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FUZZIFICATION: Anti-Fuzzing Techniques

Jinho Jung, Hong Hu, David Solodukhin, Daniel Pagan,

Kyu Hyung Lee*, Taesoo Kim

*

1

Fuzzing Discovers Many Vulnerabilities

2

Fuzzing Discovers Many Vulnerabilities

3

Testers Find Bugs with Fuzzing

Detected

bugs
Normal users

Compilation
Released binary

Source

Testers

  • Distribution
  • Fuzzing
  • Compilation

4

But Attackers Also Find Bugs

Detected

bugs
Normal users

Compilation
Attackers

Testers
Released binary

Source

  • Distribution
  • Fuzzing
  • Compilation

5

Our work: Make the Fuzzing Only Effective to the Testers

Detected bugs

Normal users

?

Fuzzification

Fortified

binary
Attackers

Testers

Source
Compilation

Compilation

Binary

  • Distribution
  • Fuzzing

6

Threat Model

Detected bugs

Normal users

Attackers

Fuzzification

Fortified binary

Source
Compilation

Compilation

Testers

Binary

  • Distribution
  • Fuzzing

7

Threat Model

Detected bugs

Normal users

Attackers

Fuzzification

Fortified binary

Source
Compilation

Compilation

Testers

Binary

  • Distribution
  • Fuzzing

Adversaries try to find vulnerabilities from fuzzing

8

Threat Model

Detected bugs

Normal users

Attackers

Fuzzification

Fortified binary

Source
Compilation

Compilation

Testers

Binary

  • Distribution
  • Fuzzing

Adversaries only have a copy of fortified binary

9

Threat Model

Detected bugs

Normal users

Attackers

Fuzzification

Fortified binary

Source
Compilation

Compilation

Testers

Binary

  • Distribution
  • Fuzzing

Adversaries know Fuzzification and try to nullify

10

Research Goals

Detected bugs

Normal users

Attackers

Fuzzification

Fortified binary

Source
Compilation

Testers

Binary

  • Distribution
  • Fuzzing
  • Compilation

11

Research Goals

Detected bugs

Normal users

Attackers

Fuzzification

Fortified binary

Source
Compilation

Testers

Binary

  • Distribution
  • Fuzzing
  • Compilation

Hinder Fuzzing Reduce the number of detected bugs

12

Research Goals

Detected bugs

Normal users

Attackers

AFL

HonggFuzz

QSym

Fuzzification

Fortified binary

VUzzer

…

Source
Compilation

  • Testers
  • Binary

  • Compilation
  • Fuzzing
  • Distribution

Generic Affect most of the fuzzers

13

Research Goals

Detected bugs

Normal users

Attackers

Fuzzification

Fortified binary

Source
Compilation

Testers

Binary

  • Distribution
  • Fuzzing
  • Compilation

Low overhead to normal user

High overhead to attackers

Overhead

14

Research Goals

Detected bugs

Normal users

Attackers

Fuzzification

Fortified binary

Source
Compilation

Testers

Binary

  • Distribution
  • Fuzzing
  • Compilation

Resilient to the adversarial analysis

Resiliency

15

Why Existing Methods Are Not Applicable?

Generic to most fuzzers overhead adversary

  • Low
  • Resilient to

Method

  • Packing or obfuscation
  • O
  • X
  • O

16

Why Existing Methods Are Not Applicable?

Generic to most fuzzers overhead adversary

  • Low
  • Resilient to

Method

Packing or obfuscation

Bug injection

O

O

  • X
  • O

  • X
  • O

17

Why Existing Methods Are Not Applicable?

Generic to most fuzzers overhead adversary

  • Low
  • Resilient to

Method

Packing or obfuscation

Bug injection

O

O

X
X

O

O
O

X

  • X
  • Fuzzer detection

18

Why Existing Methods Are Not Applicable?

Generic to most fuzzers overhead adversary

  • Low
  • Resilient to

Method

Packing or obfuscation

Bug injection

O

O

X

X

X

O

O

O

O

X

X

X

Fuzzer detection

Emulator detection

19

Why Existing Methods Are Not Applicable?

Generic to most fuzzers overhead adversary

  • Low
  • Resilient to

Method

Packing or obfuscation

Bug injection

O

O

X
X

O

O

O

O
O

X

X

X

O
Fuzzer detection

Emulator detection

Fuzzification

X

O

20

Fuzzification Hinders Advanced Features

• Fast execution

Parallel execution
H/W feature

• Coverage-guidance

Fork server

• Hybrid approach

21

Fuzzification Hinders Advanced Features

SpeedBump

• Fast execution

Parallel execution
H/W feature

• Coverage-guidance

Fork server

• Hybrid approach

22

Fuzzification Hinders Advanced Features

Coverage

• Fast execution

Parallel execution
H/W feature

• Coverage-guidance

Fork server

• Hybrid approach

23

Fuzzification Hinders Advanced Features

Coverage

• Fast execution

Parallel execution
H/W feature

• Coverage-guidance

BranchTrap

Fork server

• Hybrid approach

24

Fuzzification Hinders Advanced Features

Coverage

• Fast execution

Parallel execution
H/W feature

• Coverage-guidance

Queue

Fork server
Symbolic

execution

• Hybrid approach

Dynamic

taint

analysis

25

Fuzzification Hinders Advanced Features

Coverage

• Fast execution

Parallel execution
H/W feature

• Coverage-guidance

Anti-Hybrid

Queue

Fork server
Symbolic

execution

• Hybrid approach

Dynamic

taint

analysis

26

SpeedBump: Selective Delay Injection

Basic block

27

SpeedBump: Selective Delay Injection

• Identify frequently and

rarely visited paths

Basic block Rarely visited path Frequently visited path

28

SpeedBump: Selective Delay Injection

• Identify frequently and

rarely visited paths

1

• Inject delays from the most

rarely visited edges

2

Basic block Rarely visited path Frequently visited path

29

SpeedBump: Selective Delay Injection

• Why this is effective?

▫ User: follows common paths ▫ Attacker: searches for new paths

1

➔ Impact of delay is more

significant to attackers

2

Basic block Rarely visited path Frequently visited path

30

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  • Find Unknown Vulnerabilities Using

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    Find unknown vulnerabilities using INTRODUCTION Fuzz testing, or Fuzzing, is a technique for testing the security and stability of computer programs (applications). Fuzzing is performed by sending malformed data to an application’s input points, for the purpose of causing unexpected behavior, resource leaks or crashes in the application. A fuzzer will also stress the application with unexpectedly high workloads in order to test its robustness. A typical fuzzer generates random or semi-random, specially crafted data of different varieties Generational-based fuzzers build input based on certain specifications or formats that provide context-awareness. Evolutionary-based fuzzers use genetic algorithms to generate mutations in the input data while the fuzzer is learning about the input format, which as a result maximizes code coverage. The functionality of a fuzzer can be split across different layers and implementations of the targeted system: Application fuzzing targets the system’s input and output components, such as the UI, command- line options, forms or user-generated content. Protocol fuzzing sends forged packets to the application or acts as a proxy to modify and replay requests on-the-fly. File format fuzzing targets the parsing layer, file structure, and the encoding mechanism. Fuzzing consists of three different phases that the fuzzer must go through: 1st phase generation of random or malformed data (attack vector) to be sent as input to the target application 2nd phase delivery of the generated data to the target’s entry point 3rd phase inspection of the impact on the target to determine if the attack was successful Engagement in application security testing using fuzzing is usually performed prior to the application’s production release, with the purpose of ensuring the quality of the application’s runtime behavior in unmet and unassumed scenarios.
  • Android Spyware Disease and Medication Mustafa Hassan Saad, Department of Computer Engineering MTC Cairo, Egypt Eng.Mustafa.H.Saad@Gmail.Com

    Android Spyware Disease and Medication Mustafa Hassan Saad, Department of Computer Engineering MTC Cairo, Egypt [email protected]

    Android Spyware Disease and Medication Mustafa Hassan Saad, Department Of Computer Engineering MTC Cairo, Egypt [email protected] Abstract—Android-based smartphones are gaining significant that user may download and use these malicious applications advantages on its counterparts in terms of market share among which cause privacy leakage allowing attackers to put man- users. The increasing usage of Android OS make it ideal target in-middle (MiTM) visibility into every user transaction like for attackers. There is an urgent need to develop solutions that the proposed spy-ware in this paper. The application layer guard the user’s privacy and can monitor, detect and block has the largest attack surface where maximum damage to these Eavesdropping applications. In this paper, two proposed security occurs. In this paper an exploit to the broadcast paradigm are presented. The first proposed paradigm is a spy- ware application to highlight the security weaknesses “disease”. receiver has been presented for intercepting Received SMS, The spy-ware application has been used to deeply understand Incoming, and Outgoing calls information and silently transfer the vulnerabilities in the Android operating system, and to study these information to the developed cloud database using the how the spy-ware can be developed to abuse these vulnerabilities mobile Internet capabilities. Hence to keep a check on the for intercepting victim’s privacy such as received SMS, incoming malwares and the authenticity of the application, a medication calls and outgoing calls. The spy-ware abuses the Internet service solution is needed to dynamically analyze the behavior of to transfer the intercepted information from victim’s cell phone the already installed applications, and to work as a spy-ware illegally to a cloud database.