, y; if (x > y) { x = x + y; y = x - y; x = x - y; if (x – y > 0) assert (false); }

Dynamic Test Generation

• Executing the program starting with some initial inputs

• Performing a dynamic symbolic execution to collect constraints on inputs

• Use a constraint solver to infer variants of the previous inputs in order to steer the next executions

Dynamic Test Generationvoid 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”

• Running the program with random values for the 4 input bytes is unlikely to discovery the error: a probability of about

• Traditional fuzz testing is difficult to generate input values that will drive program through all its possible execution paths.

301/ 2

Depth-First Search

void top(char input[4])

{

int cnt = 0;




if (input[3] == ‘!’) cnt++;


}

I0 != ‘b’I1 != ‘a’I2 != ‘d’I3 != ‘!’good

I0 != ‘b’

I1 != ‘a’

I2 != ‘d’

I3 != ‘!’

Depth-First Search

goo!good


{

int cnt = 0;




if (input[3] == ‘!’) cnt++;


}

I0 != ‘b’I1 != ‘a’I2 != ‘d’I3 == ‘!’

Practical limitation

godd


{

int cnt = 0;




if (input[3] == ‘!’) cnt++;


}

I0 != ‘b’I1 != ‘a’I2 == ‘d’I3 != ‘!’good

Every time only one constraint is expanded, low efficiency!

Generational search

• BFS with a heuristic to maximize block coverage• Score returns the number of new blocks covered

14Slide from “Symbolic Execution” by Kevin Wallace, CSE504 2010-04-28

Generational SearchKey Idea: One Trace, Many Tests

goo!

godd

gaod

bood

“Generation 1” test cases

good


{

int cnt = 0;




if (input[3] == ‘!’) cnt++;


}

I0 == ‘b’I1 == ‘a’I2 == ‘d’I3 == ‘!’


{

int cnt = 0;




if (input[3] == ‘!’) cnt++;


}

0good

1goo!

1godd

2god!

1gaod

2gao!

2gadd

gad!

1bood

2boo!

2bodd

2baod

bod! bad!

badd baod

The Generational Search Space

SAGE Architecture(Scalable Automated Guided Execution)

Task: TesterCheck forCrashes

(AppVerifier)

Task: TracerTrace

Program(Nirvana)

Task: CoverageCollector

Gather Constraints(Truscan)

Task: SymbolicExecutor

SolveConstraints(Disolver)

Input0 Trace File Constraints

Input1

Input2…

InputN

• Since 1st 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,…

• Many bugs found rated as “security critical, severity 1, priority 1”

• Now used by several teams regularly as part of QA process

Initial Experiences with SAGE

Experiments ANI Parsing - MS07-017

RIFF...ACONLISTB...INFOINAM....3D Blue Alternate v1.1..IART....................1996..anih$...$...................................rate....................seq ....................LIST....framicon......... ..

RIFF...ACONBB...INFOINAM....3D Blue Alternate v1.1..IART....................1996..anih$...$...................................rate....................seq ....................anih....framicon......... ..

Only 1 in 232 chance at random!

Initial input Crashing test case

Initial input : 341 branch constraints, 1,279,939 total instructions. Crash test cases: 7706 test cases, 7hrs 36 mins

Different Crashes From Different Initial Input Files 100

zerobytes

1867196225 X X X X X

2031962117 X X X X X

612334691 X X

1061959981 X X

1212954973 X X

1011628381 X X X

842674295 X

1246509355 X X X

1527393075 X

1277839407 X

1951025690 X

Media 1: 60 machine-hours, 44,598 total tests, 357 crashes, 12 bugs

Bug ID seed1 seed2 seed3 seed4 seed5 seed6 seed7

Conclusion 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!

Contribution

• A novel search algorithm with a coverage-maximizing heuristic

• It performs symbolic execution of program traces at the x86 binary level

• Tests larger applications than previously done in dynamic test generation

Weakness

• Gathering initial inputs• The class of inputs characterized by each symbolic

execution is determined by the dependence of the program’s control flow on its inputs

• Not a general solution• Only for x86 windows applications• Only focus on file-reading applications

• Nirvana simulates a given processor’s architecture

Improvement

• Portability• Translate collected traces into an

intermediate language• Reproduce bugs on different platforms

without re-simulating the program• Better way to find initial inputs efficiently

Thank you!Questions?

Automated Whitebox Fuzz Testing

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