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The 38th Annual IEEE/IFIP International Conference on Dependable Systems and Networks. Convicting Exploitable Software Vulnerabilities: An Efficient Input Provenance Based Approach. Zhiqiang Lin Xiangyu Zhang, Dongyan Xu Purdue University June 27 th , 2008. FC. User. Motivation.

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convicting exploitable software vulnerabilities an efficient input provenance based approach

The 38th Annual IEEE/IFIP International Conference on Dependable Systems and Networks

Convicting Exploitable Software Vulnerabilities: An Efficient Input Provenance Based Approach

Zhiqiang Lin

Xiangyu Zhang, Dongyan Xu

Purdue University

June 27th, 2008

motivation

FC

User

Motivation

Internet Worms

(CodeRed, Slammer)

Vulnerability In Software

DoS

DoS

Accidental Breaches

in Security

Viruses,

Trojan Horses,

Bots (Botnet)

Denial of

Service (DoS)

related work
Related Work
  • Dynamic analysis
    • Program shepherding (V. Kiriansky et al.)

TaintCheck (J. Newsome et al.)

Control Flow Integrity (M. Abadi et al.)

Data Flow Integrity (M. Castro et al.)…

    • Run-time overhead, and waiting for attack
  • Static analysis
    • BOON (D. Wagner et al.), Splint (D. Larochelle et al.), Archer (Y. Xie et al.), RATS, Flawfinder
    • False positive
  • Recent automated multi-path exploration
    • DART (P. Godefroid et al.), Cute (K. Sen et al.), EXE (C. Cadar et al.), SAGE (P. Godefroid et al.)
    • Low Efficiency
problem statement and our technique
Problem Statement and Our Technique
  • How to more efficiently discover/convict software vulnerability
  • An Efficient Input Provenance Based Approach
    • Conservative static analysis => Suspect
    • Dynamic analysis => Convicting the suspect and pruning false positives
      • Randomly mutation is avoided
      • No symbolic execution (can handle long execution)
  • Key idea
    • Data lineage tracing (Input Provenance)
basic idea
Basic Idea

Input Data label (Offset): 6 7 8 9

fread(&imagehed,sizeof(imagehed),1,in);

...

width=(imagehed.wide_lo+256*imagehed.wide_hi)

height=(imagehed.high_lo+256*imagehed.high_hi);

...

if((...(byte *)malloc(width*height))...)

{

fclose(in);

return(_PICERR_NOMEM);

}

...

231

245

246

494

495

496

497

498

Input a.gif (256x128):xx...0x00 0x01 0x80 0x00...

Integer Overflow

  • An image viewer: Zgv-5.8/readgif.c
architecture
Architecture

Input Lineage Tracer

Program Input

Lineage

Program/

binary

Run-time Detector

Static-front End

Input Mutator

New Input

Suspect

Evidence

A piece of instruction which is exploitable to trigger the vulnerability

component 1 input lineage tracer
Component 1. Input Lineage Tracer
  • Label the input stream (using the offset)
  • Track their propagation

mov 0xfffffffc(%ebp),%eax

mov %eax, 0xfffffff8(%ebp)

add %eax, %ecx

mov %ecx, %edx

component 1 input lineage tracer1
Component 1. Input Lineage Tracer
  • Key concept
    • Data Dependency

(direct propagation)

    • Control dependency

(indirect propagation)

mov 0xfffffffc(%ebp),%eax

mov %eax,0xfffffff8(%ebp)

  • b=a
  • 1. b=a;
  • a==1

cmpl $0x1,0xfffffffc(%ebp)

jne 804832d <main+0x25>

  • b=1

movl $0x1,0xfffffff8(%ebp)

  • 1. if (a==1)
  • 2. b=1;
  • 3. else
  • 4. c=0;

jmp 8048334 <main+0x2c>

  • c=0

movl $0x0,0xfffffff4(%ebp)

component 1 data lineage tracer
Component 1. Data Lineage Tracer

Input data tracking (labeled with its offset in the input stream)

get_new_id()

if def is an input value

U DL([email protected]) otherwise

DL Representation: reduced ordered Binary Decision Diagram (roBDD)

component 1 data lineage tracer1
Component 1. Data Lineage Tracer
  • An Example

DL([email protected]) = DL([email protected]) U DL([email protected]) = {6; 7}

READ (buf,size,...), 0<= i < size , buf[i], DL(buf[i]@pc231) = get_new_id()

231

245

246

494

495

496

497

498

DL([email protected]) = DL([email protected]) U DL([email protected]) = {8; 9}

fread(&imagehed,sizeof(imagehed),1,in);

...

width=(imagehed.wide_lo+256*imagehed.wide_hi)

height=(imagehed.high_lo+256*imagehed.high_hi);

...

if((...(byte *)malloc(width*height))...)

{

fclose(in);

return(_PICERR_NOMEM);

}

...

DL([email protected])= DL(buf[6]@pc231) = {6}

DL([email protected])=DL(buf[7]@pc231) = {7}

DL((width*height)@494) = {6;7;8;9}

component 2 input mutator
Component 2. Input Mutator

Program Input

Evidence

Data Lineage

Suspect

Heuristics#1: Buffer overflow mutation

(double buffer size …)

Heuristics#2: Format string mutation

(replace %s in format string argument)

Heuristics#3: Integer overflow mutation

(Boundary integer value: 0xffffffff,0,0x0fffffff)

implementation
Implementation
  • Diablo:
    • Control flow graph
    • Statically generate Control dependency to facilitate Valgrind instrumentation
    • http://diablo.elis.ugent.be/
  • Valgrind:
    • Lineage tracing
    • http://valgrind.org/
    • RoBDD (Reduced ordered Binary Decision Diagram) to represent the data lineage.
evaluation effectiveness
Evaluation - Effectiveness
  • Static Detector
    • Known vulnerability
      • CVE-2001-1413 (ncompress 4.2.4, SO)
      • CVE-2001-1228 (gzip 1.2.4, SO)
      • CVE-2002-1496 (Nullhttpd 0.50, HO)
      • CVE-2002-1549 (lhttpd 0.1, SO)
      • CVE-2000-0573 (wu-ftpd-2.6.0, Format String)
      • CVE-2001-0609 (cfingerd-1.4.3, Format String)
      • CVE-2005-0226 (ngircd-0.8.2, Format String)
      • CVE-2004-0904 (xzgv-0.8, IO & HO)
      • CVE-2006-3082 (GnuPG 1.4.3, IO & HO)
  • RATS (Unknown)
    • Make extension to catch: buffer overflow, integer overflow (ipgrab-0.99, epstool-3.3, dcraw-7.94)
evaluation cve 2006 3082 gnupg 1 4 3
Evaluation - CVE-2006-3082 (GnuPG 1.4.3)
  • GnuPG Parse_User_ID Remote Buffer Overflow Vulnerability

pktlen=in[2,3,4,5]

=0x ff ff ff ff

evaluation cve 2001 0609 cfingerd 1 4 3
Evaluation - CVE-2001-0609 (Cfingerd-1.4.3)

syslog(LOG_NOTICE, "%s", (char *) syslog_str);

evaluation performance lineage tracing
Evaluation – Performance (Lineage Tracing)

Platform: two 2.13 Ghz Pentium processors and 2G RAM running the Linux kernel 2.6.15

summary
Summary
  • An input lineage tracing and mutation system:
  • Capable of convicting known and unknown vulnerability.
  • Has reasonable overhead for the scenario of offline vulnerability conviction.

Data Lineage Tracer

Program Input

Lineage

New Input

Program/

binary

Run-time Detector

Static-front End

Input Mutator

Suspect

Evidence

slide21
Q & A

Thank you

For more information:

{zlin, xyzhang, dxu}@cs.purdue.edu

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