Applications of SMT to. Test Generation

Transcription

1 Applications of SMT to Test Generation Patrice Godefroid Microsoft Research Page 1 June 2012

2 Test Generation is Big Business #1 application for SMT solvers today (CPU usage) Microsoft: 1 st whitebox fuzzer for security testing 400+ machine years (since 2008) 3.4+ Billion constraints 100s of apps, 100s of security bugs Example: Win7 file fuzzing ~1/3 of all fuzzing bugs found by SAGE (missed by everything else ) Bug fixes shipped (quietly) to 1 Billion+ PCs Millions of dollars saved for Microsoft + time/energy for the world How fuzzing bugs were found (Win7, ) : Blackbox Fuzzing All Others + Regression SAGE Page 2 June 2012

3 Agenda 1. Why Test Generation? 2. What kind of SMT constraints? 3. New: test generation from validity proofs Disclaimer: here, focus on test generation for software using SMT (not hardware using SAT) Page 3 June 2012

4 Part 1: Why Test Generation? Whitebox Fuzzing for Security Testing (The Killer App) Page 4 June 2012

5 Security is Critical (to Microsoft) Software security bugs can be very expensive: Cost of each Microsoft Security Bulletin: $Millions Cost due to worms (Slammer, CodeRed, Blaster, etc.): $Billions Many security exploits are initiated via files or packets Ex: MS Windows includes parsers for hundreds of file formats Security testing: hunting for million-dollar bugs Write A/V (always exploitable), Read A/V (sometimes exploitable), NULL-pointer dereference, division-by-zero (harder to exploit but still DOS attacks), etc. Page 5 June 2012

6 Hunting for Security Bugs I am from Belgium too! Main techniques used by black hats : Code inspection (of binaries) and Blackbox fuzz testing Blackbox fuzz testing: A form of blackbox random testing [Miller+90] Randomly fuzz (=modify) a well-formed input Grammar-based fuzzing: rules that encode well-formed ness + heuristics about how to fuzz (e.g., using probabilistic weights) Heavily used in security testing Simple yet effective: many bugs found this way At Microsoft, fuzzing is mandated by the SDL Page 6 June 2012

7 Introducing Whitebox Fuzzing Idea: mix fuzz testing with dynamic test generation Dynamic symbolic execution Collect constraints on inputs Negate those, solve with constraint solver, generate new inputs do systematic dynamic test generation (=DART) Whitebox Fuzzing = DART meets Fuzz Two Parts: 1. Foundation: DART (Directed Automated Random Testing) 2. Key extensions ( Whitebox Fuzzing ), implemented in SAGE Page 7 June 2012

8 Automatic Code-Driven Test Generation Problem: Given a sequential program with a set of input parameters, generate a set of inputs that maximizes code coverage = automate test generation using program analysis This is not model-based testing (= generate tests from an FSM spec) Page 8 June 2012

9 How? (1) Static Test Generation Static analysis to partition the program s input space [King76, ] Ineffective whenever symbolic reasoning is not possible which is frequent in practice (pointer manipulations, complex arithmetic, calls to complex OS or library functions, etc.) Example: int obscure(int x, int y) { } if (x==hash(y)) error(); return 0; Can t statically generate values for x and y that satisfy x==hash(y)! Page 9 June 2012

10 How? (2) Dynamic Test Generation Run the program (starting with some random inputs), gather constraints on inputs at conditional statements, use a constraint solver to generate new test inputs Repeat until a specific program statement is reached [Korel90, ] Or repeat to try to cover ALL feasible program paths: DART = Directed Automated Random Testing = systematic dynamic test generation [PLDI 05, ] detect crashes, assertion violations, use runtime checkers (Purify, ) Page 10 June 2012

11 DART = Directed Automated Random Testing Example: Run 1 : - start with (random) x=33, y=42 int obscure(int x, int y) { - execute concretely and symbolically: if (33!= 567) if (x!= hash(y)) if (x==hash(y)) error(); constraint too complex return 0; simplify it: x!= 567 } - solve: x==567 solution: x=567 - new test input: x=567, y=42 Observations: Run 2 : the other branch is executed All program paths are now covered! Dynamic test generation extends static test generation with additional runtime information: it is more powerful see [DART in PLDI 05], [PLDI 11] The number of program paths can be infinite: may not terminate! Still, DART works well for small programs (1,000s LOC) Significantly improves code coverage vs. random testing Page 11 June 2012

12 DART Implementations Defined by symbolic execution, constraint generation and solving Languages: C, Java, x86,.net, Theories: linear arith., bit-vectors, arrays, uninterpreted functions, Solvers: lp_solve, CVCLite, STP, Disolver, Z3, Examples of tools/systems implementing DART: EXE/EGT (Stanford): independent [ 05-06] closely related work CUTE = same as first DART implementation done at Bell Labs SAGE (CSE/MSR) for x86 binaries and merges it with fuzz testing for finding security bugs (more later) PEX (MSR) for.net binaries in conjunction with parameterized-unit tests for unit testing of.net programs YOGI (MSR) for checking the feasibility of program paths generated statically using a SLAM-like tool Vigilante (MSR) for generating worm filters BitScope (CMU/Berkeley) for malware analysis CatchConv (Berkeley) focus on integer overflows Splat (UCLA) focus on fast detection of buffer overflows Apollo (MIT/IBM) for testing web applications and more! Page 12 June 2012

13 Whitebox Fuzzing [NDSS 08] Whitebox Fuzzing = DART meets Fuzz Apply DART to large applications (not unit) Start with a well-formed input (not random) Combine with a generational search (not DFS) Negate 1-by-1 each constraint in a path constraint Generate many children for each parent run Challenge all the layers of the application sooner Leverage expensive symbolic execution Search spaces are huge, the search is partial yet effective at finding bugs! parent Gen 1 Page 13 June 2012

14 Example void top(char input[4]) input = good { int cnt = 0; Path constraint: if (input[0] == b ) cnt++; I 0!= b I 0 = b bood if (input[1] == a ) cnt++; I 1!= a I 1 = a gaod if (input[2] == d ) cnt++; I 2!= d I 2 = d godd } if (input[3] ==! ) cnt++; if (cnt >= 4) crash(); I 3!=! I 3 =! SMT solver Negate each constraint in path constraint Solve new constraint new input good SAT goo! Gen 1 Page 14 June 2012

15 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 >= 4) crash(); } If symbolic execution is perfect and search space is small, this is verification! Page 15 June 2012

16 SAGE (Scalable Automated Guided Execution) Generational search introduced in SAGE Performs symbolic execution of x86 execution traces Builds on Nirvana, idna and TruScan for x86 analysis Don t care about language or build process Easy to test new applications, no interference possible Can analyse any file-reading Windows applications Several optimizations to handle huge execution traces Constraint caching and common subexpression elimination Unrelated constraint optimization Constraint subsumption for constraints from input-bound loops Flip-count limit (to prevent endless loop expansions) Page 16 June 2012

17 SAGE Architecture Input0 Coverage Data Constraints Check for Crashes (AppVerifier) Code Coverage (Nirvana) Generate Constraints (TruScan) Solve Constraints (Z3) Input1 Input2 SAGE was mostly developed by CSE ( ) InputN MSR algorithms & code inside ( ) Page 17 June 2012

18 Some Experiments Most much (100x) bigger than ever tried before! Seven applications 10 hours search each App Tested #Tests Mean Depth Mean #Instr. Mean Input Size ANI ,066,087 5,400 Media ,409,376 65,536 Media ,432,489 27,335 Media ,644,652 30,833 Media ,685,240 22,209 Compressed File Format , OfficeApp ,731,248 45,064 Page 18 June 2012

19 Generational Search Leverages Symbolic Execution Each symbolic execution is expensive m30s SymbolicExecutor TestTask X1,000 Yet, symbolic execution does not dominate search time SymbolicExecutor 10 hours Testing/Tracing/Coverage Page 19 June 2012

20 SAGE Results Since April 07 1 st release: many new security bugs found (missed by blackbox fuzzers, static analysis) Apps: image processors, media players, file decoders, Bugs: Write A/Vs, Read A/Vs, Crashes, Many triaged as security critical, severity 1, priority 1 (would trigger Microsoft security bulletin if known outside MS) Example: WEX Security team for Win7 Dedicated fuzzing lab with 100s machines 100s apps (deployed on 1billion+ computers) ~1/3 of all fuzzing bugs found by SAGE! SAGE = gold medal at Fuzzing Olympics organized by SWI at BlueHat 08 (Oct 08) Credit due to entire SAGE team + users! Page 20 June 2012

21 WEX Fuzzing Lab Bug Yield for Win7 How fuzzing bugs found ( ) : 100s of apps, total number of fuzzing bugs is confidential Default Blackbox Fuzzer + Regression All Others SAGE SAGE is running 24/7 on 100s machines: the largest usage ever of any SMT solver N. Bjorner + L. de Moura (MSR, Z3 authors) But SAGE didn t exist in 2006 Since 2007 (SAGE 1 st release), ~1/3 bugs found by SAGE But SAGE was then deployed on only ~2/3 of those apps Normalizing the data by 2/3, SAGE found ~1/2 bugs SAGE was run last in the lab, so all SAGE bugs were missed by everything else! Page 21 June 2012

22 SAGE Summary SAGE is so effective at finding bugs that, for the first time, we face bug triage issues with dynamic test generation What makes it so effective? Works on large applications (not unit test, like DART, EXE, etc.) Can detect bugs due to problems across components Fully automated (focus on file fuzzing) Easy to deploy (x86 analysis any language or build process!) 1 st tool for whole-program dynamic symbolic execution at x86 level Now, used daily in various groups at Microsoft Page 22 June 2012

23 Part 2: What kind of SMT constraints? (Tests from Satisfiability Models) Page 23 June 2012

24 Types of Constraints Roughly speaking: Long conjunctions! Bit vector constraints Bounded arrays (for pointer reasoning) No unbounded quantifiers See (anonimysed) SAGE benchmarks part of SMT comp (for more, contact Nikolaj Bjorner or Leonardo de Moura) Sometimes: Axiomatization for type systems (e.g., PEX) String constraints, etc. (e.g., HAMPI) uninterpreted functions (for summaries) Page 24 June 2012

25 The Art of Constraint Generation More On the Research Behind SAGE : How to recover from imprecision in symbolic exec.? PLDI 05, PLDI 11 Must under-approximations How to scale symbolic exec. to billions of instructions? NDSS 08 Techniques to deal with large path constraints How to check efficiently many properties together? EMSOFT 08 Active property checking How to leverage grammars for complex input formats? PLDI 08 Lift input constraints to the level of symbolic terminals in an input grammar How to deal with path explosion? POPL 07, TACAS 08, POPL 10, SAS 11 Symbolic test summaries (more later) How to reason precisely about pointers? ISSTA 09 New memory models leveraging concrete memory addresses and regions How to deal with floating-point instructions? ISSTA 10 Prove non-interference with memory accesses How to deal with input-dependent loops? ISSTA 11 Automatic dynamic loop-invariant generation and summarization Page 25 June 2012

26 SAGAN: Fuzzing in the (Virtual) Cloud Since June 2010, new centralized server collecting stats from all SAGE runs! 200+ machine-years of SAGE data (since June 2010) Track results (bugs, concrete & symbolic test coverage), incompleteness (unhandled tainted x86 instructions, Z3 timeouts, divergences, etc.) Help troubleshooting (SAGE has 100+ options ) Tell us what works and what does not Page 26 June 2012

27 Picking and Choosing Typical SAGE run can fill up 300 GB in 1 week Problem: 100s of machines * 300+ GB = lots of data Solution: pick and choose what to ship up Configuration files Counters from each SAGE execution Run heartbeats at random intervals Crashing test information Key principles Enough information to repro SAGE run results Support key analyses for improving SAGE Page 27 June 2012

28 Are we hitting known problems in symbolic execution? Page 28 June 2012

29 Incompleteness 100s of instructions in x86 Some of them we handle perfectly Some we do not handle perfectly and we know it Which instructions should we prioritize? SAGAN tells us! Page 29 June 2012

30 How to Automate These Steps? Automated Synthesis of Symbolic Instruction Encodings from I/O Samples (to appear at [PLDI 2012]) : 6 abstract instruction templates building blocks are bit-vector constraints (SMT-lib format) for 534 x86 ALU instructions (8/16/32bits, outputs, EFLAGS) new smart sampling synthesis algorithm takes <2 hours with Z3 synthesis against specific x86 processor as I/O oracle : Page 30 June 2012

31 How long do we spend in constraint generation and constraint solving? Page 31 June 2012

32 Most solver queries are fast Page 32 June 2012

33 but solving time often dominates Page 33 June 2012

34 We can cut off outlying tasks! Solver time Page 34 June 2012

35 Most Constraints are UNSAT UNSAT / timeout SAT Page 35 June 2012

36 Why are these constraints (usually) fast to solve? 90.18% of Z3 queries solved in 0.1 seconds or less! Page 36 June 2012

37 Key Optimizations Sound Common subexpression elimination on every new constraint Crucial for memory usage Related Constraint Optimization Unsound Constraint subsumption Syntactic check for implication, take strongest constraint Drop constraints at same instruction pointer after threshold This is the art of constraint generation! So that the constraint solver is not the bottleneck Predictability is key to good business! Nobody wants an NP-hard problem in the way! Page 37 June 2012

38 What Next? Towards Verification When can we safely stop testing? When we know that there are no more bugs! = Verification Testing can only prove the existence of bugs, not their absence. Unless it is exhaustive! This is the model checking thesis Model Checking = exhaustive testing (state-space exploration) Two main approaches to software model checking: Modeling languages state-space exploration Model checking [Dijkstra] abstraction (SLAM, Bandera, FeaVer, BLAST, ) adaptation Programming languages state-space exploration Systematic testing Concurrency: VeriSoft, JPF, CMC, Bogor, CHESS, Data inputs: DART, EXE, SAGE, Page 38 June 2012

39 Exhaustive Testing? Model checking is always up to some bound Limited (often finite) input domain, for specific properties, under some environment assumptions Ex: exhaustive testing of Win7 JPEG parser up to 1,000 input bytes 8000 bits 2^8000 possibilities if 1 test per sec, 2^8000 secs FYI, 15 billion years = secs = 2^60 secs! MUST be symbolic! How far can we go? Practical goals: (easier?) Eradicate all remaining buffer overflows in all Windows parsers Reduce costs & risks for Microsoft: when to stop fuzzing? Increase costs & risks for Black Hats! Many have probably moved to greener pastures already (Ex: Adobe) Ex: <5 security bulletins in all the SAGE-cleaned Win7 parsers If noone can find bugs in P, P is observationally equivalent to verified! Page 39 June 2012

40 How to Get There? 1. Identify and patch holes in symbolic execution + constraint solving 2. Tackle path explosion with compositional testing and symbolic test summaries [POPL 07,TACAS 08,POPL 10] Page 40 June 2012

41 The Art of Constraint Generation Static analysis: abstract away irrelevant details Good for focused search, can be combined with DART (Ex: [POPL 10]) But for bit-precise analysis of low-level code (function pointers, in-lined assembly,...)? In a non-property-guided setting? Open problem Bit-precise VC-gen: statically generate 1 formula from a program Good to prove complex properties of small programs (units) Does not scale (huge formula encodings), asks too much of the user SAT/SMT-based Bounded Model Checking : stripped-down VC-gen Emphasis on automation Unrolling all loops is naïve, does not scale DART : the only option today for large programs (Ex: Excel) Path-by-path exploration is slow, but whitebox fuzzing can scale it to large executions + zero false alarms! But suffers from path explosion Page 41 June 2012

42 DART is Beautiful Generates formulas where the only free symbolic variables are whole-program inputs When generating tests, one can only control inputs! Strength: scalability to large programs Only tracks direct input dependencies (i.e., tests on inputs); the rest of the execution is handled with the best constantpropagation engine ever: running the code on the computer! (The size of) path constraints only depend on (the number of) program tests on inputs, not on the size of the program = the right metric: complexity only depends on nondeterminism! Price to pay: path explosion [POPL 07] Solution = symbolic test summaries Page 42 June 2012

43 Example void top(char input[4]) { int cnt = 0; input = good Path constraint: if (input[0] == b ) cnt++; if (input[1] == a ) cnt++; if (input[2] == d ) cnt++; if (input[3] ==! ) cnt++; I 0!= b I 1!= a I 2!= d I 3!=! if (cnt >= 3) crash(); } Page 43 June 2012

44 Compositionality = Key to Scalability Idea: compositional dynamic test generation [POPL 07] use summaries of individual functions (or program blocks, etc.) like in interprocedural static analysis but here must formulas generated dynamically If f calls g, test g, summarize the results, and use g s summary when testing f A summary φ(g) is a disjunction of path constraints expressed in terms of g s input preconditions and g s output postconditions: φ(g) = φ(w) with φ(w) = pre(w) post(w) g s outputs are treated as fresh symbolic inputs to f, all bound to prior inputs and can be eliminated (for test generation) Can provide same path coverage exponentially faster! See details and refinements in [POPL 07,TACAS 08,POPL 10] Page 44 June 2012

45 SAGAN tracks branch flipped Summarize this! Sampled runs on Windows, many different file-reading applications Max frequency 17761, min frequency 592 Total of branches flipped, 3360 distinct branches Page 45 June 2012

46 Summaries Cure Search Redundancy Across different program paths IF THEN ELSE Across different program versions Incremental Compositional Dynamic Test Generation [SAS 11] Across different applications Summaries avoid unnecessary work What if central server of summaries for all code?... Page 46 June 2012

47 The Engineering of Test Summaries Systematically summarizing everywhere is foolish Very expensive and not necessary (costs outweigh benefits) Not scalable without user help (see work on VC-gen and BMC) Summarization on-demand: (100% algorithmic) When? At search bottlenecks (with dynamic feedback loop) Where? At simple interfaces (with simple data types) How? With limited side-effects (to be manageable and sound ) Goal: use summaries intelligently THE KEY to scalable bit-precise whole-program analysis? Necessary, but sufficient? In what form(s)? Computed statically? [POPL 10, ISSTA 10] Stay tuned Page 47 June 2012

48 Part 3: Tests from Validity Proofs (Higher-Order Test Generation) [PLDI 2011] Page 48 June 2012

49 Why Dynamic Test Gen.? Most Precise! Example: Run 1 : - start with (random) x=33, y=42 int obscure(int x, int y) { - execute concretely and symbolically: if (33!= 567) if (x!= hash(y)) if (x==hash(y)) error(); constraint too complex return 0; simplify it: x!= 567 } - solve: x==567 solution: x=567 - new test input: x=567, y=42 Observations: Run 2 : the other branch is executed All program paths are now covered! Unknown/complex symbolic expressions can be simplified using concrete runtime values [DART, PLDI 05] Let s call this step concretization (ex: hash(y) 567) Dynamic test generation extends static test generation with additional runtime information: it is more powerful How often? When exactly? Why? this work! Page 49 June 2012

50 Unsound and Sound Concretization Concretization is not always sound int foo(int x, int y) { } if (x==hash(y)) { } if (y==10) error(); Run: x=567, y=42 pc: x==567 and y!=10 New pc: x==567 and y==10 New inputs: x=567, y=10 Divergence! pc and new pc are unsound! Definition: A path constraint pc for a path w is sound if every input satisfying pc defines an execution following w Sound concretization: add concretization constraints pc: y==42 and x==567 and y!=10 (sound) New pc: y==42 and x==567 and y==10 (sound) Theorem: path constraint is now always sound. Is this better? No Forces us to detect all sources of imprecision (expensive/impossible ) Can prevent test generation and good divergences Page 50 June 2012

51 Idea: Using Uninterpreted Functions Modeling imprecision with uninterpreted functions int obscure(int x, int y) { Run: x=33, y=42 if (x==hash(y)) error(); return 0; pc: x!= h(y) } New pc: x == h(y) How to generate tests? Is ( x,y,h:) x=h(y) SAT? Yes, but so what? (ex: x=y=0, h(0)=0) Need universal quantification! ( h:) x,y: x=h(y) is this first-order logic formula valid? Yes. Solution (strategy): fix y, set x to the value of h(y) Test generation from validity proofs! (not SAT models) Necessary but not sufficient: what value of h(y)? Page 51 June 2012

52 Need for Uninterpreted Function Samples Record I/O UF samples int obscure(int x, int y) { if (x==hash(y)) error(); return 0; } Run: x=33, y=42 Record: 567 == h(42) pc: x!=h(y) Use UF samples to interpret a validity proof/strategy fix y, set x to the value of h(y) set y=42, x=567 Or new pc: ( h:) x,y: (567=h(42)) => (x=h(y)) is valid? Higher-order test generation = models imprecision using Uninterpreted Functions records UF samples as concrete input/output value pairs generates tests from validity proofs of FOL formulas Key: a higher-order logic representation of path constraints Page 52 June 2012

53 Higher-Order Test Generation is Powerful Theorem: HOTG is as powerful as sound concretization Can simulate it (both UFs and UF samples are needed for this) Higher-Order Test Generation is more powerful Ex 1: ( h:) x,y: h(x)=h(y) Ex 2: ( h:) x,y: h(x)=h(y)+1 is valid (solution: set x=y) is invalid But ( h:) x,y: (h(0)=0 h(1)=1) => h(x)=h(y)+1 Ex 3: (solution: set x=1, y=0) int foo(int x, int y) { } if (x==hash(y)) { } if (y==10) error(); is valid Run: x=567, y=42 pc: x==h(y) and y!=10 New pc: ( h:) x,y: (h(42)=567) => x=h(y) y=10 is valid. Solution: set y=10, set x=h(10) 2-step test generation: - run1 with y=10, x=567 to learn h(10) =66 - run2 with y=10, x=66! Page 53 June 2012

54 Implementability Issues Tracking all sources of imprecision is problematic Excel on a 45K input bytes executes 1 billion x86 instructions Imprecision cannot always be represented by UFs Unknown input/output signatures, nondeterminism, Capturing all input/output pairs can be very expensive Limited support from current SMT solvers X:Ф(F,X) is valid iff X: Ф(F,X) is UNSAT little support for generating+parsing UNSAT saturation proofs In practice, HOTG can be used for targeted reasoning about specific user-defined complex/unknown functions Page 54 June 2012

55 Application: Lexers with Hash Functions Parsers with input lexers using hash functions for fast keyword recognition Initially, forall language keywords: addsym(keyword, hashtable) When parsing the input: X=findsym(inputChunk, hashtable); // is inputchunk in hashtable? if (x==52) // how to get here? With higher-order test generation: Represent hashfunct by one UF h Capture all pairs (hashvalue,h(keyword)) If h(inputchunk)==52 and (52,h( while )) -> inputchunk= while This effectively inverses hashfunct only for all keywords Sufficient to drive executions through the lexer! See [PLDI 2011] for details Page 55 June 2012

56 Other Related Work Modeling imprecision with UFs is well-known in program verification of universal properties may abstractions, universal quantifiers only, validity checks Here, novelty is for existential properties Test generation is only one way to verify existential properties of programs More generally, one can build must abstractions Alternation is also used then Test generation as a game is not new in model-based testing, testing for reactive systems, etc. but from validity proofs of FOL formulas with UFs is new Page 56 June 2012

57 Summary (see [PLDI 2011]) Higher-order test generation = UFs + UF samples + test generation from validity checks of FOL formulas A new powerful form of test generation Tracking all sources of incompleteness is unrealistic, targeted use of UFs is more practical (ex: lexer with h) A formal tool to define the limits of test generation Explains in what sense dynamic test generation is more powerful than static test generation only in its ability to record concrete values in path constraints concrete values are ultimately needed for test generation Page 57 June 2012

58 General Summary 1. Why Test Generation? 2. What kind of SMT constraints? 3. New: test generation from validity proofs Page 58 June 2012

59 Conclusion: Impact of SAGE (In Numbers) 400+ machine-years Runs in the largest dedicated fuzzing lab in the world 3.4 Billion+ constraints Largest computational usage ever for any SMT solver 100s of apps, 100s of bugs (missed by everything else) Bug fixes shipped quietly (no MSRCs) to 1 Billion+ PCs Millions of dollars saved for Microsoft + time/energy savings for the world DART, Whitebox fuzzing now adopted by (many) others (10s tools, 100s citations) Page 59 June 2012

60 Conclusion: Blackbox vs. Whitebox Fuzzing Different cost/precision tradeoffs Blackbox is lightweight, easy and fast, but poor coverage Whitebox is smarter, but complex and slower Note: other recent semi-whitebox approaches Less smart (no symbolic exec, constr. solving) but more lightweight: Bunny-the-fuzzer (taint-flow, source-based, fuzz heuristics from input usage), Flayer (fault injection, not necessarily feasible), etc. Which is more effective at finding bugs? It depends Many apps are so buggy, any form of fuzzing find bugs in those! 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! (We do at Microsoft) Page 60 June 2012

61 What Next? Towards Verification Tracking all(?) sources of incompleteness Possibly using UFs and Higher-Order Test Generation Summaries (on-demand ) against path explosion How far can we go? Reduce costs & risks for Microsoft: when to stop fuzzing? Increase costs & risks for Black Hats (goal already achieved?) For history books!? Blackbox Fuzzing Whitebox Fuzzing Verification Page 61 June 2012

62 Acknowledgments SAGE is joint work with: MSR: Ella Bounimova, David Molnar, CSE: Michael Levin, Chris Marsh, Lei Fang, Stuart de Jong, Interns Dennis Jeffries (06), David Molnar (07), Adam Kiezun (07), Bassem Elkarablieh (08), Marius Nita (08), Cindy Rubio-Gonzalez (08,09), Johannes Kinder (09), Daniel Luchaup (10), Nathan Rittenhouse (10), Mehdi Bouaziz (11), Ankur Taly (11) Thanks to the entire SAGE team and users! Z3 (MSR): Nikolaj Bjorner, Leonardo de Moura, Windows: Nick Bartmon, Eric Douglas, Dustin Duran, Elmar Langholz, Isaac Sheldon, Dave Weston, TruScan support: Evan Tice, David Grant, Office: Tom Gallagher, Eric Jarvi, Octavian Timofte, SAGE users all across Microsoft! References: see Page 62 June 2012