Meltdown & Spectre attacks

  • Written by
    Herman Bos
  • Published on

Information regarding Meltdown and Spectre attacks.

Current state

  • Waiting for software patch availability. Patched ubuntu kernels are available for testing.


  • 20180104: Created blogpost
  • 20180105: Added new information/links
  • 20180105: Status update
  • 20180108: Added information from Redhat about performance impact from patches.
  • 20180108: Updated links list.
  • 20180108: Status update

High level description


  • Spectre - CVE-2017-5715
  • Spectre - CVE-2017-5753
  • Meltdown - CVE-2017-5754

As described on: / (same site)


Meltdown breaks the most fundamental isolation between user applications and the operating system. This attack allows a program to access the memory, and thus also the secrets, of other programs and the operating system.

If your computer has a vulnerable processor and runs an unpatched operating system, it is not safe to work with sensitive information without the chance of leaking the information. This applies both to personal computers as well as cloud infrastructure. Luckily, there are software patches against Meltdown.


Spectre breaks the isolation between different applications. It allows an attacker to trick error-free programs, which follow best practices, into leaking their secrets. In fact, the safety checks of said best practices actually increase the attack surface and may make applications more susceptible to Spectre

Spectre is harder to exploit than Meltdown, but it is also harder to mitigate.

Performance impact from security patches

  • Measureable: 8-19% - Highly cached random memory, with buffered I/O, OLTP database workloads, and benchmarks with high kernel-to-user space transitions are impacted between 8-19%. Examples include OLTP Workloads (tpc), sysbench, pgbench, netperf (< 256 byte), and fio (random I/O to NvME).
  • Modest: 3-7% - Database analytics, Decision Support System (DSS), and Java VMs are impacted less than the “Measurable” category. These applications may have significant sequential disk or network traffic, but kernel/device drivers are able to aggregate requests to moderate level of kernel-to-user transitions. Examples include SPECjbb2005, Queries/Hour and overall analytic timing (sec).
  • Small: 2-5% - HPC (High Performance Computing) CPU-intensive workloads are affected the least with only 2-5% performance impact because jobs run mostly in user space and are scheduled using cpu-pinning or numa-control. Examples include Linpack NxN on x86 and SPECcpu2006.
  • Minimal: Linux accelerator technologies that generally bypass the kernel in favor of user direct access are the least affected, with less than 2% overhead measured. Examples tested include DPDK (VsPERF at 64 byte) and OpenOnload (STAC-N). Userspace accesses to VDSO like get-time-of-day are not impacted. We expect similar minimal impact for other offloads.

NOTE: Because microbenchmarks like netperf/uperf, iozone, and fio are designed to stress a specific hardware component or operation, their results are not generally representative of customer workload. Some microbenchmarks have shown a larger performance impact, related to the specific area they stress.

Due to containerized applications being implemented as generic Linux processes, applications deployed in containers incur the same performance impact as those deployed on bare metal. We expect the impact on applications deployed in virtual guests to be higher than bare metal due to the increased frequency of user-to-kernel transitions.

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