Remote Virtual Machine Monitor Detection Jason Franklin, Mark Luk, Jonathan McCune, Arvind Seshadri, Adrian Perrig, Leendert van Doorn

Remote Virtual Machine Monitor Detection  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 External Verifier Remote Machine Are you virtual?

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 cr3R/W cr2 R/W cr0cli

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  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 Fastest VMM = FV(x) Real Machine = RM(x)

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  instructions fit in the trace cache  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