Hey, You, Get Off of My Cloud: Exploring Information Leakage in Third-Party Compute Clouds Written by Thomas Ristenpart Eran Tromer Hovav Shacham Stehan Savage Presented by Jeremy Weinstein

Outline IntroductionMotivation System specifications Experiments Explore cloud infrastructure. Explore cloud infrastructure. Determine co-residency. Determine co-residency. Achieve co-residency. Achieve co-residency. Exploit information. Exploit information. Legal, Ethical, and Contractual implications ConclusionContributionsWeaknessesReferencesQuestions?

Introduction Cloud computing is an networked computing system in which hardware with extra resources can be used by other users. Resource examples: Clock cycles Clock cycles Disk space Disk space

Introduction Sample Architecture

Introduction Who is this useful for? Consumers Need more computing resources than they have available. Need more computing resources than they have available. Temporary need which does not justify purchasing hardware. Temporary need which does not justify purchasing hardware.Seller Has more resources than they need. Has more resources than they need. Can make revenue selling or renting to clients. Can make revenue selling or renting to clients.

Motivation Computing systems such as this have many well documented security flaws. New infrastructure “expands the attack surface of the victim.” One client can access information from another if they are on the same physical machine.

System specifications Experiments were preformed on Amazon’s EC2 (Elastic Compute Cloud). Client space was purchased. This took the form of one or more Virtual Machines (VM). This took the form of one or more Virtual Machines (VM). Most experiments were done on m1.small instance type. The following specifications were split across all active clients on this particular cloud. 32-bit architecture 32-bit architecture Single virtual core equivalent to GHz 2007 Opteron processor. Single virtual core equivalent to GHz 2007 Opteron processor. 1.7 GB memory 1.7 GB memory 160 GB disk space. 160 GB disk space. $0.10 per hour fee. $0.10 per hour fee.

Experiments The goal is to determine the viability of attaining restricted data from another client. Targeted ports 80 and 443 only (http and https). The experiments use network probing to attain relevant information.

Probes External Probe: Probe originates outside the EC2 server and targets a user in the EC2 server. Probe originates outside the EC2 server and targets a user in the EC2 server. Internal Probe: Probe originates inside the EC2 server and targets a user in the EC2 server. Probe originates inside the EC2 server and targets a user in the EC2 server. External probing is contractually illegal by Amazon’s Terms of Use Policy.

Exploring the cloud infrastructure Clients on the EC2 server were assigned an IP address with prefix bits shared with other clients’ IPs, depending on instance type. Probing other addresses with WHOIS determined which areas of the network were being utilized by users.

Cloud Cartography

Determining co-residency To be co-resident you need: Matching dom0 addresses (same hardware) Matching dom0 addresses (same hardware) Small round-trip packet time or Small round-trip packet time or Similar IP addresses Similar IP addresses Using multiple accounts in arbitrary locations, they checked these factors to determine co- residency. Having the actual values of one’s own account determined a nil false positive rate.

Achieving co-residency Two techniques are presented to become co- resident with another user Brute Force Arbitrarily probe a target zone over a long period of time. Arbitrarily probe a target zone over a long period of time. Experiments achieved an 8.4% coverage of targets. Experiments achieved an 8.4% coverage of targets. Placement Locality Attack recently launched instances (temporal locality). Attack recently launched instances (temporal locality). Reports achieving co-residence 40% of the time. Reports achieving co-residence 40% of the time.

Achieving co-residency

Exploiting information Knowing about the cloud your account is on gives you key information about other users E.G. computational load. E.G. computational load. The slower your memory access, the more resources a co-resident user is using. One proposed method of using this information is as a pseudo key logger, determining the time between keystrokes.

Extracting information 1) Allocate memory 2) Sleep briefly to rank high on the scheduler 3) Prime: Read the memory to be sure its fully cached 4) Trigger: Loop until CPU’s cycle increases by a large amount (indicates other user access) 5) Probe: Analyze differences between reads.

Legal, Ethical, and Contractual implications Project was government funded. Computer Fraud and Abuse Act Probes checked public ports only Skirted around definitions such as “access” and “authorization” A malicious hacker would not be constrained by these issues

Prevention A hacker can be slowed or stopped by trying to prevent internal probing, or reducing the information a client has. Doing so would limit vulnerability of clients. Hackers would be prevented from seeing private information about the provider such as server infrastructure.

Conclusion Using these techniques a hacker can gain access a cloud client. As a client Probe the network Probe the network Learn its mapping Learn its mapping Attempt to gain co-residence with another client Attempt to gain co-residence with another client Gain private information from the co-resident Gain private information from the co-resident

Contributions Thorough analysis of every step required to exploit this technique. Techniques yielded good results. Indicated the importance of this work. Indicated the importance of this work. Made recommendations for further work.

Weaknesses Omitted outlier results from their conclusions. Walked a fine line of unethical research. Many graphs seemed busy and unhelpful. One experiment implied a very low number of test cases.

References [1]Ristenpart T, Trhomer E, Scacham H, Savage S. “Hey, You, Get Off of My Cloud: Exploring Information Leakage in Third-Party Compute Clouds.” [2]Cloud Computing. ng. March ng ng

Questions?