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Advanced Fuzzing with Peach 2 презентация
Содержание
- 1. Advanced Fuzzing with Peach 2
- 2. Agenda Introduction to Peach 2 Data
- 3. Introduction to Peach 2
- 4. Peach 1 Framework for writing fuzzers
- 5. Peach 2 Reduce creation time and simplify
- 6. Modeling Based Fuzzing Model types and data
- 7. Model Data: Types INT INT INT Flags
- 8. Model Data: Relationships INT INT INT Flags
- 9. Model Data: State Model Packet A Packet
- 10. Benefits of Modeling Easy reuse of definitions
- 11. Data Modeling Define structure of data Define
- 12. State Modeling
- 13. Stream Call TCP, UDP, Files Connect
- 14. State Modeling: Stream State Machine 1 2 3 4 5
- 15. State Modeling: Stream State Machine 1 5
- 16. State Modeling: Stream State Machine 1 2 3 4
- 17. State Modeling: Call State Machine 1 2 3
- 18. Data Mutations
- 19. Mutation: String “?k1=v+1&k2=v2” 40,000+ variations
- 20. Mutation: Number 00
- 21. Mutation: Size Relation #1 Length: Data:
- 22. Mutation: Size Relation #2 Length: Data: 200 Bytes
- 23. Mutation: Size Relation #3 Data & Length:
- 24. Mutation: State Packet A Packet B-1 Packet
- 25. Mutation: State Packet A Packet B-1 Packet D Packet B-2
- 26. Mutation: State Packet A Packet B-1 Packet D Packet B-2
- 27. Add Custom Mutators Sling some Python
- 28. AND DATA COLLECTION Fault Detection
- 29. Agents & Monitors Peach
- 30. 2 Tier Configuration 1 2 3 4 5 6
- 31. Monitors Debuggers Process Monitor Memory Monitor Network
- 32. Peach Development
- 33. Documented XML Schema
- 34. Peach Builder
- 35. Peach Shark
- 36. MASSIVELY PARALLEL FUZZING Peach Farm
- 37. Peach Farm Adam Cecchetti Massively Parallel
- 38. WHAT’S NEXT? Peach in The Middle
- 39. Peach in The Middle Client Server Peach Controller Agent Data Model
- 40. HTTP://PEACHFUZZ.SF.NET HTTP://PHED.ORG MIKE@LEVIATHANSECURITY.COM Q & A
Слайд 4Peach 1
Framework for writing fuzzers
Instrumentation via wrapper APIs
No data definition layer
(DDL), just fuzzer
Steep learning curve
Complex fuzzers result in complex fuzzer code
Steep learning curve
Complex fuzzers result in complex fuzzer code
Слайд 5Peach 2
Reduce creation time and simplify fuzzer generation
Fuzzer platform, not framework
Modeling
based approach
Fault detection
Lower learning curve
Fault detection
Lower learning curve
Слайд 6Modeling Based Fuzzing
Model types and data
Model state machine
Support models with data
sets
Mutate models with mutators
Mutate models with mutators
Слайд 10Benefits of Modeling
Easy reuse of definitions
Complex mutations can be applied to
a model
Improvements to data generation or mutation independent of model
Data read into definition as well as generated
Improvements to data generation or mutation independent of model
Data read into definition as well as generated
Слайд 11Data Modeling
Define structure of data
Define relations in data
Reuse definitions
Block
Sequence
Choice
String
Number
Flags/Flag
Blob
Relation
Transformer
Слайд 13Stream
Call
TCP, UDP, Files
Connect
Accept
Input
Output
Close
COM, RPC, SOAP
Call
Method
Parameters
Result
State Modeling
Слайд 31Monitors
Debuggers
Process Monitor
Memory Monitor
Network Capture
VM Control (snapshot, revert)
Networked Power Strips (cycle power)
Easy
to implement custom monitors
Слайд 37Peach Farm
Adam Cecchetti
Massively Parallel Fuzzing
Scales from 1 to 10,000
nodes
Choose your Virtual Platform/Hosting
EC2, Xen, VMWare, Etc
Utilizes Map/Reduce Algorithm
Map: Maps the fuzzing cases to indexes and results
Reduce: Reduces fuzzing results to interesting cases
Metric based : Time, size, diff, expected errors, OS faults, crashes
Choose your Virtual Platform/Hosting
EC2, Xen, VMWare, Etc
Utilizes Map/Reduce Algorithm
Map: Maps the fuzzing cases to indexes and results
Reduce: Reduces fuzzing results to interesting cases
Metric based : Time, size, diff, expected errors, OS faults, crashes
