Remote Virtual Machine Monitor
Detection
Jason Franklin, Mark Luk, Jonathan
McCune, Arvind Seshadri, Adrian Perrig,
Leendert van Doorn
Remote Virtual Machine Monitor Detection
Are you virtual?
`
External
Verifier
Remote
Machine
 Problem Statement
• Determine if a remote machine is virtual or real
 Challenges
• VMM provides an accurate abstraction of the underlying hardware
• VMM controls execution of code and may return arbitrary values
VMM Detection and Botnets (1/2)
 Scenario 1
• Bots may install a stealthy virtual machine based
rootkit (VMBR) to avoid detection by traditional
malware scanners
• Stealthy rootkits prevent administered machines
from removing bots
• You run an AV, update, patch, yet never locate/remove
the bot
• Detecting VMMs allows us to detect bots
VMM Detection and Botnets (2/2)
 Scenario 2
• Bots may check for the existence of a VMM in
order to prevent dynamic analysis
• “Detecting the sandbox”
• Real threat & mentioned several times yesterday
• Agobot uses a heuristic to check for VMWare
• Studying VMM detection helps us understand
how to enable VMM-based dynamic analysis
State of the Art in VMM Detection
 Check for software-implementation artifacts
• Redpill checks the location of the IDT (different
location under VMWare)
• VMWare’s Back checks for VMWare I/O port
 Other approaches
• Make restrictive assumptions
• Easy to thwart
• Require benchmarking
Our Goals
 Develop a VMM detection algorithm:
• VMM implementation independent
• Accurate
• Practical/relies on few assumptions
 Leverage fundamental differences between
virtual and real machines
VMM Model
 Popek and Goldberg ’74 formally defined the
properties a control program must satisfy to
be deemed a VMM
• Efficiency Property
• Resource Control Property
• Equivalence Property
• Program execution in a virtual environment must be
indistinguishable from execution in a real environment
Indistinguishable? Oh no!
 If a program executes indistinguishably,
we can’t detect a virtual execution
environment
 Don’t worry! There are exceptions to the
equivalence property
• Timing dependency exception
• Certain sequences of instructions may take longer
to execute
• Resource availability exception
Does the timing dependency
exception necessarily exist?
 Empirically, yes.
• Programs executing in a VMM experience VMM
overhead
 In theory, yes.
• Intuition is that VMM must maintain control of
executing code by interposing on the operations
or rewrite the binary
Exploiting the timing dependency
exception to detect a VMM
 Algorithm:
Given:
• Real machine R with configuration C e.g., C={Pentium IV,
2.0GHz}
• Remote machine M with configuration C
• Program P with control-modifying instructions
1: Time the execution of P on R and store the value in r
2: Time the execution of P on M and store the value in m
3: IF m > r + k THEN M is virtual [note: k is the detection constant]
4: ELSE M is real
Tasks Remaining
 Achieve accurate high-integrity execution
timing
 Construct program P with externally
noticeable VMM overhead
 Determine configuration of remote machine
 Determine detection constant k
Accurate High-Integrity Execution Timing
 Can’t trust the integrity of the timing measurements
returned by the VMM
 Use an external source of time (e.g., remote machine,
watch, etc…)
Constructing P with VMM Overhead
 P is a sequence of sensitive (potentially
control modifying) instructions that requires
VMM interposition
 P is designed to invoke VMM overhead
 Design decisions in developing P include:
• Sensitive instruction selection
• Number of instructions
Selecting Sensitive Instructions
R/W cr3
R/W cr2
R/W cr0
cli
Number of Instructions in P
 Assume we have complete configuration
information for remote machine M
 Easy to determine the number of instructions
required to overcome experimental noise
• Variance in execution time
• Variance in network latency
Complete Configuration Information
Fastest VMM = FV(x)
Real Machine = RM(x)
 Given an estimate of the noise N in the environment
(i.e., 10 ms variation in network latency)
 Select x s.t. FV(x) – RM(x) >> N
Incomplete Configuration Information
 Unreasonable to assume complete
configuration information is available for a
remote machine
 Use “hardware discovery” heuristic
• Intuition: certain properties of the underlying
hardware are difficult to mask through the VMM
and are unique to a particular architecture
• Discovering these hardware artifacts gives us
partial configuration information about a remote
machine
Incomplete Configuration Information
 Given a subset C’ of the complete configuration information C
• C = {Pentium IV, 2.0 GHz} and C’ = {Pentium IV}
 Bound the execution time of P on the fastest and slowest
machines that satisfy C’
• Works because P is CPU bound
• We can time the execution of P on a x GHz machine and then
use the ratio of the fastest and slowest machines to bound the
execution times
Hardware Discovery on the Pentium IV
 P4 has a unique trace cache which “shines” through the VMM
 With sequences of register-to-register arithmetic instructions
without data hazards populate the trace cache of the Intel
Pentium IV, a CPI of 1/3 is attainable
 Once an instruction sequence exceeds the trace cache’s size
of 12KB, the CPI becomes 1
Remote Trace Cache Discovery
 11264 instructions fit in the trace cache
 11328 instructions exceeds the size of the trace cache
 A considerable jump in overhead occurs when the trace
cache overflows
Putting it All Together
 Remotely timed overhead from reading and writing x86 Control
Register 3 multiple times consecutively
 Despite not being included in our analysis, remote detection works
against a machine running Xen with hardware virtualization support
(HVM Xen)
• We conclude that hardware virtualization support is not sufficient
to prevent VMM detection
Detection Algorithm Limitations
 VMM could tamper with execution of detection code
• Countermeasure: Leverage software-based attestation
(Pioneer)
 VMM could prevent communication to external timer
• Countermeasure: Containment policy-based detection
 Receive incorrect response from hardware
discovery heuristic
 VMM may be incorporated with OS
• Malware can still own the lowest layer
• Virtual-machine-based rootkits are a threat today
Conclusion
 Developed a remote VMM detection algorithm
• Attempts to be independent of VMM software
implementation details
• Practical/relies on fewer assumptions than previous
schemes
• Accurate, configurable, and effective over the
Internet
 Hardware virtualization support is not sufficient
to mask differences between real and virtual
environments