Tag Archives: Uncategorized

Thieves Using AirTags to “Follow” Cars

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/12/thieves-using-airtags-to-follow-cars.html

From Ontario and not surprising:

Since September 2021, officers have investigated five incidents where suspects have placed small tracking devices on high-end vehicles so they can later locate and steal them. Brand name “air tags” are placed in out-of-sight areas of the target vehicles when they are parked in public places like malls or parking lots. Thieves then track the targeted vehicles to the victim’s residence, where they are stolen from the driveway.

Thieves typically use tools like screwdrivers to enter the vehicles through the driver or passenger door, while ensuring not to set off alarms. Once inside, an electronic device, typically used by mechanics to reprogram the factory setting, is connected to the onboard diagnostics port below the dashboard and programs the vehicle to accept a key the thieves have brought with them. Once the new key is programmed, the vehicle will start and the thieves drive it away.

I’m not sure if there’s anything that can be done:

When Apple first released AirTags earlier this year, concerns immediately sprung up about nefarious use cases for the covert trackers. Apple responded with a slew of anti-stalking measures, but those are more intended for keeping people safe than cars. An AirTag away from its owner will sound an alarm, letting anyone nearby know that it’s been left behind, but it can take up to 24 hours for that alarm to go off — more than enough time to nab a car in the dead of night.

За маските и хората

Post Syndicated from Grigor Gatchev – A Weblog original http://www.gatchev.info/blog/?p=2403

– Ей, момиче! Не е редно маската ти да е под носа!

Дрезгавият глас на стареца на опашката пред мен ме изтръгна от мислите ми. Бяхме стигнали до касата, и маската на касиерката наистина покриваше само устата ѝ.

– Няма ли да си я оправиш?

– Не съм ви никаква, че да ми говорите на ти! – отвърна момичето и гордо вирна глава.

Огледах се. Маските и на тримата на опашката зад мен също бяха под носовете им. Очевидно беше нещо като традиция в магазинчето, в което бях влязъл случайно.

Да направя нещо, за да не започне скандал? Като минимум бързам и ще ме забави…

– Не се безпокойте, господине. При такива инфекции, ако човек не киха, носът е на практика само входна врата. Покрива ли маската устата, няма да ви зарази. – Не беше абсолютно точно вярно, но все пак може би щеше да успокои дядото.

Той се обърна, изгледа ме, кимна ми, хвърли на тезгяха няколко монети и излезе с покупката си. Аз също платих набързо, изскочих от магазина… и насмалко да си скъсам якето – успях неясно как да закача вътрешния му джоб на дръжката на вратата.

Докато го откачах, погледът ми падна върху останалите вътре. Касиерката тъкмо дръпваше маската върху носа си. Точно същото правеха и хората от опашката.

Защо?! Вече при тях нямаше никой, който да им създаде проблем!… ?!

Чак тогава ми просветна какво всъщност съм им казал. „Като си държите маските под носа, създавате проблем само на себе си. Така че вие се минавате, не околните.“

За да носят маските под носа си, значи не са вярвали, че са особено нужни. Може би дори не са вярвали, че има вируси и епидемия. Но в момента, в който им беше „намекнато“, че те се минават, моментално си ги оправиха. Страхът да не се минат се оказа по-силен от логиката, каквато можеха да я проумеят…

Просто случка, каквато се случи. И изводите ми от нея – каквито си ги направих.

Testing Faraday Cages

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/12/testing-faraday-cages.html

Matt Blaze tested a variety of Faraday cages for phones, both commercial and homemade.

The bottom line:

A quick and likely reliable “go/no go test” can be done with an Apple AirTag and an iPhone: drop the AirTag in the bag under test, and see if the phone can locate it and activate its alarm (beware of caching in the FindMy app when doing this).

This test won’t tell you the exact attenuation level, of course, but it will tell you if the attenuation is sufficient for most practical purposes. It can also detect whether an otherwise good bag has been damaged and compromised.

At least in the frequency ranges I tested, two commercial Faraday pouches (the EDEC OffGrid and Mission Darkness Window pouches) yielded excellent performance sufficient to provide assurance of signal isolation under most real-world circumstances. None of the makeshift solutions consistently did nearly as well, although aluminum foil can, under ideal circumstances (that are difficult to replicate) sometimes provide comparable levels of attenuation.

Smart Contract Bug Results in $31 Million Loss

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/12/smart-contract-bug-results-in-31-million-loss.html

A hacker stole $31 million from the blockchain company MonoX Finance , by exploiting a bug in software the service uses to draft smart contracts.

Specifically, the hack used the same token as both the tokenIn and tokenOut, which are methods for exchanging the value of one token for another. MonoX updates prices after each swap by calculating new prices for both tokens. When the swap is completed, the price of tokenIn­that is, the token sent by the user­decreases and the price of tokenOut­or the token received by the user­increases.

By using the same token for both tokenIn and tokenOut, the hacker greatly inflated the price of the MONO token because the updating of the tokenOut overwrote the price update of the tokenIn. The hacker then exchanged the token for $31 million worth of tokens on the Ethereum and Polygon blockchains.

The article goes on to talk about how common these sorts of attacks are. The basic problem is that the code is the ultimate authority — there is no adjudication protocol — so if there’s a vulnerability in the code, there is no recourse. And, of course, there are lots of vulnerabilities in code.

To me, this is reason enough never to use smart contracts for anything important. Human-based adjudication systems are not useless pre-Internet human baggage, they’re vital.

Announcing winners of the AWS Graviton Challenge Contest and Hackathon

Post Syndicated from Neelay Thaker original https://aws.amazon.com/blogs/compute/announcing-winners-of-the-aws-graviton-challenge-contest-and-hackathon/

At AWS, we are constantly innovating on behalf of our customers so they can run virtually any workload, with optimal price and performance. Amazon EC2 now includes more than 475 instance types that offer a choice of compute, memory, networking, and storage to suit your workload needs. While we work closely with our silicon partners to offer instances based on their latest processors and accelerators, we also drive more choice for our customers by building our own silicon.

The AWS Graviton family of processors were built as part of that silicon innovation initiative with the goal of pushing the price performance envelope for a wide variety of customer workloads in EC2. We now have 12 EC2 instance families powered by AWS Graviton2 processors – general purpose (M6g, M6gd), burstable (T4g), compute optimized (C6g, C6gd, C6gn), memory optimized (R6g, R6gd, X2gd), storage optimized (Im4gn, Is4gen), and accelerated computing (G5g) available globally across 23 AWS Regions. We also announced the preview of Amazon EC2 C7g instances powered by the latest generation AWS Graviton3 processors that will provide the best price performance for compute-intensive workloads in EC2. Thousands of customers, including Discovery, DIRECTV, Epic Games, and Formula 1, have realized significant price performance benefits with AWS Graviton-based instances for a broad range of workloads. This year, AWS Graviton-based instances also powered much of Amazon Prime Day 2021 and supported 12 core retail services during the massive 2-day online shopping event.

To make it easy for customers to adopt Graviton-based instances, we launched a program called the Graviton Challenge. Working with customers, we saw that many successful adoptions of Graviton-based instances were the result of one or two developers taking a single workload and spending a few days to benchmark the price performance gains with Graviton2-based instances, before scaling it to more workloads. The Graviton Challenge provides a step-by-step plan that developers can follow to move their first workload to Graviton-based instances. With the Graviton Challenge, we also launched a Contest (US-only), and then a Hackathon (global), where developers could compete for prizes by building new applications or moving existing applications to run on Graviton2-based instances. More than a thousand participants, including enterprises, startups, individual developers, open-source developers, and Arm developers, registered and ran a variety of applications on Graviton-based instances with significant price performance benefits. We saw some fantastic entries and usage of Graviton2-based instances across a variety of use cases and want to highlight a few.

The Graviton Challenge Contest winners:

  • Best Adoption – Enterprise and Most Impactful Adoption: VMware vRealize SRE team, who migrated 60 micro-services written in Java, Rust, and Golang to Graviton2-based general purpose and compute optimized instances and realized up to 48% latency reduction and 22% cost savings.
  • Best Adoption – Startup: Kasm Technologies, who realized up to 48% better performance and 25% potential cost savings for its container streaming platform built on C/C++ and Python.
  • Best New Workload adoption: Dustin Wilson, who built a dynamic tile server based on Golang and running on Graviton2-based memory-optimized instances that helps analysts query large geospatial datasets and benchmarked up to 1.8x performance gains over comparable x86-based instances.
  • Most Innovative Adoption: Loroa, an application that translates any given text into spoken words from one language into multiple other languages using Graviton2-based instances, Amazon Polly, and Amazon Translate.

If you are attending AWS re:Invent 2021 in person, you can hear more details on their Graviton adoption experience by attending the CMP213: Lessons learned from customers who have adopted AWS Graviton chalk talk.

Winners for the Graviton Challenge Hackathon:

  • Best New App: PickYourPlace, an open-source based data analytics platform to help users select a place to live based on property value, safety, and accessibility.
  • Best Migrated App: Genie, an image credibility checker based on deep learning that makes predictions on photographic and tampered confidence of an image.
  • Highest Potential Impact: Welly Tambunan, who’s also an AWS Community Builder, for porting big data platforms Spark, Dremio, and AirByte to Graviton2 instances so developers can leverage it to build big data capabilities into their applications.
  • Most Creative Use Case: OXY, a low-cost custom Oximeter with mobile and web apps that enables continuous and remote monitoring to prevent deaths due to Silent Hypoxia.
  • Best Technical Implementation: Apollonia Bot that plays songs, playlists, or podcasts on a Discord voice channel, so users can listen to it together.

It’s been incredibly exciting to see the enthusiasm and benefits realized by our customers. We are also thankful to our judges – Patrick Moorhead from Moor Insights, James Governor from RedMonk, and Jason Andrews from Arm, for their time and effort.

In addition to EC2, several AWS services for databases, analytics, and even serverless support options to run on Graviton-based instances. These include Amazon Aurora, Amazon RDS, Amazon MemoryDB, Amazon DocumentDB, Amazon Neptune, Amazon ElastiCache, Amazon OpenSearch, Amazon EMR, AWS Lambda, and most recently, AWS Fargate. By using these managed services on Graviton2-based instances, customers can get significant price performance gains with minimal or no code changes. We also added support for Graviton to key AWS infrastructure services such as Elastic Beanstalk, Amazon EKS, Amazon ECS, and Amazon CloudWatch to help customers build, run, and scale their applications on Graviton-based instances. Additionally, a large number of Linux and BSD-based operating systems, and partner software for security, monitoring, containers, CI/CD, and other use cases now support Graviton-based instances and we recently launched the AWS Graviton Ready program as part of the AWS Service Ready program to offer Graviton-certified and validated solutions to customers.

Congrats to all of our Contest and Hackathon winners! Full list of the Contest and Hackathon winners is available on the Graviton Challenge page.

P.S.: Even though the Contest and Hackathon have ended, developers can still access the step-by-step plan on the Graviton Challenge page to move their first workload to Graviton-based instances.

Intel Is Maintaining Legacy Technology for Security Research

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/11/intel-is-maintaining-legacy-technology-for-security-research.html

Interesting:

Intel’s issue reflects a wider concern: Legacy technology can introduce cybersecurity weaknesses. Tech makers constantly improve their products to take advantage of speed and power increases, but customers don’t always upgrade at the same pace. This creates a long tail of old products that remain in widespread use, vulnerable to attacks.

Intel’s answer to this conundrum was to create a warehouse and laboratory in Costa Rica, where the company already had a research-and-development lab, to store the breadth of its technology and make the devices available for remote testing. After planning began in mid-2018, the Long-Term Retention Lab was up and running in the second half of 2019.

The warehouse stores around 3,000 pieces of hardware and software, going back about a decade. Intel plans to expand next year, nearly doubling the space to 27,000 square feet from 14,000, allowing the facility to house 6,000 pieces of computer equipment.

Intel engineers can request a specific machine in a configuration of their choice. It is then assembled by a technician and accessible through cloud services. The lab runs 24 hours a day, seven days a week, typically with about 25 engineers working any given shift.

Slashdot thread.

AWS Security Profiles: Megan O’Neil, Sr. Security Solutions Architect

Post Syndicated from Maddie Bacon original https://aws.amazon.com/blogs/security/aws-security-profiles-megan-oneil-sr-security-solutions-architect/

AWS Security Profiles: Megan O’Neil, Sr. Security Solutions Architect
In the week leading up to AWS re:Invent 2021, we’ll share conversations we’ve had with people at AWS who will be presenting, and get a sneak peek at their work.

How long have you been at Amazon Web Services (AWS), and what do you do in your current role?

I’ve been at AWS nearly 4 years, and in IT security over 15 years. I’m a solutions architect with a specialty in security. I work with commercial customers in North America, helping them solve security problems and build out secure foundations for their AWS workloads.

How did you get started in security?

I took part in a Boeing internship for three summers starting my junior year of high school. This internship gave me the opportunity to work with mechanical engineers at Boeing. The specific team I worked with were engineers responsible for building digital tools and robots for the 767-400 line at the Everett plant in Washington state. The purpose of these custom tools and robots was to help build the planes more efficiently and accurately. I had a lot of fun and learned a lot from my time working with them. I asked the group for career advice during lunch one day, and they all pointed me towards computer science (CS) instead of mechanical engineering. Because of their strong support for CS, I took the first course, Intro to Computer Science, and was excited that something that I previously thought was intimidating was actually approachable and a subject I really enjoyed.

During my sophomore year there was a new elective class offered called Digital Security, which piqued my interest and influenced my senior project. I built (coded) an intrusion detection program that identified nefarious network traffic. I also worked on campus during college in the sound services department and participated in the Dance Ensemble Program, where I met the IT manager for a local hospital in Washington state, Good Samaritan Hospital in Puyallup. He was helping mix music at the studio I worked in. After showing him my senior project, he told me about a job opening for a network security specialist at the hospital. No one else had applied for the role. I then interviewed with the team, which was made up of only three engineers including the manager. They were responsible for the all-backend systems including the hospital information system, patient telemetry and clinic systems, the hospital network, etc. The group of people I worked with at the hospital is still very special to me, we are all still friends.

How do you explain your job to non-tech friends?

I’m in tech, and I help companies protect their websites and their customers’ data.

What are you currently working on that you’re excited about?

I’m very excited about re:Invent. It’s the 10th anniversary, we’re back in person, and I’ve got quite a few sessions I’m delivering.

Speaking of AWS re:Invent 2021 – can you give readers a sneak peek at what you’re covering?

The first is a session I’m delivering is called Use AWS to improve your security posture against ransomware (SEC308) with Merritt Baer, Principal in the Office of the CISO. We’re discussing what AWS services and features you can use to help you protect your systems from ransomware.

The second is a chalk talk, Automating and evidencing key compliance security controls (STP211-R1 and STP211-R2), I’m delivering with Kristin Haught, Principal Security TPM, and we’re discussing strategies for automating, monitoring, and evidencing common controls required for multiple compliance standards.

The third session is a builder session called Grant least privilege temporary access securely at scale (WPS304). We’ll use AWS Secrets Manager, AWS Identity and Access Management (IAM), and the isolated compute functionality provided by AWS Nitro Enclaves to allow system administrators to request and retrieve narrowly scoped and limited-time access.

The fourth session is another builder session called Detecting security threats with Amazon GuardDuty (SEC213-R1 and SEC213-R2). It includes several simulated scenarios, representing just a small sample of the threats that GuardDuty can detect. We will review how to view and analyze GuardDuty findings, how to send alerts based on the findings, and, finally, how to remediate findings.

From your perspective, what’s the most important thing to know about ransomware?

Whenever we see a security event continue to make news, it’s a call to action and an opportunity for customers to analyze their security programs including operations and controls. There’s no silver bullet when it comes to protection from ransomware, but it’s time to level up your security operations and controls. This means minimize human access, translate security policies into code, build mechanism and measure them, streamline the use of environment and infrastructure, and use advanced data/database service features.

For example, we still see customers with large amounts of long-lived credentials; it’s time to take inventory and minimize or eliminate them. While there is a small subset of use cases where they may be required, such as on-premises to AWS access, I recommend the following:

  1. Inventory your long-lived credentials.
  2. Ensure the access is least privilege, absolutely no wildcard actions and/or resources.
  3. If the access is interactive, apply multi-factor authentication (MFA).
  4. Ask if you can architect a better option that doesn’t rely on static access keys.
  5. Rotate access keys on a regular, frequent basis.
  6. Enable alerts on login events.

For more information, check out Ransomware mitigation: Top 5 protections and recovery preparation actions and Ransomware Risk Management on AWS Using the NIST Cyber Security Framework (CSF).

What’s your favorite Leadership Principle at Amazon and why?

Learn and Be Curious! I am the most happy in my job and personal life when I’m learning new things. I also believe that this principle is a way of life for us technology folks. Learning new technology and finding better ways of implementing technology is our job. My favorite quote/laptop sticker is:

“I hate programming”

“I hate programming”

“I hate programming”

“IT WORKS! ”

“I love programming.”

It just makes me laugh because it’s so true. Of course we are only that frustrated when something is very new. It’s like solving a puzzle. When a project comes together, it’s absolutely worth it – the puzzle pieces now fit.

What’s the best career advice you’ve ever gotten?

Work with a mentor. This can be casual by finding projects where you can collaborate with folks who have more experience than you. Or it can be more formal by asking someone to be your mentor and setting up a regular cadence of meetings with them. I’ve done both, a simple example is by collaborating with Merritt and Kristen on upcoming re:Invent presentations, I’ve already learned a lot from both of them just through the preparation process and developing the content. Having a mentor by your side can be especially helpful when setting new goals. Sometimes we need someone to push us out of our comfort zone and believe that we can achieve bigger things than we would have thought and then can help devise a plan to help you achieve those goals. All it takes is someone else believing in us.

If you had to pick any other job, what would you want to do?

I’ve always been interested in naturopathic medicine and getting to the root cause of an issue. It’s somewhat similar to my job in that I’m solving puzzles and complex problems, but in technology, instead of the body.
 

If you have feedback about this post, submit comments in the Comments section below.

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Author

Megan O’Neil

Megan is a Senior Specialist Solutions Architect focused on threat detection and incident response. Megan and her team enable AWS customers to implement sophisticated, scalable, and secure solutions that solve their business challenges.

Author

Maddie Bacon

Maddie (she/her) is a technical writer for AWS Security with a passion for creating meaningful content. She previously worked as a security reporter and editor at TechTarget and has a BA in Mathematics. In her spare time, she enjoys reading, traveling, and all things Harry Potter.

Apple Sues NSO Group

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/11/apple-sues-nso-group.html

Piling more on NSO Group’s legal troubles, Apple is suing it:

The complaint provides new information on how NSO Group infected victims’ devices with its Pegasus spyware. To prevent further abuse and harm to its users, Apple is also seeking a permanent injunction to ban NSO Group from using any Apple software, services, or devices.

NSO Group’s Pegasus spyware is favored by totalitarian governments around the world, who use it to hack Apple phones and computers.

More news:

Apple’s legal complaint provides new information on NSO Group’s FORCEDENTRY, an exploit for a now-patched vulnerability previously used to break into a victim’s Apple device and install the latest version of NSO Group’s spyware product, Pegasus. The exploit was originally identified by the Citizen Lab, a research group at the University of Toronto.

The spyware was used to attack a small number of Apple users worldwide with dangerous malware and spyware. Apple’s lawsuit seeks to ban NSO Group from further harming individuals by using Apple’s products and services. The lawsuit also seeks redress for NSO Group’s flagrant violations of US federal and state law, arising out of its efforts to target and attack Apple and its users.

NSO Group and its clients devote the immense resources and capabilities of nation-states to conduct highly targeted cyberattacks, allowing them to access the microphone, camera, and other sensitive data on Apple and Android devices. To deliver FORCEDENTRY to Apple devices, attackers created Apple IDs to send malicious data to a victim’s device — allowing NSO Group or its clients to deliver and install Pegasus spyware without a victim’s knowledge. Though misused to deliver FORCEDENTRY, Apple servers were not hacked or compromised in the attacks.

This follows in the footsteps of Facebook, which is also suing NSO Group and demanding a similar prohibition. And while the idea of the intermediary suing the attacker, and not the victim, is somewhat novel, I think it makes a lot of sense. I have a law journal article about to be published with Jon Penney on the Facebook case.

Offset lag metric for Amazon MSK as an event source for Lambda

Post Syndicated from Eric Johnson original https://aws.amazon.com/blogs/compute/offset-lag-metric-for-amazon-msk-as-an-event-source-for-lambda/

This post written by Adam Wagner, Principal Serverless Solutions Architect.

Last year, AWS announced support for Amazon Managed Streaming for Apache Kafka (MSK) and self-managed Apache Kafka clusters as event sources for AWS Lambda. Today, AWS adds a new OffsetLag metric to Lambda functions with MSK or self-managed Apache Kafka event sources.

Offset in Apache Kafka is an integer that marks the current position of a consumer. OffsetLag is the difference in offset between the last record written to the Kafka topic and the last record processed by Lambda. Kafka expresses this in the number of records, not a measure of time. This metric provides visibility into whether your Lambda function is keeping up with the records added to the topic it is processing.

This blog walks through using the OffsetLag metric along with other Lambda and MSK metrics to understand your streaming application and optimize your Lambda function.

Overview

In this example application, a producer writes messages to a topic on the MSK cluster that is an event source for a Lambda function. Each message contains a number and the Lambda function finds the factors of that number. It outputs the input number and results to an Amazon DynamoDB table.

Finding all the factors of a number is fast if the number is small but takes longer for larger numbers. This difference means the size of the number written to the MSK topic influences the Lambda function duration.

Example application architecture

Example application architecture

  1. A Kafka client writes messages to a topic in the MSK cluster.
  2. The Lambda event source polls the MSK topic on your behalf for new messages and triggers your Lambda function with batches of messages.
  3. The Lambda function factors the number in each message and then writes the results to DynamoDB.

In this application, several factors can contribute to offset lag. The first is the volume and size of messages. If more messages are coming in, the Lambda may take longer to process them. Other factors are the number of partitions in the topic, and the number of concurrent Lambda functions processing messages. A full explanation of how Lambda concurrency scales with the MSK event source is in the documentation.

If the average duration of your Lambda function increases, this also tends to increase the offset lag. This lag could be latency in a downstream service or due to the complexity of the incoming messages. Lastly, if your Lambda function errors, the MSK event source retries the identical records set until they succeed. This retry functionality also increases offset lag.

Measuring OffsetLag

To understand how the new OffsetLag metric works, you first need a working MSK topic as an event source for a Lambda function. Follow this blog post to set up an MSK instance.

To find the OffsetLag metric, go to the CloudWatch console, select All Metrics from the left-hand menu. Then select Lambda, followed by By Function Name to see a list of metrics by Lambda function. Scroll or use the search bar to find the metrics for this function and select OffsetLag.

OffsetLag metric example

OffsetLag metric example

To make it easier to look at multiple metrics at once, create a CloudWatch dashboard starting with the OffsetLag metric. Select Actions -> Add to Dashboard. Select the Create new button, provide the dashboard a name. Choose Create, keeping the rest of the options at the defaults.

Adding OffsetLag to dashboard

Adding OffsetLag to dashboard

After choosing Add to dashboard, the new dashboard appears. Choose the Add widget button to add the Lambda duration metric from the same function. Add another widget that combines both Lambda errors and invocations for the function. Finally, add a widget for the BytesInPerSec metric for the MSK topic. Find this metric under AWS/Kafka -> Broker ID, Cluster Name, Topic. Finally, click Save dashboard.

After a few minutes, you see a steady stream of invocations, as you would expect when consuming from a busy topic.

Data incoming to dashboard

Data incoming to dashboard

This example is a CloudWatch dashboard showing the Lambda OffsetLag, Duration, Errors, and Invocations, along with the BytesInPerSec for the MSK topic.

In this example, the OffSetLag metric is averaging about eight, indicating that the Lambda function is eight records behind the latest record in the topic. While this is acceptable, there is room for improvement.

The first thing to look for is Lambda function errors, which can drive up offset lag. The metrics show that there are no errors so the next step is to evaluate and optimize the code.

The Lambda handler function loops through the records and calls the process_msg function on each record:

def lambda_handler(event, context):
    for batch in event['records'].keys():
        for record in event['records'][batch]:
            try:
                process_msg(record)
            except:
                print("error processing record:", record)
    return()

The process_msg function handles base64 decoding, calls a factor function to factor the number, and writes the record to a DynamoDB table:

def process_msg(record):
    #messages are base64 encoded, so we decode it here
    msg_value = base64.b64decode(record['value']).decode()
    msg_dict = json.loads(msg_value)
    #using the number as the hash key in the dynamodb table
    msg_id = f"{msg_dict['number']}"
    if msg_dict['number'] <= MAX_NUMBER:
        factors = factor_number(msg_dict['number'])
        print(f"number: {msg_dict['number']} has factors: {factors}")
        item = {'msg_id': msg_id, 'msg':msg_value, 'factors':factors}
        resp = ddb_table.put_item(Item=item)
    else:
        print(f"ERROR: {msg_dict['number']} is >= limit of {MAX_NUMBER}")

The heavy computation takes place in the factor function:

def factor(number):
    factors = [1,number]
    for x in range(2, (int(1 + number / 2))):
        if (number % x) == 0:
            factors.append(x)
    return factors

The code loops through all numbers up to the factored number divided by two. The code is optimized by only looping up to the square root of the number.

def factor(number):
    factors = [1,number]
    for x in range(2, 1 + int(number**0.5)):
        if (number % x) == 0:
            factors.append(x)
            factors.append(number // x)
    return factors

There are further optimizations and libraries for factoring numbers but this provides a noticeable performance improvement in this example.

Data after optimization

Data after optimization

After deploying the code, refresh the metrics after a while to see the improvements:

The average Lambda duration has dropped to single-digit milliseconds and the OffsetLag is now averaging two.

If you see a noticeable change in the OffsetLag metric, there are several things to investigate. The input side of the system, increased messages per second, or a significant increase in the size of the message are a few options.

Conclusion

This post walks through implementing the OffsetLag metric to understand latency between the latest messages in the MSK topic and the records a Lambda function is processing. It also reviews other metrics that help understand the underlying cause of increases to the offset lag. For more information on this topic, refer to the documentation and other MSK Lambda metrics.

For more serverless learning resources, visit Serverless Land.

AWS Security Profiles: J.D. Bean, Sr. Security Solutions Architect

Post Syndicated from Maddie Bacon original https://aws.amazon.com/blogs/security/aws-security-profiles-j-d-bean-sr-security-solutions-architect/

JD Bean AWS Security Profile
In the week leading up to AWS re:Invent 2021, we’ll share conversations we’ve had with people at AWS who will be presenting, and get a sneak peek at their work.

How long have you been at AWS, and what do you do in your current role?

I’m coming up on my three-year anniversary at AWS. Which, as I say it out loud, is hard to believe. It feels as if the time has passed in the blink of an eye. I’m a Solutions Architect with a specialty in security. I work primarily with AWS Strategic Accounts, a set of companies at the forefront of innovation. I partner with my customers to help them design, build, and deploy secure and compliant cloud workloads.

How did you get started in security?

Security began as a hobby for me, and I found it came quite naturally. Perhaps it’s just the way my brain is wired, but I often found security was a topic that consistently drew me in. I leaned into security professionally, and I really enjoy it. AWS makes security its top priority, which is really exciting as a security professional. I’m the kind of person who loves to understand how all the pieces of a system fit together, and AWS Security has been an incredible opportunity, letting me carry my depth of expertise to all sorts of interesting new technical areas such as IoT, HPC, and AI/ML.

How do you explain your job to non-tech friends?

I often say that I work as an AWS Solutions Architect, which means I work with AWS customers to help design their cloud environments and projects, and that I specifically focus on security. If they’re interested in hearing more, I tell them AWS offers a wide array of services customers can configure and combine in all sorts of different ways to fit their needs. If they’re anything like me, I use the analogy of my own experience at hardware stores. In a way, part of what I do is to act like that helpful person at the hardware store who understands what all the tools and equipment do, how to use them correctly, and how they interact with one another. I partner with AWS customers to learn about their project requirements and help them work backwards from those requirements to determine the best approach for achieving their goals.

What are you currently working on that you’re excited about?

I’m working with my customers on a bunch of exciting projects for establishing security, governance, and compliance at scale. I’ve also been returning to my roots and spending more time focusing on open-source software, which is a big passion area for me both personally and professionally.

You’re presenting at AWS re:Invent this year—can you give readers a sneak peek at what you’re covering?

I’m presenting two sessions this year. The first session is a builder session called Grant least privilege temporary access securely at scale (WPS304). We’ll use AWS Secrets Manager, AWS Identity and Access Management (IAM), and the isolated compute functionality provided by AWS Nitro Enclaves to allow system administrators to request and retrieve narrowly scoped and limited-time access.

My second session is the Using AWS Nitro Enclaves to process highly sensitive data workshop (SEC304). AWS Nitro Enclaves allow customers to create an isolated, hardened, and highly constrained environment to host security-critical applications. A lot of work has gone in to building this workshop over the past few months, and I’m excited to share it at re:Invent.

The workshop gives attendees an opportunity to get hands-on, practical experience with AWS Nitro Enclaves. Attendees will get experience launching enclave applications, using the Nitro Enclaves secure local channel for communication. Attendees will also work with Nitro Enclaves’ included cryptographic attestation features and integration with AWS Key Management Services. After putting all these elements together, attendees will be able to see how you can be sure that only your authorized code in your Nitro Enclave is able to access sensitive material.

For those who won’t be able to join the re:Invent workshop session in person, the AWS Nitro Enclaves Workshop is available online and can be completed in your own account at any time.

What are you hoping the audience will take away from the session(s)?

I hope attendees will come away from the session with a sense of how approachable and flexible AWS Nitro Enclaves are, and start to formulate ideas for how they can use Nitro Enclaves in their own workloads.

From your perspective, what’s the biggest thing happening in confidential computing right now?

Over the last year I’ve seen a big increase in interest from customers around confidential computing. This is how we’ve been approaching the design of the AWS Nitro System for many years now. The Nitro System, the underlying platform for all modern Amazon EC2 instances, already provides confidential computing protections by default.

More recently, AWS Nitro Enclaves has offered a new capability for customers to divide their own workloads into more-trusted and less-trusted components. The isolation of workload components in AWS Nitro Enclaves is powered by the specialized hardware and associated firmware of the Nitro System.

What’s your favorite Leadership Principle at Amazon and why?

My favorite Amazon Leadership principle is Learn and Be Curious. I think I’m at my best when I’m learning, growing, and pushing outward at the edges. AWS is such an incredible place to work for people who love to learn. AWS is constantly innovating and inventing for our customers, and learning is central to the culture here.

What’s the best career advice you’ve ever received?

One piece of advice I’ve held close from an early age is just how important it is to be comfortable saying “I don’t know”—ideally followed by “but I’d like to find out.” This has served me well in life, both professionally and personally.

Another is “lead with trust.” Being willing to be vulnerable and assume the best of others goes a long way. At Amazon, one of our leadership principles is Earn Trust. I’ve found how important it is to set an example of offering trust to others. Most people tend to rise to a challenge. If you enter new interactions with a default expectation of trusting others, more often than not, your trust ends up being well-placed.

If you had to pick any other job, what would you want to do?

It’s funny you ask that. I still think of my current role as the “other job” I daydream about. I began my professional life in the legal field. Admittedly, my work was primarily focused around open-source software, so it wasn’t entirely unrelated to what I do now, but I really do feel like being a Solutions Architect is a second phase in my career. I’m enjoying this new chapter too much to give doing anything else much thought.

If you were to really press me, I’d say that my wife, who’s a psychologist, tells me I missed my calling as a therapist. I take that as a real compliment.

Author

J. D. Bean

J.D. is a senior security specialist Solutions Architect for AWS Strategic Accounts based out of New York City. His interests include security, privacy, and compliance. He is passionate about his work enabling AWS customers’ successful cloud journeys. J.D. holds a Bachelor of Arts from The George Washington University and a Juris Doctor from New York University School of Law.

Author

Maddie Bacon

Maddie (she/her) is a technical writer for AWS Security with a passion for creating meaningful content. She previously worked as a security reporter and editor at TechTarget and has a BA in Mathematics. In her spare time, she enjoys reading, traveling, and all things Harry Potter.

New Rowhammer Technique

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/11/new-rowhammer-technique.html

Rowhammer is an attack technique involving accessing — that’s “hammering” — rows of bits in memory, millions of times per second, with the intent of causing bits in neighboring rows to flip. This is a side-channel attack, and the result can be all sorts of mayhem.

Well, there is a new enhancement:

All previous Rowhammer attacks have hammered rows with uniform patterns, such as single-sided, double-sided, or n-sided. In all three cases, these “aggressor” rows — meaning those that cause bitflips in nearby “victim” rows — are accessed the same number of times.

Research published on Monday presented a new Rowhammer technique. It uses non-uniform patterns that access two or more aggressor rows with different frequencies. The result: all 40 of the randomly selected DIMMs in a test pool experienced bitflips, up from 13 out of 42 chips tested in previous work from the same researchers.

[…]

The non-uniform patterns work against Target Row Refresh. Abbreviated as TRR, the mitigation works differently from vendor to vendor but generally tracks the number of times a row is accessed and recharges neighboring victim rows when there are signs of abuse. The neutering of this defense puts further pressure on chipmakers to mitigate a class of attacks that many people thought more recent types of memory chips were resistant to.

Setting up EC2 Mac instances as shared remote development environments

Post Syndicated from Rick Armstrong original https://aws.amazon.com/blogs/compute/setting-up-ec2-mac-instances-as-shared-remote-development-environments/

This post is written by: Michael Meidlinger, Solutions Architect 

In December 2020, we announced a macOS-based Amazon Elastic Compute Cloud (Amazon EC2) instance. Amazon EC2 Mac instances let developers build, test, and package their applications for every Apple platform, including macOS, iOS, iPadOS, tvOS, and watchOS. Customers have been utilizing these instances in order to automate their build pipelines for the Apple platform and integrate their native build tools, such as Jenkins and GitLab.

Aside from build automation, more and more customers are looking to utilize EC2 Mac instances for interactive development. Several advantages exist when utilizing remote development environments over installations on local developer machines:

  • Light-weight process for rolling out consistent, up-to-date environments for every developer without having to install software locally.
  • Solve cross-platform issues by having separate environments for different target platforms, all of which are independent of the developer’s local setup.
  • Consolidate access to source code and internal build tools, as they can be integrated with the remote development environment rather than local developer machines.
  • No need for specialized or powerful developer hardware.

On top of that, this approach promotes cost efficiency, as it enables EC2 Mac instances to be shared and utilized by multiple developers concurrently. This is particularly relevant for EC2 Mac instances, as they run on dedicated Mac mini hosts with a minimum tenancy of 24 hours. Therefore, handing out full instances to individual developers is not practical most often.

Interactive remote development environments are also facilitated by code editors, such as VSCode, which provide a modern GUI based experience on the developer’s local machine while having source code files and terminal sessions for testing and debugging in the remote environment context.

This post will demonstrate how EC2 Mac instances can be setup as remote development servers that can be accessed by multiple developers concurrently in order to compile and run their code interactively via command line access. The proposed setup features centralized user management based on AWS Directory Service and shared network storage utilizing Amazon Elastic File System (Amazon EFS), thereby decoupling those aspects from the development server instances. As a result, new instances can easily be added when needed, and existing instances can be updated to the newest OS and development toolchain version without affecting developer workflow.

Architecture

The following diagram shows the architecture rolled out in the context of this blog.

Architecture Diagram. A detailed description is featured in the blog text.

Compute Layer

The compute layer consists of two EC2 Mac instances running in isolated private subnets in different Availability Zones. In a production setup, these instances are provisioned with every necessary tool and software needed by developers to build and test their code for Apple platforms. Provisioning can be accomplished by creating custom Amazon Machine Images (AMIs) for the EC2 Mac instances or by bootstrapping them with setup scripts. This post utilizes Amazon provided AMIs with macOS BigSur without custom software. Once setup, developers gain command line access to the instances via SSH and utilize them as remote development environments.

Storage Layer

The architecture promotes the decoupling of compute and storage so that EC2 Mac instances can be updated with new OS and/or software versions without affecting the developer experience or data. Home directories reside on a highly available Amazon EFS file system, and they can be consistently accessed from all EC2 Mac instances. From a user perspective, any two EC2 Mac instances are alike, in that the user experiences the same configuration and environment (e.g., shell configurations such as .zshrc, VSCode remote extensions .vscode-server, or other tools and configurations installed within the user’s home directory). The file system is exposed to the private subnets via redundant mount target ENIs and persistently mounted on the Mac instances.

Identity Layer

For centralized user and access management, all instances in the architecture are part of a common Active Directory domain based on AWS Managed Microsoft AD. This is exposed via redundant ENIs to the private subnets containing the Mac instances.

To manage and configure the Active Directory domain, a Windows Instance (MGMT01) is deployed. For this post, we will connect to this instance for setting up Active Directory users. Note: other than that, this instance is not required for operating the solution, and it can be shut down both for reasons of cost efficiency and security.

Access Layer

The access layer constitutes the entry and exit point of the setup. For this post, it is comprised of an internet-facing bastion host connecting authorized Active Directory users to the Mac instances, as well as redundant NAT gateways providing outbound internet connectivity.

Depending on customer requirements, the access layer can be realized in various ways. For example, it can provide access to customer on-premises networks by using AWS Direct Connect or AWS Virtual Private Network (AWS VPN), or to services in different Virtual Private Cloud (VPC) networks by using AWS PrivateLink. This means that you can integrate your Mac development environment with pre-existing development-related services, such as source code and software repositories or build and test services.

Prerequisites

We utilize AWS CloudFormation to automatically deploy the entire setup in the preceding description. All templates and code can be obtained from the blog’s GitHub repository. To complete the setup, you need

Warning: Deploying this example will incur AWS service charges of at least $50 due to the fact that EC2 Mac instances can only be released 24 hours after allocation.

Solution Deployment

In this section, we provide a step-by-step guide for deploying the solution. We will mostly rely on AWS CLI and shell scripts provided along with the CloudFormation templates and use the AWS Management Console for checking and verification only.

1. Get the Code: Obtain the CloudFormation templates and all relevant scripts and assets via git:

git clone https://github.com/aws-samples/ec2-mac-remote-dev-env.git
cd ec2-mac-remote-dev-env
git submodule init 
git submodule update

2. Create an Amazon Simple Storage Service (Amazon S3) deployment bucket and upload assets for deployment: CloudFormation templates and other assets are uploaded to this bucket in order to deploy them. To achieve this, run the upload.sh script in the repository root, accepting the default bucket configuration as suggested by the script:

./upload.sh

3. Create an SSH Keypair for admin Access: To access the instances deployed by CloudFormation, create an SSH keypair with name mac-admin, and then import it with EC2:

ssh-keygen -f ~/.ssh/mac-admin
aws ec2 import-key-pair \
    --key-name "mac-admin" \
    --public-key-material fileb://~/.ssh/mac-admin.pub

4. Create CloudFormation Parameters file: Initialize the json file by copying the provided template parameters-template.json :

cp parameters-template.json parameters.json

Substitute the following placeholders:

a. <YourS3BucketName>: The unique name of the S3 bucket you created in step 2.

b. <YourSecurePassword>: Active Directory domain admin password. This must be 8-32 characters long and can contain numbers, letters and symbols.

c. <YourMacOSAmiID>: We used the latest macOS BigSur AMI at the time of writing with AMI ID ami-0c84d9da210c1110b in the us-east-2 Region. You can obtain other AMI IDs for your desired AWS Region and macOS version from the console.

d. <MacHost1ID> and <MacHost2ID>: See the next step 5. on how to allocate Dedicated Hosts and obtain the host IDs.

5. Allocate Dedicated Hosts: EC2 Mac Instances run on Dedicated Hosts. Therefore, prior to being able to deploy instances, Dedicated Hosts must be allocated. We utilize us-east-2 as the target Region, and we allocate the hosts in the Availability Zones us-east-2b and us-east-2c:

aws ec2 allocate-hosts \
    --auto-placement off \
    --region us-east-2 \
    --availability-zone us-east-2b \
    --instance-type mac1.metal \
    --quantity 1 \
    --tag-specifications 'ResourceType=dedicated-host,Tags=[{Key=Name,Value=MacHost1}]'

aws ec2 allocate-hosts \
    --auto-placement off \
    --region us-east-2 \
    --availability-zone us-east-2c \
    --instance-type mac1.metal \
    --quantity 1 \
    --tag-specifications 'ResourceType=dedicated-host,Tags=[{Key=Name,Value=MacHost2}]'

Substitute the host IDs returned from those commands in the parameters.json file as instructed in the previous step 5.

6. Deploy the CloudFormation Stack: To deploy the stack with the name ec2-mac-remote-dev-env, run the provided sh script as follows:

./deploy.sh ec2-mac-remote-dev-env

Stack deployment can take up to 1.5 hours, which is due to the Microsoft Managed Active Directory, the Windows MGMT01 instance, and the Mac instances being created sequentially. Check the CloudFormation Console to see whether the stack finished deploying. In the console, under Stacks, select the stack name from the preceding code (ec2-mac-remote-dev-env), and then navigate to the Outputs Tab. Once finished, this will display the public DNS name of the bastion host, as well as the private IPs of the Mac instances. You need this information in the upcoming section in order to connect and test your setup.

Solution Test

Now you can log in and explore the setup. We will start out by creating a developer account within Active Directory and configure an SSH key in order for it to grant access.

Create an Active Directory User

Create an SSH Key for the Active Directory User and configure SSH Client

First, we create a new SSH key for the developer Active Directory user. Utilize OpenSSH CLI,

ssh-keygen -f ~/.ssh/mac-developer

Furthermore, utilizing the connection information from the CloudFormation output, setup your ~/.ssh/config to contain the following entries, where $BASTION_HOST_PUBLIC_DNS, $MAC1_PRIVATE_IP and $MAC2_PRIVATE_IP must be replaced accordingly:

Host bastion
  HostName $BASTION_HOST_PUBLIC_DNS
  User ec2-user
  IdentityFile ~/.ssh/mac-admin

Host bastion-developer
  HostName $BASTION_HOST_PUBLIC_DNS
  User developer
  IdentityFile ~/.ssh/mac-developer

Host macos1
  HostName $MAC1_PRIVATE_IP
  ProxyJump %r@bastion-developer
  User developer
  IdentityFile ~/.ssh/mac-developer

Host macos2
  HostName $MAC2_PRIVATE_IP
  ProxyJump %r@bastion-developer
  User developer
  IdentityFile ~/.ssh/mac-developer

As you can see from this configuration, we set up both SSH keys created during this blog. The mac-admin key that you created earlier provides access to the privileged local ec2-user account, while the mac-developer key that you just created grants access to the unprivileged AD developer account. We will create this next.

Login to the Windows MGMT Instance and setup a developer Active Directory account

Now login to the bastion host, forwarding port 3389 to the MGMT01 host in order to gain Remote Desktop Access to the Windows management instance:

ssh -L3389:mgmt01:3389 bastion

While having this connection open, launch your Remote Desktop Client and connect to localhost with Username admin and password as specified earlier in the CloudFormation parameters. Once connected to the instance, open Control Panel>System and Security>Administrative Tools and click Active Directory Users and Computers. Then, in the appearing window, enable View>Advanced Features. If you haven’t changed the Active Directory domain name explicitly in CloudFormation, then the default domain name is example.com with corresponding NetBIOS Name example. Therefore, to create a new user for that domain, select Active Directory Users and Computers>example.com>example>Users, and click Create a new User. In the resulting wizard, set the Full name and User logon name fields to developer, and proceed to set a password to create the user. Once created, right-click on the developer user, and select Properties>Attribute Editor. Search for the altSecurityIdentities property, and copy-paste the developer public SSH key (contained in ~/.ssh/mac-developer.pub) into the Value to add field, click Add, and then click OK. In the Properties window, save your changes by clicking Apply and OK. The following figure illustrates the process just described:

Screenshot from the Windows Management instance depicting the creation of the Active Directory user. A detailed description of this process is contained in the blog text.

Connect to the EC2 Mac instances

Now that the developer account is setup, you can connect to either of the two EC2 Mac instances from your local machine with the Active Directory account:

ssh macos1

When you connect via the preceding command, your local machine first establishes an SSH connection to the bastion host which authorizes the request against the key we just stored in Active Directory. Upon success, the bastion host forwards the connection to the macos1 instance, which again authorizes against Active Directory and launches a  terminal session upon success. The following figure illustrates the login with the macos1 instances, showcasing both the integration with AD (EXAMPLE\Domain Users group membership) as well as with the EFS share, which is mounted at /opt/nfsshare and symlinked to the developer’s home directory.

Screenshot from a terminal window after logging into the macos1 instance. Instructions for doing this are included in the blog text.

Likewise, you can create folders and files in the developer’s home directory such as the test-project folder depicted in the screenshot.

Lastly, let’s utilize VS Code’s remote plugin and connect to the other macos2 instance. Select the Remote Explorer on the left-hand pane and click to open the macos2 host as shown in the following screenshot:

Screenshot depicting how to connect to the macos2 instance using the VSCode Remote SSH extension.

A new window will be opened with the context of the remote server, as shown in the next figure. As you can see, we have access to the same files seen previously on the macos1 host.

Screenshot showing VSCode UI once connected to the macos2 instance.

Cleanup

From the repository root, run the provided destroy.sh script in order to destroy all resources created by CloudFormation, specifying the stack name as input parameter:

./destroy.sh ec2-mac-remote-dev-env

Check the CloudFormation Console to confirm that the stack and its resources are properly deleted.

Lastly, in the EC2 Console, release the dedicated Mac Hosts that you allocated in the beginning. Notice that this is only possible 24 hours after allocation.

Summary

This post has shown how EC2 Mac instances can be set up as remote development environments, thereby allowing developers to create software for Apple platforms regardless of their local hardware and software setup. Aside from increased flexibility and maintainability, this setup also saves cost because multiple developers can work interactively with the same EC2 Mac instance. We have rolled out an architecture that integrates EC2 Mac instances with AWS Directory Services for centralized user and access management as well as Amazon EFS to store developer home directories in a durable and highly available manner. This has resulted in an architecture where instances can easily be added, removed, or updated without affecting developer workflow. Now, irrespective of your client machine, you are all set to start coding with your local editor while leveraging EC2 Mac instances in the AWS Cloud to provide you with a macOS environment! To get started and learn more about EC2 Mac instances, please visit the product page.

Modernizing deployments with container images in AWS Lambda

Post Syndicated from Eric Johnson original https://aws.amazon.com/blogs/compute/modernizing-deployments-with-container-images-in-aws-lambda/

This post is written by Joseph Keating, AWS Modernization Architect, and Virginia Chu, Sr. DevSecOps Architect.

Container image support for AWS Lambda enables developers to package function code and dependencies using familiar patterns and tools. With this pattern, developers use standard tools like Docker to package their functions as container images and deploy them to Lambda.

In a typical deployment process for image-based Lambda functions, the container and Lambda function are created or updated in the same process. However, some use cases require developers to create the image first, and then update one or more Lambda functions from that image. In these situations, organizations may mandate that infrastructure components such as Amazon S3 and Amazon Elastic Container Registry (ECR) are centralized and deployed separately from their application deployment pipelines.

This post demonstrates how to use AWS continuous integration and deployment (CI/CD) services and Docker to separate the container build process from the application deployment process.

Overview

There is a sample application that creates two pipelines to deploy a Java application. The first pipeline uses Docker to build and deploy the container image to the Amazon ECR. The second pipeline uses AWS Serverless Application Model (AWS SAM) to deploy a Lambda function based on the container from the first process.

This shows how to build, manage, and deploy Lambda container images automatically with infrastructure as code (IaC). It also covers automatically updating or creating Lambda functions based on a container image version.

Example architecture

Example architecture

The example application uses AWS CloudFormation to configure the AWS Lambda container pipelines. Both pipelines use AWS CodePipeline, AWS CodeBuild, and AWS CodeCommit. The lambda-container-image-deployment-pipeline builds and deploys a container image to ECR. The sam-deployment-pipeline updates or deploys a Lambda function based on the new container image.

The pipeline deploys the sample application:

  1. The developer pushes code to the main branch.
  2. An update to the main branch invokes the pipeline.
  3. The pipeline clones the CodeCommit repository.
  4. Docker builds the container image and assigns tags.
  5. Docker pushes the image to ECR.
  6. The lambda-container-image-pipeline completion triggers an Amazon EventBridge event.
  7. The pipeline clones the CodeCommit repository.
  8. AWS SAM builds the Lambda-based container image application.
  9. AWS SAM deploys the application to AWS Lambda.

Prerequisites

To provision the pipeline deployment, you must have the following prerequisites:

Infrastructure configuration

The pipeline relies on infrastructure elements like AWS Identity and Access Management roles, S3 buckets, and an ECR repository. Due to security and governance considerations, many organizations prefer to keep these infrastructure components separate from their application deployments.

To start, deploy the core infrastructure components using CloudFormation and the AWS CLI:

  1. Create a local directory called BlogDemoRepo and clone the source code repository found in the following location: 
    mkdir -p $HOME/BlogDemoRepo
cd $HOME/BlogDemoRepo
git clone https://github.com/aws-samples/modernize-deployments-with-container-images-in-lambda
  2. Change directory into the cloned repository: 
    cd modernize-deployments-with-container-images-in-lambda/
  3. Deploy the s3-iam-config CloudFormation template, keeping the following CloudFormation template names: 
    aws cloudformation create-stack \
  --stack-name s3-iam-config \
  --template-body file://templates/s3-iam-config.yml \
  --parameters file://parameters/s3-iam-config-params.json \
  --capabilities CAPABILITY_NAMED_IAM

    The output should look like the following:

    Output example for stack creation

    Output example for stack creation

Application overview

The application uses Docker to build the container image and an ECR repository to store the container image. AWS SAM deploys the Lambda function based on the new container.

The example application in this post uses a Java-based container image using Amazon Corretto. Amazon Corretto is a no-cost, multi-platform, production-ready Open Java Development Kit (OpenJDK).

The Lambda container-image base includes the Amazon Linux operating system, and a set of base dependencies. The image also consists of the Lambda Runtime Interface Client (RIC) that allows your runtime to send and receive to the Lambda service. Take some time to review the Dockerfile and how it configures the Java application.

Configure the repository

The CodeCommit repository contains all of the configurations the pipelines use to deploy the application. To configure the CodeCommit repository:

  1. Get metadata about the CodeCommit repository created in a previous step. Run the following command from the BlogDemoRepo directory created in a previous step: 
    aws codecommit get-repository \
  --repository-name DemoRepo \
  --query repositoryMetadata.cloneUrlHttp \
  --output text

    The output should look like the following:

    Output example for get repository

    Output example for get repository

  2. In your terminal, paste the Git URL from the previous step and clone the repository: 
    git clone <insert_url_from_step_1_output>

    You receive a warning because the repository is empty.

    Empty repository warning

    Empty repository warning

  3. Create the main branch: 
    cd DemoRepo
git checkout -b main
  4. Copy all of the code from the cloned GitHub repository to the CodeCommit repository: 
    cp -r ../modernize-deployments-with-container-images-in-lambda/* .
  5. Commit and push the changes: 
    git add .
git commit -m "Initial commit"
git push -u origin main

Pipeline configuration

This example deploys two separate pipelines. The first is called the modernize-deployments-with-container-images-in-lambda, which consists of building and deploying a container-image to ECR using Docker and the AWS CLI. An EventBridge event starts the pipeline when the CodeCommit branch is updated.

The second pipeline, sam-deployment-pipeline, is where the container image built from lambda-container-image-deployment-pipeline is deployed to a Lambda function using AWS SAM. This pipeline is also triggered using an Amazon EventBridge event. Successful completion of the lambda-container-image-deployment-pipeline invokes this second pipeline through Amazon EventBridge.

Both pipelines consist of AWS CodeBuild jobs configured with a buildspec file. The buildspec file enables developers to run bash commands and scripts to build and deploy applications.

Deploy the pipeline

You now configure and deploy the pipelines and test the configured application in the AWS Management Console.

  1. Change directory back to modernize-serverless-deployments-leveraging-lambda-container-images directory and deploy the lambda-container-pipeline CloudFormation Template: 
    cd $HOME/BlogDemoRepo/modernize-deployments-with-container-images-in-lambda/
aws cloudformation create-stack \
  --stack-name lambda-container-pipeline \
  --template-body file://templates/lambda-container-pipeline.yml \
  --parameters file://parameters/lambda-container-params.json  \
  --capabilities CAPABILITY_IAM \
  --region us-east-1

    The output appears:

    Output example for stack creation

    Output example for stack creation

  2. Wait for the lambda-container-pipeline stack from the previous step to complete and deploy the sam-deployment-pipeline CloudFormation template: 
    aws cloudformation create-stack \
  --stack-name sam-deployment-pipeline \
  --template-body file://templates/sam-deployment-pipeline.yml \
  --parameters file://parameters/sam-deployment-params.json  \
  --capabilities CAPABILITY_IAM \
  --region us-east-1

    The output appears:

    Output example of stack creation

    Output example of stack creation

  3. In the console, select CodePipeline → pipelines:

  4. Wait for the status of both pipelines to show Succeeded:
  5. Navigate to the ECR console and choose demo-java. This shows that the pipeline is built and the image is deployed to ECR.
  6. Navigate to the Lambda console and choose the MyCustomLambdaContainer function.
  7. The Image configuration panel shows that the function is configured to use the image created earlier.
  8. To test the function, choose Test.
  9. Keep the default settings and choose Test.

This completes the walkthrough. To further test the workflow, modify the Java application and commit and push your changes to the main branch. You can then review the updated resources you have deployed.

Conclusion

This post shows how to use AWS services to automate the creation of Lambda container images. Using CodePipeline, you create a CI/CD pipeline for updates and deployments of Lambda container-images. You then test the Lambda container-image in the AWS Management Console.

For more serverless content visit Serverless Land.

Is Microsoft Stealing People’s Bookmarks?

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/11/is-microsoft-stealing-peoples-bookmarks.html

I received email from two people who told me that Microsoft Edge enabled synching without warning or consent, which means that Microsoft sucked up all of their bookmarks. Of course they can turn synching off, but it’s too late.

Has this happened to anyone else, or was this user error of some sort? If this is real, can some reporter write about it?

(Not that “user error” is a good justification. Any system where making a simple mistake means that you’ve forever lost your privacy isn’t a good one. We see this same situation with sharing contact lists with apps on smartphones. Apps will repeatedly ask, and only need you to accidentally click “okay” once.)

EDITED TO ADD: It’s actually worse than I thought. Edge urges users to store passwords, ID numbers, and even passport numbers, all of which get uploaded to Microsoft by default when synch is enabled.

Wire Fraud Scam Upgraded with Bitcoin

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/11/wire-fraud-scam-upgraded-with-bitcoin.html

The FBI has issued a bulletin describing a bitcoin variant of a wire fraud scam:

As the agency describes it, the scammer will contact their victim and somehow convince them that they need to send money, either with promises of love, further riches, or by impersonating an actual institution like a bank or utility company. After the mark is convinced, the scammer will have them get cash (sometimes out of investment or retirement accounts), and head to an ATM that sells cryptocurrencies and supports reading QR codes. Once the victim’s there, they’ll scan a QR code that the scammer sent them, which will tell the machine to send any crypto purchased to the scammer’s address. Just like that, the victim loses their money, and the scammer has successfully exploited them.

[…]

The “upgrade” (as it were) for scammers with the crypto ATM method is two-fold: it can be less friction than sending a wire transfer, and at the end the scammer has cryptocurrency instead of fiat. With wire transfers, you have to fill out a form, and you may give that form to an actual person (who could potentially vibe check you). Using the ATM method, there’s less time to reflect on the fact that you’re about to send money to a stranger. And, if you’re a criminal trying to get your hands on Bitcoin, you won’t have to teach your targets how to buy coins on the internet and transfer them to another wallet — they probably already know how to use an ATM and scan a QR code.