Stamping out worms and other Internet pests

Miguel Castro

Microsoft Research

The challenge

• worms are bad– worms can infect many machines– attacker gains control of infected machines– worm propagation disrupts Internet traffic

• it is crucial to prevent these attacks– not a new challenge but no solution yet– the problem is as serious as ever

Preventing worm infections

• techniques to prevent bugs are important– type safe languages, static analysis, … – but unlikely to remove all bugs from services

• need research on worm containment– worms spread too fast for human response

– worm containment must be automatic

Automatic worm containment

• previous solutions are network centric– they analyze network traffic – block suspect packets

• no vulnerability information at network level– false negatives: worm traffic appears normal– false positives: good traffic misclassified– fundamental problem

false positives are a barrier to automation

End-to-end worm containment

• host-based cooperative detection– detector runs instrumented software– analyzes infection attempt to generate an alert– distributes alert to other hosts

• host-based protection– hosts analyze exploit described by alert– generate protection mechanism automatically

• for example, a filter to block worm packets

• first steps: Vigilante [Costa et al, SOSP 2005]

Better host-based detectors

• existing detectors are not sufficient– easily bypassed (e.g., stack canaries, NX)– high overhead (e.g., program shepherding)– poor coverage of some attack classes

• better instrumentation to detect more worms– low false positives and false negatives– widely applicable (ideally to any binary)– low overhead (no alerts if negligible overhead)

Better host-based protection

• robust automatic protection– block all polymorphic variants of detected worms– no false positives– fast inoculation:

• fast alert distribution, fast deployment of protection

• efficient protection – provide good performance for legitimate requests– existing high coverage detectors are too expensive– recovery after detection is also expensive

Better survivability

• worm containment will not be perfect

• must survive compromised hosts– without disruptions to critical services– without loss of critical data

• better Byzantine fault tolerant replication – keep faults below threshold with high probability

Conclusion

• worms and viruses must be stopped

• need more research to stop them

• proposed a new research focus

• requires research spanning– operating systems, networking, programming

languages, and distributed systems

Vigilante’s components

Network

Detection Engine

SCA Generation

SCA Verification

SCA Distribution

Detector Host

SCA Verification

Protection

Vulnerable Host

SCA Distribution

Filter

Vulnerable Application

Network Network

• Detection

• SCA generation

• SCA distribution

• SCA verification

• Protection

Outline

• self-certifying alerts

• detection and generation of self-certifying alerts

• generation of vulnerability filters

• evaluation

• next steps

Self-certifying alerts

• identify an application vulnerability– describe how to exploit a vulnerability – contain a log of events, verification information

• enable hosts to verify if they are vulnerable– hosts modify events to signal verification success– hosts replay events in sandboxed application– there are no false positives

enable cooperative worm containment without trust

SCA types

• arbitrary execution control (AEC)– attacker can load a value in message into the PC

• arbitrary code execution (ACE)– attacker can execute code in message

• arbitrary function argument (AFA)– attacker can call function with arbitrary argument

Verifying an AEC alert

vulnerable process

normal code

verified

SCA:• message log• verification information

0x111111111114444444411111

ReceiveMessage()0x44444444

proves that external interfaces allow arbitrary control of the execution

SCA generation

• log events

• generate SCA when worm is detected– compute verification information – search log for relevant events– generate tentative version of SCA– repeat until verification succeeds

• detectors may guide search– dynamic dataflow analysis is one such detector

Dynamic dataflow analysis

• high coverage and low false positive rate• allows direct extraction of verification information

return address

msg

buffer

(a) Memory beforevulnerable code

(b) Memory after vulnerable code

id 100

id 400

stackpointer

return address

msg

buffer

id 100

id 400

id 100

id 400

stack

Cooperative worm containment

• SCA enables cooperative containment– any host can be a detector– hosts can run high-overhead detection engines – hosts can run different detection engines

• NX, …

– small TCB for SCA verification

cooperation enables low false negative rate

SCA broadcast

• uses secure Pastry– hosts join overlay– detectors flood alerts over overlay links

• denial-of-service prevention– per-link rate limiting – per-hop filtering and verification

can reach vulnerable hosts with high probability

Protection

• hosts generate filter from SCA

• dynamic data and control flow analysis– run vulnerable application in a sandbox– track control and data flow from input messages– compute conditions that determine execution path– filter blocks messages that satisfy conditions

• filters can block polymorphic worms

• no false positives by design

Vulnerability filter generationmov al,[netbuf]

mov cl,0x31

cmp al,cl

jne out

xor eax,eax

loop:

mov [esp+eax+4],cl

mov cl,[eax+netbuf+1]

inc eax

test cl,cl

jne loop

out:

Conditions:

netbuf[0] == 0x31

netbuf[1] != 0

netbuf[2] != 0

0x31 0x24 0x67 0x42 0x0

netbuf

Filter

Vulnerable Application

Network

Evaluation

• three real worms:– Slammer (SQL server), Blaster (RPC), CodeRed (IIS)

• measurements of prototype implementation– SCA generation and verification– filter generation– filtering overhead

• simulations of SCA propagation with attacks

Time to generate SCAs

Time to verify SCAs

Time to generate filters

Filtering overhead

0%

5%

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45%

50%

0.000 0.002 0.004 0.006 0.008 0.010

Detector Fraction

Infe

cted

Per

cen

tag

e Slammer+DoS

Slammer

Containing Slammer

0%

5%

10%

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30%

0.5β β 2β 4β 8β

Infection Rate

Infe

cte

d P

erc

en

tag

e w/ DoS

w/o DoS

Increasing infection rate

ß is Slammer’s infection rate(the rate at which a host infects new hosts)

0%

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0 200 400 600 800 1000

SCA Verification Time (msec)

Infe

cte

d P

erc

en

tag

e w/ DoS

w/o DoS

Increasing verification time

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35%

0 2000 4000 6000 8000 10000

Number of Initially Infected Hosts

Infe

cte

d P

erc

en

tag

e w/ DoS

w/o DoS

Baseline

Increasing seed hosts

Conclusion

• Vigilante can contain worms automatically– requires no prior knowledge of vulnerabilities– no false positives– low false negatives