Tag Archives: intel

Another Intel Speculative Execution Vulnerability

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/06/another_intel_s.html

Remember Spectre and Meltdown? Back in early 2018, I wrote:

Spectre and Meltdown are pretty catastrophic vulnerabilities, but they only affect the confidentiality of data. Now that they — and the research into the Intel ME vulnerability — have shown researchers where to look, more is coming — and what they’ll find will be worse than either Spectre or Meltdown. There will be vulnerabilities that will allow attackers to manipulate or delete data across processes, potentially fatal in the computers controlling our cars or implanted medical devices. These will be similarly impossible to fix, and the only strategy will be to throw our devices away and buy new ones.

That has turned out to be true. Here’s a new vulnerability:

On Tuesday, two separate academic teams disclosed two new and distinctive exploits that pierce Intel’s Software Guard eXtension, by far the most sensitive region of the company’s processors.


The new SGX attacks are known as SGAxe and CrossTalk. Both break into the fortified CPU region using separate side-channel attacks, a class of hack that infers sensitive data by measuring timing differences, power consumption, electromagnetic radiation, sound, or other information from the systems that store it. The assumptions for both attacks are roughly the same. An attacker has already broken the security of the target machine through a software exploit or a malicious virtual machine that compromises the integrity of the system. While that’s a tall bar, it’s precisely the scenario that SGX is supposed to defend against.

Another news article.

Attack Against PC Thunderbolt Port

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/05/attack_against_2.html

The attack requires physical access to the computer, but it’s pretty devastating:

On Thunderbolt-enabled Windows or Linux PCs manufactured before 2019, his technique can bypass the login screen of a sleeping or locked computer — and even its hard disk encryption — to gain full access to the computer’s data. And while his attack in many cases requires opening a target laptop’s case with a screwdriver, it leaves no trace of intrusion and can be pulled off in just a few minutes. That opens a new avenue to what the security industry calls an “evil maid attack,” the threat of any hacker who can get alone time with a computer in, say, a hotel room. Ruytenberg says there’s no easy software fix, only disabling the Thunderbolt port altogether.

“All the evil maid needs to do is unscrew the backplate, attach a device momentarily, reprogram the firmware, reattach the backplate, and the evil maid gets full access to the laptop,” says Ruytenberg, who plans to present his Thunderspy research at the Black Hat security conference this summer­or the virtual conference that may replace it. “All of this can be done in under five minutes.”

Lots of details in the article above, and in the attack website. (We know it’s a modern hack, because it comes with its own website and logo.)

Intel responds.

EDITED TO ADD (5/14): More.

Another Side Channel in Intel Chips

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/09/another_side_ch.html

Not that serious, but interesting:

In late 2011, Intel introduced a performance enhancement to its line of server processors that allowed network cards and other peripherals to connect directly to a CPU’s last-level cache, rather than following the standard (and significantly longer) path through the server’s main memory. By avoiding system memory, Intel’s DDIO­short for Data-Direct I/O­increased input/output bandwidth and reduced latency and power consumption.

Now, researchers are warning that, in certain scenarios, attackers can abuse DDIO to obtain keystrokes and possibly other types of sensitive data that flow through the memory of vulnerable servers. The most serious form of attack can take place in data centers and cloud environments that have both DDIO and remote direct memory access enabled to allow servers to exchange data. A server leased by a malicious hacker could abuse the vulnerability to attack other customers. To prove their point, the researchers devised an attack that allows a server to steal keystrokes typed into the protected SSH (or secure shell session) established between another server and an application server.

Attacking the Intel Secure Enclave

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/08/attacking_the_i.html

Interesting paper by Michael Schwarz, Samuel Weiser, Daniel Gruss. The upshot is that both Intel and AMD have assumed that trusted enclaves will run only trustworthy code. Of course, that’s not true. And there are no security mechanisms that can deal with malicious enclaves, because the designers couldn’t imagine that they would be necessary. The results are predictable.

The paper: “Practical Enclave Malware with Intel SGX.”

Abstract: Modern CPU architectures offer strong isolation guarantees towards user applications in the form of enclaves. For instance, Intel’s threat model for SGX assumes fully trusted enclaves, yet there is an ongoing debate on whether this threat model is realistic. In particular, it is unclear to what extent enclave malware could harm a system. In this work, we practically demonstrate the first enclave malware which fully and stealthily impersonates its host application. Together with poorly-deployed application isolation on personal computers, such malware can not only steal or encrypt documents for extortion, but also act on the user’s behalf, e.g., sending phishing emails or mounting denial-of-service attacks. Our SGX-ROP attack uses new TSX-based memory-disclosure primitive and a write-anything-anywhere primitive to construct a code-reuse attack from within an enclave which is then inadvertently executed by the host application. With SGX-ROP, we bypass ASLR, stack canaries, and address sanitizer. We demonstrate that instead of protecting users from harm, SGX currently poses a security threat, facilitating so-called super-malware with ready-to-hit exploits. With our results, we seek to demystify the enclave malware threat and lay solid ground for future research on and defense against enclave malware.

Another Intel Chip Flaw

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/05/another_intel_c.html

Remember the Spectre and Meltdown attacks from last year? They were a new class of attacks against complex CPUs, finding subliminal channels in optimization techniques that allow hackers to steal information. Since their discovery, researchers have found additional similar vulnerabilities.

A whole bunch more have just been discovered.

I don’t think we’re finished yet. A year and a half ago I wrote: “But more are coming, and they’ll be worse. 2018 will be the year of microprocessor vulnerabilities, and it’s going to be a wild ride.” I think more are still coming.

More Spectre/Meltdown-Like Attacks

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/11/more_spectremel.html

Back in January, we learned about a class of vulnerabilities against microprocessors that leverages various performance and efficiency shortcuts for attack. I wrote that the first two attacks would be just the start:

It shouldn’t be surprising that microprocessor designers have been building insecure hardware for 20 years. What’s surprising is that it took 20 years to discover it. In their rush to make computers faster, they weren’t thinking about security. They didn’t have the expertise to find these vulnerabilities. And those who did were too busy finding normal software vulnerabilities to examine microprocessors. Security researchers are starting to look more closely at these systems, so expect to hear about more vulnerabilities along these lines.

Spectre and Meltdown are pretty catastrophic vulnerabilities, but they only affect the confidentiality of data. Now that they — and the research into the Intel ME vulnerability — have shown researchers where to look, more is coming — and what they’ll find will be worse than either Spectre or Meltdown. There will be vulnerabilities that will allow attackers to manipulate or delete data across processes, potentially fatal in the computers controlling our cars or implanted medical devices. These will be similarly impossible to fix, and the only strategy will be to throw our devices away and buy new ones.

We saw several variants over the year. And now researchers have discovered seven more.

Researchers say they’ve discovered the seven new CPU attacks while performing “a sound and extensible systematization of transient execution attacks” — a catch-all term the research team used to describe attacks on the various internal mechanisms that a CPU uses to process data, such as the speculative execution process, the CPU’s internal caches, and other internal execution stages.

The research team says they’ve successfully demonstrated all seven attacks with proof-of-concept code. Experiments to confirm six other Meltdown-attacks did not succeed, according to a graph published by researchers.

Microprocessor designers have spent the year rethinking the security of their architectures. My guess is that they have a lot more rethinking to do.

Speculation Attack Against Intel’s SGX

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/08/speculation_att.html

Another speculative-execution attack against Intel’s SGX.

At a high level, SGX is a new feature in modern Intel CPUs which allows computers to protect users’ data even if the entire system falls under the attacker’s control. While it was previously believed that SGX is resilient to speculative execution attacks (such as Meltdown and Spectre), Foreshadow demonstrates how speculative execution can be exploited for reading the contents of SGX-protected memory as well as extracting the machine’s private attestation key. Making things worse, due to SGX’s privacy features, an attestation report cannot be linked to the identity of its signer. Thus, it only takes a single compromised SGX machine to erode trust in the entire SGX ecosystem.

News article.

The details of the Foreshadow attack are a little more complicated than those of Meltdown. In Meltdown, the attempt to perform an illegal read of kernel memory triggers the page fault mechanism (by which the processor and operating system cooperate to determine which bit of physical memory a memory access corresponds to, or they crash the program if there’s no such mapping). Attempts to read SGX data from outside an enclave receive special handling by the processor: reads always return a specific value (-1), and writes are ignored completely. The special handling is called “abort page semantics” and should be enough to prevent speculative reads from being able to learn anything.

However, the Foreshadow researchers found a way to bypass the abort page semantics. The data structures used to control the mapping of virtual-memory addresses to physical addresses include a flag to say whether a piece of memory is present (loaded into RAM somewhere) or not. If memory is marked as not being present at all, the processor stops performing any further permissions checks and immediately triggers the page fault mechanism: this means that the abort page mechanics aren’t used. It turns out that applications can mark memory, including enclave memory, as not being present by removing all permissions (read, write, execute) from that memory.

EDITED TO ADD: Intel has responded:

L1 Terminal Fault is addressed by microcode updates released earlier this year, coupled with corresponding updates to operating system and hypervisor software that are available starting today. We’ve provided more information on our web site and continue to encourage everyone to keep their systems up-to-date, as it’s one of the best ways to stay protected.

I think this is a comprehensive link to everything the company is saying about the vulnerability.

EC2 Instance Update – M5 Instances with Local NVMe Storage (M5d)

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/ec2-instance-update-m5-instances-with-local-nvme-storage-m5d/

Earlier this month we launched the C5 Instances with Local NVMe Storage and I told you that we would be doing the same for additional instance types in the near future!

Today we are introducing M5 instances equipped with local NVMe storage. Available for immediate use in 5 regions, these instances are a great fit for workloads that require a balance of compute and memory resources. Here are the specs:

Instance NamevCPUsRAMLocal StorageEBS-Optimized BandwidthNetwork Bandwidth
m5d.large28 GiB1 x 75 GB NVMe SSDUp to 2.120 GbpsUp to 10 Gbps
m5d.xlarge416 GiB1 x 150 GB NVMe SSDUp to 2.120 GbpsUp to 10 Gbps
m5d.2xlarge832 GiB1 x 300 GB NVMe SSDUp to 2.120 GbpsUp to 10 Gbps
m5d.4xlarge1664 GiB1 x 600 GB NVMe SSD2.210 GbpsUp to 10 Gbps
m5d.12xlarge48192 GiB2 x 900 GB NVMe SSD5.0 Gbps10 Gbps
m5d.24xlarge96384 GiB4 x 900 GB NVMe SSD10.0 Gbps25 Gbps

The M5d instances are powered by Custom Intel® Xeon® Platinum 8175M series processors running at 2.5 GHz, including support for AVX-512.

You can use any AMI that includes drivers for the Elastic Network Adapter (ENA) and NVMe; this includes the latest Amazon Linux, Microsoft Windows (Server 2008 R2, Server 2012, Server 2012 R2 and Server 2016), Ubuntu, RHEL, SUSE, and CentOS AMIs.

Here are a couple of things to keep in mind about the local NVMe storage on the M5d instances:

Naming – You don’t have to specify a block device mapping in your AMI or during the instance launch; the local storage will show up as one or more devices (/dev/nvme*1 on Linux) after the guest operating system has booted.

Encryption – Each local NVMe device is hardware encrypted using the XTS-AES-256 block cipher and a unique key. Each key is destroyed when the instance is stopped or terminated.

Lifetime – Local NVMe devices have the same lifetime as the instance they are attached to, and do not stick around after the instance has been stopped or terminated.

Available Now
M5d instances are available in On-Demand, Reserved Instance, and Spot form in the US East (N. Virginia), US West (Oregon), EU (Ireland), US East (Ohio), and Canada (Central) Regions. Prices vary by Region, and are just a bit higher than for the equivalent M5 instances.



Friday Squid Blogging: Do Cephalopods Contain Alien DNA?

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/06/friday_squid_bl_627.html

Maybe not DNA, but biological somethings.

Cause of Cambrian explosion — Terrestrial or Cosmic?“:

Abstract: We review the salient evidence consistent with or predicted by the Hoyle-Wickramasinghe (H-W) thesis of Cometary (Cosmic) Biology. Much of this physical and biological evidence is multifactorial. One particular focus are the recent studies which date the emergence of the complex retroviruses of vertebrate lines at or just before the Cambrian Explosion of ~500 Ma. Such viruses are known to be plausibly associated with major evolutionary genomic processes. We believe this coincidence is not fortuitous but is consistent with a key prediction of H-W theory whereby major extinction-diversification evolutionary boundaries coincide with virus-bearing cometary-bolide bombardment events. A second focus is the remarkable evolution of intelligent complexity (Cephalopods) culminating in the emergence of the Octopus. A third focus concerns the micro-organism fossil evidence contained within meteorites as well as the detection in the upper atmosphere of apparent incoming life-bearing particles from space. In our view the totality of the multifactorial data and critical analyses assembled by Fred Hoyle, Chandra Wickramasinghe and their many colleagues since the 1960s leads to a very plausible conclusion — life may have been seeded here on Earth by life-bearing comets as soon as conditions on Earth allowed it to flourish (about or just before 4.1 Billion years ago); and living organisms such as space-resistant and space-hardy bacteria, viruses, more complex eukaryotic cells, fertilised ova and seeds have been continuously delivered ever since to Earth so being one important driver of further terrestrial evolution which has resulted in considerable genetic diversity and which has led to the emergence of mankind.

Two commentaries.

This is almost certainly not true.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

Some quick thoughts on the public discussion regarding facial recognition and Amazon Rekognition this past week

Post Syndicated from Dr. Matt Wood original https://aws.amazon.com/blogs/aws/some-quick-thoughts-on-the-public-discussion-regarding-facial-recognition-and-amazon-rekognition-this-past-week/

We have seen a lot of discussion this past week about the role of Amazon Rekognition in facial recognition, surveillance, and civil liberties, and we wanted to share some thoughts.

Amazon Rekognition is a service we announced in 2016. It makes use of new technologies – such as deep learning – and puts them in the hands of developers in an easy-to-use, low-cost way. Since then, we have seen customers use the image and video analysis capabilities of Amazon Rekognition in ways that materially benefit both society (e.g. preventing human trafficking, inhibiting child exploitation, reuniting missing children with their families, and building educational apps for children), and organizations (enhancing security through multi-factor authentication, finding images more easily, or preventing package theft). Amazon Web Services (AWS) is not the only provider of services like these, and we remain excited about how image and video analysis can be a driver for good in the world, including in the public sector and law enforcement.

There have always been and will always be risks with new technology capabilities. Each organization choosing to employ technology must act responsibly or risk legal penalties and public condemnation. AWS takes its responsibilities seriously. But we believe it is the wrong approach to impose a ban on promising new technologies because they might be used by bad actors for nefarious purposes in the future. The world would be a very different place if we had restricted people from buying computers because it was possible to use that computer to do harm. The same can be said of thousands of technologies upon which we all rely each day. Through responsible use, the benefits have far outweighed the risks.

Customers are off to a great start with Amazon Rekognition; the evidence of the positive impact this new technology can provide is strong (and growing by the week), and we’re excited to continue to support our customers in its responsible use.

-Dr. Matt Wood, general manager of artificial intelligence at AWS

Amazon SageMaker Updates – Tokyo Region, CloudFormation, Chainer, and GreenGrass ML

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/sagemaker-tokyo-summit-2018/

Today, at the AWS Summit in Tokyo we announced a number of updates and new features for Amazon SageMaker. Starting today, SageMaker is available in Asia Pacific (Tokyo)! SageMaker also now supports CloudFormation. A new machine learning framework, Chainer, is now available in the SageMaker Python SDK, in addition to MXNet and Tensorflow. Finally, support for running Chainer models on several devices was added to AWS Greengrass Machine Learning.

Amazon SageMaker Chainer Estimator

Chainer is a popular, flexible, and intuitive deep learning framework. Chainer networks work on a “Define-by-Run” scheme, where the network topology is defined dynamically via forward computation. This is in contrast to many other frameworks which work on a “Define-and-Run” scheme where the topology of the network is defined separately from the data. A lot of developers enjoy the Chainer scheme since it allows them to write their networks with native python constructs and tools.

Luckily, using Chainer with SageMaker is just as easy as using a TensorFlow or MXNet estimator. In fact, it might even be a bit easier since it’s likely you can take your existing scripts and use them to train on SageMaker with very few modifications. With TensorFlow or MXNet users have to implement a train function with a particular signature. With Chainer your scripts can be a little bit more portable as you can simply read from a few environment variables like SM_MODEL_DIR, SM_NUM_GPUS, and others. We can wrap our existing script in a if __name__ == '__main__': guard and invoke it locally or on sagemaker.

import argparse
import os

if __name__ =='__main__':

    parser = argparse.ArgumentParser()

    # hyperparameters sent by the client are passed as command-line arguments to the script.
    parser.add_argument('--epochs', type=int, default=10)
    parser.add_argument('--batch-size', type=int, default=64)
    parser.add_argument('--learning-rate', type=float, default=0.05)

    # Data, model, and output directories
    parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
    parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])
    parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])
    parser.add_argument('--test', type=str, default=os.environ['SM_CHANNEL_TEST'])

    args, _ = parser.parse_known_args()

    # ... load from args.train and args.test, train a model, write model to args.model_dir.

Then, we can run that script locally or use the SageMaker Python SDK to launch it on some GPU instances in SageMaker. The hyperparameters will get passed in to the script as CLI commands and the environment variables above will be autopopulated. When we call fit the input channels we pass will be populated in the SM_CHANNEL_* environment variables.

from sagemaker.chainer.estimator import Chainer
# Create my estimator
chainer_estimator = Chainer(
    hyperparameters={'epochs': 10, 'batch-size': 64}
# Train my estimator
chainer_estimator.fit({'train': train_input, 'test': test_input})

# Deploy my estimator to a SageMaker Endpoint and get a Predictor
predictor = chainer_estimator.deploy(

Now, instead of bringing your own docker container for training and hosting with Chainer, you can just maintain your script. You can see the full sagemaker-chainer-containers on github. One of my favorite features of the new container is built-in chainermn for easy multi-node distribution of your chainer training jobs.

There’s a lot more documentation and information available in both the README and the example notebooks.

AWS GreenGrass ML with Chainer

AWS GreenGrass ML now includes a pre-built Chainer package for all devices powered by Intel Atom, NVIDIA Jetson, TX2, and Raspberry Pi. So, now GreenGrass ML provides pre-built packages for TensorFlow, Apache MXNet, and Chainer! You can train your models on SageMaker then easily deploy it to any GreenGrass-enabled device using GreenGrass ML.


I want to give a quick shout out to all of our wonderful and inspirational friends in the JAWS UG who attended the AWS Summit in Tokyo today. I’ve very much enjoyed seeing your pictures of the summit. Thanks for making Japan an amazing place for AWS developers! I can’t wait to visit again and meet with all of you.


New – Pay-per-Session Pricing for Amazon QuickSight, Another Region, and Lots More

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-pay-per-session-pricing-for-amazon-quicksight-another-region-and-lots-more/

Amazon QuickSight is a fully managed cloud business intelligence system that gives you Fast & Easy to Use Business Analytics for Big Data. QuickSight makes business analytics available to organizations of all shapes and sizes, with the ability to access data that is stored in your Amazon Redshift data warehouse, your Amazon Relational Database Service (RDS) relational databases, flat files in S3, and (via connectors) data stored in on-premises MySQL, PostgreSQL, and SQL Server databases. QuickSight scales to accommodate tens, hundreds, or thousands of users per organization.

Today we are launching a new, session-based pricing option for QuickSight, along with additional region support and other important new features. Let’s take a look at each one:

Pay-per-Session Pricing
Our customers are making great use of QuickSight and take full advantage of the power it gives them to connect to data sources, create reports, and and explore visualizations.

However, not everyone in an organization needs or wants such powerful authoring capabilities. Having access to curated data in dashboards and being able to interact with the data by drilling down, filtering, or slicing-and-dicing is more than adequate for their needs. Subscribing them to a monthly or annual plan can be seen as an unwarranted expense, so a lot of such casual users end up not having access to interactive data or BI.

In order to allow customers to provide all of their users with interactive dashboards and reports, the Enterprise Edition of Amazon QuickSight now allows Reader access to dashboards on a Pay-per-Session basis. QuickSight users are now classified as Admins, Authors, or Readers, with distinct capabilities and prices:

Authors have access to the full power of QuickSight; they can establish database connections, upload new data, create ad hoc visualizations, and publish dashboards, all for $9 per month (Standard Edition) or $18 per month (Enterprise Edition).

Readers can view dashboards, slice and dice data using drill downs, filters and on-screen controls, and download data in CSV format, all within the secure QuickSight environment. Readers pay $0.30 for 30 minutes of access, with a monthly maximum of $5 per reader.

Admins have all authoring capabilities, and can manage users and purchase SPICE capacity in the account. The QuickSight admin now has the ability to set the desired option (Author or Reader) when they invite members of their organization to use QuickSight. They can extend Reader invites to their entire user base without incurring any up-front or monthly costs, paying only for the actual usage.

To learn more, visit the QuickSight Pricing page.

A New Region
QuickSight is now available in the Asia Pacific (Tokyo) Region:

The UI is in English, with a localized version in the works.

Hourly Data Refresh
Enterprise Edition SPICE data sets can now be set to refresh as frequently as every hour. In the past, each data set could be refreshed up to 5 times a day. To learn more, read Refreshing Imported Data.

Access to Data in Private VPCs
This feature was launched in preview form late last year, and is now available in production form to users of the Enterprise Edition. As I noted at the time, you can use it to implement secure, private communication with data sources that do not have public connectivity, including on-premises data in Teradata or SQL Server, accessed over an AWS Direct Connect link. To learn more, read Working with AWS VPC.

Parameters with On-Screen Controls
QuickSight dashboards can now include parameters that are set using on-screen dropdown, text box, numeric slider or date picker controls. The default value for each parameter can be set based on the user name (QuickSight calls this a dynamic default). You could, for example, set an appropriate default based on each user’s office location, department, or sales territory. Here’s an example:

To learn more, read about Parameters in QuickSight.

URL Actions for Linked Dashboards
You can now connect your QuickSight dashboards to external applications by defining URL actions on visuals. The actions can include parameters, and become available in the Details menu for the visual. URL actions are defined like this:

You can use this feature to link QuickSight dashboards to third party applications (e.g. Salesforce) or to your own internal applications. Read Custom URL Actions to learn how to use this feature.

Dashboard Sharing
You can now share QuickSight dashboards across every user in an account.

Larger SPICE Tables
The per-data set limit for SPICE tables has been raised from 10 GB to 25 GB.

Upgrade to Enterprise Edition
The QuickSight administrator can now upgrade an account from Standard Edition to Enterprise Edition with a click. This enables provisioning of Readers with pay-per-session pricing, private VPC access, row-level security for dashboards and data sets, and hourly refresh of data sets. Enterprise Edition pricing applies after the upgrade.

Available Now
Everything I listed above is available now and you can start using it today!

You can try QuickSight for 60 days at no charge, and you can also attend our June 20th Webinar.



Randomly generated, thermal-printed comics

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/random-comic-strip-generation-vomit-comic-robot/

Python code creates curious, wordless comic strips at random, spewing them from the thermal printer mouth of a laser-cut body reminiscent of Disney Pixar’s WALL-E: meet the Vomit Comic Robot!

The age of the thermal printer!

Thermal printers allow you to instantly print photos, data, and text using a few lines of code, with no need for ink. More and more makers are using this handy, low-maintenance bit of kit for truly creative projects, from Pierre Muth’s tiny PolaPi-Zero camera to the sound-printing Waves project by Eunice Lee, Matthew Zhang, and Bomani McClendon (and our own Secret Santa Babbage).

Vomiting robots

Interaction designer and developer Cadin Batrack, whose background is in game design and interactivity, has built the Vomit Comic Robot, which creates “one-of-a-kind comics on demand by processing hand-drawn images through a custom software algorithm.”

The robot is made up of a Raspberry Pi 3, a USB thermal printer, and a handful of LEDs.

Comic Vomit Robot Cadin Batrack's Raspberry Pi comic-generating thermal printer machine

At the press of a button, Processing code selects one of a set of Cadin’s hand-drawn empty comic grids and then randomly picks images from a library to fill in the gaps.

Vomit Comic Robot Cadin Batrack's Raspberry Pi comic-generating thermal printer machine

Each image is associated with data that allows the code to fit it correctly into the available panels. Cadin says about the concept behing his build:

Although images are selected and placed randomly, the comic panel format suggests relationships between elements. Our minds create a story where there is none in an attempt to explain visuals created by a non-intelligent machine.

The Raspberry Pi saves the final image as a high-resolution PNG file (so that Cadin can sell prints on thick paper via Etsy), and a Python script sends it to be vomited up by the thermal printer.

Comic Vomit Robot Cadin Batrack's Raspberry Pi comic-generating thermal printer machine

For more about the Vomit Comic Robot, check out Cadin’s blog. If you want to recreate it, you can find the info you need in the Imgur album he has put together.

We ❤ cute robots

We have a soft spot for cute robots here at Pi Towers, and of course we make no exception for the Vomit Comic Robot. If, like us, you’re a fan of adorable bots, check out Mira, the tiny interactive robot by Alonso Martinez, and Peeqo, the GIF bot by Abhishek Singh.

Mira Alfonso Martinez Raspberry Pi

The post Randomly generated, thermal-printed comics appeared first on Raspberry Pi.

The FBI tells everybody to reboot their router

Post Syndicated from corbet original https://lwn.net/Articles/755741/rss

warns of over 500,000 home routers that have been compromised
by the VPNFilter malware and is advising everybody to reboot their routers
to (partially) remove it. This Talos
Intelligence page
has a lot more information about VPNFilter, though a
lot apparently remains unknown. “At the time of this publication, we
do not have definitive proof on how the threat actor is exploiting the
affected devices. However, all of the affected makes/models that we have
uncovered had well-known, public vulnerabilities. Since advanced threat
actors tend to only use the minimum resources necessary to accomplish their
goals, we assess with high confidence that VPNFilter required no zero-day
exploitation techniques.

Security and Human Behavior (SHB 2018)

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/05/security_and_hu_7.html

I’m at Carnegie Mellon University, at the eleventh Workshop on Security and Human Behavior.

SHB is a small invitational gathering of people studying various aspects of the human side of security, organized each year by Alessandro Acquisti, Ross Anderson, and myself. The 50 or so people in the room include psychologists, economists, computer security researchers, sociologists, political scientists, neuroscientists, designers, lawyers, philosophers, anthropologists, business school professors, and a smattering of others. It’s not just an interdisciplinary event; most of the people here are individually interdisciplinary.

The goal is to maximize discussion and interaction. We do that by putting everyone on panels, and limiting talks to 7-10 minutes. The rest of the time is left to open discussion. Four hour-and-a-half panels per day over two days equals eight panels; six people per panel means that 48 people get to speak. We also have lunches, dinners, and receptions — all designed so people from different disciplines talk to each other.

I invariably find this to be the most intellectually stimulating conference of my year. It influences my thinking in many different, and sometimes surprising, ways.

This year’s program is here. This page lists the participants and includes links to some of their work. As he does every year, Ross Anderson is liveblogging the talks. (Ross also maintains a good webpage of psychology and security resources.)

Here are my posts on the first, second, third, fourth, fifth, sixth, seventh, eighth, ninth, and tenth SHB workshops. Follow those links to find summaries, papers, and occasionally audio recordings of the various workshops.

Next year, I’ll be hosting the event at Harvard.

C is to low level

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/05/c-is-too-low-level.html

I’m in danger of contradicting myself, after previously pointing out that x86 machine code is a high-level language, but this article claiming C is a not a low level language is bunk. C certainly has some problems, but it’s still the closest language to assembly. This is obvious by the fact it’s still the fastest compiled language. What we see is a typical academic out of touch with the real world.

The author makes the (wrong) observation that we’ve been stuck emulating the PDP-11 for the past 40 years. C was written for the PDP-11, and since then CPUs have been designed to make C run faster. The author imagines a different world, such as where CPU designers instead target something like LISP as their preferred language, or Erlang. This misunderstands the state of the market. CPUs do indeed supports lots of different abstractions, and C has evolved to accommodate this.

The author criticizes things like “out-of-order” execution which has lead to the Spectre sidechannel vulnerabilities. Out-of-order execution is necessary to make C run faster. The author claims instead that those resources should be spent on having more slower CPUs, with more threads. This sacrifices single-threaded performance in exchange for a lot more threads executing in parallel. The author cites Sparc Tx CPUs as his ideal processor.

But here’s the thing, the Sparc Tx was a failure. To be fair, it’s mostly a failure because most of the time, people wanted to run old C code instead of new Erlang code. But it was still a failure at running Erlang.

Time after time, engineers keep finding that “out-of-order”, single-threaded performance is still the winner. A good example is ARM processors for both mobile phones and servers. All the theory points to in-order CPUs as being better, but all the products are out-of-order, because this theory is wrong. The custom ARM cores from Apple and Qualcomm used in most high-end phones are so deeply out-of-order they give Intel CPUs competition. The same is true on the server front with the latest Qualcomm Centriq and Cavium ThunderX2 processors, deeply out of order supporting more than 100 instructions in flight.

The Cavium is especially telling. Its ThunderX CPU had 48 simple cores which was replaced with the ThunderX2 having 32 complex, deeply out-of-order cores. The performance increase was massive, even on multithread-friendly workloads. Every competitor to Intel’s dominance in the server space has learned the lesson from Sparc Tx: many wimpy cores is a failure, you need fewer beefy cores. Yes, they don’t need to be as beefy as Intel’s processors, but they need to be close.

Even Intel’s “Xeon Phi” custom chip learned this lesson. This is their GPU-like chip, running 60 cores with 512-bit wide “vector” (sic) instructions, designed for supercomputer applications. Its first version was purely in-order. Its current version is slightly out-of-order. It supports four threads and focuses on basic number crunching, so in-order cores seems to be the right approach, but Intel found in this case that out-of-order processing still provided a benefit. Practice is different than theory.

As an academic, the author of the above article focuses on abstractions. The criticism of C is that it has the wrong abstractions which are hard to optimize, and that if we instead expressed things in the right abstractions, it would be easier to optimize.

This is an intellectually compelling argument, but so far bunk.

The reason is that while the theoretical base language has issues, everyone programs using extensions to the language, like “intrinsics” (C ‘functions’ that map to assembly instructions). Programmers write libraries using these intrinsics, which then the rest of the normal programmers use. In other words, if your criticism is that C is not itself low level enough, it still provides the best access to low level capabilities.

Given that C can access new functionality in CPUs, CPU designers add new paradigms, from SIMD to transaction processing. In other words, while in the 1980s CPUs were designed to optimize C (stacks, scaled pointers), these days CPUs are designed to optimize tasks regardless of language.

The author of that article criticizes the memory/cache hierarchy, claiming it has problems. Yes, it has problems, but only compared to how well it normally works. The author praises the many simple cores/threads idea as hiding memory latency with little caching, but misses the point that caches also dramatically increase memory bandwidth. Intel processors are optimized to read a whopping 256 bits every clock cycle from L1 cache. Main memory bandwidth is orders of magnitude slower.

The author goes onto criticize cache coherency as a problem. C uses it, but other languages like Erlang don’t need it. But that’s largely due to the problems each languages solves. Erlang solves the problem where a large number of threads work on largely independent tasks, needing to send only small messages to each other across threads. The problems C solves is when you need many threads working on a huge, common set of data.

For example, consider the “intrusion prevention system”. Any thread can process any incoming packet that corresponds to any region of memory. There’s no practical way of solving this problem without a huge coherent cache. It doesn’t matter which language or abstractions you use, it’s the fundamental constraint of the problem being solved. RDMA is an important concept that’s moved from supercomputer applications to the data center, such as with memcached. Again, we have the problem of huge quantities (terabytes worth) shared among threads rather than small quantities (kilobytes).

The fundamental issue the author of the the paper is ignoring is decreasing marginal returns. Moore’s Law has gifted us more transistors than we can usefully use. We can’t apply those additional registers to just one thing, because the useful returns we get diminish.

For example, Intel CPUs have two hardware threads per core. That’s because there are good returns by adding a single additional thread. However, the usefulness of adding a third or fourth thread decreases. That’s why many CPUs have only two threads, or sometimes four threads, but no CPU has 16 threads per core.

You can apply the same discussion to any aspect of the CPU, from register count, to SIMD width, to cache size, to out-of-order depth, and so on. Rather than focusing on one of these things and increasing it to the extreme, CPU designers make each a bit larger every process tick that adds more transistors to the chip.

The same applies to cores. It’s why the “more simpler cores” strategy fails, because more cores have their own decreasing marginal returns. Instead of adding cores tied to limited memory bandwidth, it’s better to add more cache. Such cache already increases the size of the cores, so at some point it’s more effective to add a few out-of-order features to each core rather than more cores. And so on.

The question isn’t whether we can change this paradigm and radically redesign CPUs to match some academic’s view of the perfect abstraction. Instead, the goal is to find new uses for those additional transistors. For example, “message passing” is a useful abstraction in languages like Go and Erlang that’s often more useful than sharing memory. It’s implemented with shared memory and atomic instructions, but I can’t help but think it couldn’t better be done with direct hardware support.

Of course, as soon as they do that, it’ll become an intrinsic in C, then added to languages like Go and Erlang.


Academics live in an ideal world of abstractions, the rest of us live in practical reality. The reality is that vast majority of programmers work with the C family of languages (JavaScript, Go, etc.), whereas academics love the epiphanies they learned using other languages, especially function languages. CPUs are only superficially designed to run C and “PDP-11 compatibility”. Instead, they keep adding features to support other abstractions, abstractions available to C. They are driven by decreasing marginal returns — they would love to add new abstractions to the hardware because it’s a cheap way to make use of additional transitions. Academics are wrong believing that the entire system needs to be redesigned from scratch. Instead, they just need to come up with new abstractions CPU designers can add.

Kata Containers 1.0

Post Syndicated from ris original https://lwn.net/Articles/755230/rss

Kata Containers 1.0 has been released. “This first release of Kata Containers completes the merger of Intel’s Clear Containers and Hyper’s runV technologies, and delivers an OCI compatible runtime with seamless integration for container ecosystem technologies like Docker and Kubernetes.

Another Spectre-Like CPU Vulnerability

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/05/another_spectre.html

Google and Microsoft researchers have disclosed another Spectre-like CPU side-channel vulnerability, called “Speculative Store Bypass.” Like the others, the fix will slow the CPU down.

The German tech site Heise reports that more are coming.

I’m not surprised. Writing about Spectre and Meltdown in January, I predicted that we’ll be seeing a lot more of these sorts of vulnerabilities.

Spectre and Meltdown are pretty catastrophic vulnerabilities, but they only affect the confidentiality of data. Now that they — and the research into the Intel ME vulnerability — have shown researchers where to look, more is coming — and what they’ll find will be worse than either Spectre or Meltdown.

I still predict that we’ll be seeing lots more of these in the coming months and years, as we learn more about this class of vulnerabilities.

Spectre variants 3a and 4

Post Syndicated from corbet original https://lwn.net/Articles/755114/rss

Intel has, finally, disclosed
two more Spectre variants, called 3a and 4. The first (“rogue system
register read”) allows system-configuration registers to be read
speculatively, while the second (“speculative store bypass”) could enable
speculative reads to data after a store operation has been speculatively
ignored. Some more information on variant 4 can be found in the
Project Zero bug tracker
. The fix is to install microcode updates,
which are not yet available.

Japan’s Directorate for Signals Intelligence

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/05/japans_director.html

The Intercept has a long article on Japan’s equivalent of the NSA: the Directorate for Signals Intelligence. Interesting, but nothing really surprising.

The directorate has a history that dates back to the 1950s; its role is to eavesdrop on communications. But its operations remain so highly classified that the Japanese government has disclosed little about its work ­ even the location of its headquarters. Most Japanese officials, except for a select few of the prime minister’s inner circle, are kept in the dark about the directorate’s activities, which are regulated by a limited legal framework and not subject to any independent oversight.

Now, a new investigation by the Japanese broadcaster NHK — produced in collaboration with The Intercept — reveals for the first time details about the inner workings of Japan’s opaque spy community. Based on classified documents and interviews with current and former officials familiar with the agency’s intelligence work, the investigation shines light on a previously undisclosed internet surveillance program and a spy hub in the south of Japan that is used to monitor phone calls and emails passing across communications satellites.

The article includes some new documents from the Snowden archive.