Introducing Anthropic’s Claude Opus 4.7 model in Amazon Bedrock

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Today, we’re announcing Claude Opus 4.7 in Amazon Bedrock, Anthropic’s most intelligent Opus model for advancing performance across coding, long-running agents, and professional work.

Claude Opus 4.7 is powered by Amazon Bedrock’s next generation inference engine, delivering enterprise-grade infrastructure for production workloads. Bedrock’s new inference engine has brand-new scheduling and scaling logic which dynamically allocates capacity to requests, improving availability particularly for steady-state workloads while making room for rapidly scaling services. It provides zero operator access—meaning customer prompts and responses are never visible to Anthropic or AWS operators—keeping sensitive data private.

According to Anthropic, Claude Opus 4.7 model provides improvements across the workflows that teams run in production such as agentic coding, knowledge work, visual understanding,long-running tasks. Opus 4.7 works better through ambiguity, is more thorough in its problem solving, and follows instructions more precisely.

  • Agentic coding: The model extends Opus 4.6’s lead in agentic coding, with stronger performance on long-horizon autonomy, systems engineering, and complex code reasoning tasks. According to Anthropic, the model records high-performance scores with 64.3% on SWE-bench Pro, 87.6% on SWE-bench Verified, and 69.4% on Terminal-Bench 2.0.
  • Knowledge work: The model advances professional knowledge work, with stronger performance on document creation, financial analysis, and multi-step research workflows. The model reasons through underspecified requests, making sensible assumptions and stating them clearly, and self-verifies its output to improve quality on the first step. According to Anthropic, the model reaches 64.4% on Finance Agent v1.1.
  • Long-running tasks: The model stays on track over longer horizons, with stronger performance over its full 1M token context window as it reasons through ambiguity and self-verifies its output.
  • Vision: the model adds high-resolution image support, improving accuracy on charts, dense documents, and screen UIs where fine detail matters.

The model is an upgrade from Opus 4.6 but may require prompting changes and harness tweaks to get the most out of the model. To learn more, visit Anthropic’s prompting guide.

Claude Opus 4.7 model in action
You can get started with Claude Opus 4.7 model in Amazon Bedrock console. Choose Playground under Test menu and choose Claude Opus 4.7 when you select model. Now, you can test your complex coding prompt with the model.

I run the following prompt example about technical architecture decision:
Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions.

You can also access the model programmatically using the Anthropic Messages API to call the bedrock-runtime through Anthropic SDK or bedrock-mantle endpoints, or keep using the Invoke and Converse API on bedrock-runtime through the AWS Command Line Interface (AWS CLI) and AWS SDK.

To get started with making your first API call to Amazon Bedrock in minutes, choose Quickstart in the left navigation pane in the console. After choosing your use case, you can generate a short term API key to authenticate your requests as testing purpose.

When you choose the API method such as the OpenAI-compatible Responses API, you can get sample codes to run your prompt to make your inference request using the model.


To invoke the model through the Anthropic Claude Messages API, you can proceed as follows using anthropic[bedrock] SDK package for a streamlined experience:

from anthropic import AnthropicBedrockMantle
# Initialize the Bedrock Mantle client (uses SigV4 auth automatically)
mantle_client = AnthropicBedrockMantle(aws_region=REGION)
# Create a message using the Messages API
message = mantle_client.messages.create(
    model="anthropic.claude-opus-4-7",
    max_tokens=2048,
    messages=[ 
	    {"role": "user", "content": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions"}
    ]
)
print(message.content[0].text)

You can also run the following command to invoke the model directly to bedrock-runtime endpoint using the AWS CLI and the Invoke API:

aws bedrock-runtime invoke-model  
 --model-id anthropic.claude-opus-4-7  
 --region us-east-1  
 --body '{"messages": [{"role": "user", "content": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions."}], "max_tokens": 512, "temperature": 0.5, "top_p": 0.9}'  
 --cli-binary-format raw-in-base64-out  
invoke-model-output.txt

For more intelligent reasoning capability, you can use Adaptive thinking with Claude Opus 4.7, which lets Claude dynamically allocate thinking token budgets based on the complexity of each request.

To learn more, visit the Anthropic Claude Messages API and check out code examples for multiple use cases and a variety of programming languages.

Things to know
Let me share some important technical details that I think you’ll find useful.

  • Choosing APIs: You can choose from a variety of Bedrock APIs for model inference, as well as the Anthropic Messages API. The Bedrock-native Converse API supports multi-turn conversations and Guardrails integration. The Invoke API provides direct model invocation and lowest-level control.
  • Scaling and capacity: Bedrock’s new inference engine is designed to rapidly provision and serve capacity across many different models. When accepting requests, we prioritize keeping steady state workloads running, and ramp usage and capacity rapidly in response to changes in demand. During periods of high demand, requests are queued, rather than rejected. Up to 10,000 requests per minute (RPM) per account per Region are available immediately, with more available upon request.

Now available
Anthropic’s Claude Opus 4.7 model is available today in the US East (N. Virginia), Asia Pacific (Tokyo), Europe (Ireland), and Europe (Stockholm) Regions; check the full list of Regions for future updates. To learn more, visit the Claude by Anthropic in Amazon Bedrock page and the Amazon Bedrock pricing page.

Give Anthropic’s Claude Opus 4.7 a try in the Amazon Bedrock console today and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

Channy

AWS Weekly Roundup: Claude Mythos Preview in Amazon Bedrock, AWS Agent Registry, and more (April 13, 2026)

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In my last Week in Review post, I mentioned how much time I’ve been spending on AI-Driven Development Lifecycle (AI-DLC) workshops with customers this year. A common theme in those sessions is the need for better cost visibility. Teams are moving fast with AI, but as they go from experimenting to full production, finance and leadership really need to know who is using which resources and at what cost. That’s why I was so excited to see the launch of Amazon Bedrock new support for cost allocation by IAM user and role this week. This lets you tag IAM principals with attributes like team or cost center and then activate those tags in your Billing and Cost Management console. The resulting cost data flows into AWS Cost Explorer and the detailed Cost and Usage Report, giving you a clear line of sight into model inference spending. Whether you’re scaling agents across teams, tracking foundation model use by department, or running tools like Claude Code on Amazon Bedrock, this new feature is a game changer for tracking and managing your AI investments. You can get all the details on setting this up in the IAM principal cost allocation documentation.

Now, let’s get into this week’s AWS news…

Headlines
Amazon Bedrock now offers Claude Mythos Preview Anthropic’s most sophisticated AI model to date is now available on Amazon Bedrock as a gated research preview through Project Glasswing. Claude Mythos introduces a new model class focused on cybersecurity, capable of identifying sophisticated security vulnerabilities in software, analyzing large codebases, and delivering state of the art performance across cybersecurity, coding, and complex reasoning tasks. Security teams can use it to discover and address vulnerabilities in critical software before threats emerge. Access is currently limited to allowlisted organizations, with Anthropic and AWS prioritizing internet critical companies and open source maintainers.

AWS Agent Registry for centralized agent discovery and governance now in preview AWS launched Agent Registry through Amazon Bedrock AgentCore, providing organizations with a private catalog for discovering and managing AI agents, tools, skills, MCP servers, and custom resources. The registry helps teams locate existing capabilities rather than duplicating them, with semantic and keyword search, approval workflows, and CloudTrail audit trails. It is accessible via the AgentCore Console, AWS CLI, SDK, and as an MCP server queryable from IDEs.

Last week’s launches
Here are some launches and updates from this past week that caught my attention:

  • Announcing Amazon S3 Files, making S3 buckets accessible as file systems — Amazon S3 Files transforms S3 buckets into shared file systems that connect any AWS compute resource directly with your S3 data. Built on Amazon EFS technology, it delivers full file system semantics with low latency performance, caching actively used data and providing multiple terabytes per second of aggregate read throughput. Applications can access S3 data through both file system and S3 APIs simultaneously without code modifications or data migration.
  • Amazon OpenSearch Service supports Managed Prometheus and agent tracing —Amazon OpenSearch Service now provides a unified observability platform that consolidates metrics, logs, traces, and AI agent tracing into a single interface. The update includes native Prometheus integration with direct PromQL query support, RED metrics monitoring, and OpenTelemetry GenAI semantic convention support for LLM execution visibility. Operations teams can correlate slow traces to logs and overlay Prometheus metrics on dashboards without switching between tools.
  • Amazon WorkSpaces Advisor now available for AI powered troubleshooting— AWS launched Amazon WorkSpaces Advisor, an AI powered administrative tool that uses generative AI to help IT administrators troubleshoot Amazon WorkSpaces Personal deployments. It analyzes WorkSpace configurations, detects problems automatically, and provides actionable recommendations to restore service and optimize performance.
  • Amazon Braket adds support for Rigetti’s 108 qubit Cepheus QPU — Amazon Braket now offers access to Rigetti’s Cepheus-1-108Q device, the first 100+ qubit superconducting quantum processor on the platform. The modular design features twelve 9 qubit chiplets with CZ gates that offer enhanced resilience to phase errors. It supports multiple frameworks including Braket SDK, Qiskit, CUDA-Q, and Pennylane, with pulse level control for researchers.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news
Here are some additional posts and resources that you might find interesting:

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

  • What’s Next with AWS (April 28, Virtual) Join this livestream at 9am PT for a candid discussion about how agentic AI is transforming how businesses operate. Featuring AWS CEO Matt Garman, SVP Colleen Aubrey, and OpenAI leaders discussing emerging agent capabilities, Amazon’s internal experiences, and new agentic solutions and platform capabilities.

Browse here for upcoming AWS led in person and virtual events, startup events, and developer focused events.


That’s all for this week. Check back next Monday for another Weekly Roundup!

~ micah

Scans for EncystPHP Webshell, (Mon, Apr 13th)

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Last week, I wrote about attackers scanning for various webshells, hoping to find some that do not require authentication or others that use well-known credentials. But some attackers are paying attention and are deploying webshells with more difficult-to-guess credentials. Today, I noticed some scans for what appears to be the "EncystPHP" web shell. Fortinet wrote about this webshell back in January. It appears to be a favorite among attackers compromising vulnerable FreePBX systems.

PowerShell MSI package deprecation and preview updates

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Beginning with PowerShell 7.7-preview.1 (April 2026), the MSIX package will be the primary
installation method for PowerShell on Windows. We will no longer ship the MSI installer package for
new PowerShell releases.

For existing releases, including PowerShell 7.6, we will continue to provide MSI packages. However,
MSI isn’t planned for future releases, including PowerShell 7.7 GA and beyond.

Why we’re making this change

MSIX provides a modern installation and servicing model and is supported by Windows deployment
tools. It uses a declarative model that’s more predictable and reliable than MSI, which relies on
custom actions and scripts that can lead to inconsistent behavior. MSIX supports built-in update
mechanisms with differential updates. Microsoft is investing in improving MSIX.

MSI is a legacy technology. Servicing MSI installations requires external tooling and often results
in full reinstalls. MSI doesn’t meet modern accessibility requirements, particularly for screen
reader scenarios. To be accessible, MSI must present predictable tab stops and accurate
announcements for screen readers, which it doesn’t. Accessibility is a core requirement for
PowerShell.

This decision isn’t just about modernizing packaging for its own sake. It’s about ensuring that
PowerShell installations are modern and accessible for all users, now and in the future.

Looking forward

Our goal is to provide a fully accessible, reliable, and enterprise-ready installation experience.
At this time, MSIX doesn’t support all use case scenarios that MSI enabled, such as remoting and
execution by system-level services (like Task Scheduler). We recognize this gap and are actively
working to address it.

As part of this work, we’re investing in:

  • Improving MSIX support for system-level and enterprise deployment scenarios
  • Ensuring accessibility requirements are fully met across all installation paths
  • Providing clearer guidance and tooling for deployment at scale

We will continue to share updates as this work progresses.

Closing

We understand this change may require adjustments, especially in environments that rely heavily on
MSI-based deployment. We appreciate your patience as we make this transition.

Our focus is to ensure PowerShell remains accessible, predictable, and practical for all users.

— The PowerShell Team

The post PowerShell MSI package deprecation and preview updates appeared first on PowerShell Team.

Number Usage in Passwords: Take Two, (Thu, Apr 9th)

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In a previous diary [1], we looked to see how numbers were used within passwords submitted to honeypots. One of the items of interest was how dates, and more specifically years, were represented within the data and how that changed over time. It is often seen that years and seasons are used in passwords, especially when password change requirements include frequenty password changes. Some examples we might see today:

TeamPCP Supply Chain Campaign: Update 007 – Cisco Source Code Stolen via Trivy-Linked Breach, Google GTIG Tracks TeamPCP as UNC6780, and CISA KEV Deadline Arrives with No Standalone Advisory, (Wed, Apr 8th)

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This is the seventh update to the TeamPCP supply chain campaign threat intelligence report, "When the Security Scanner Became the Weapon" (v3.0, March 25, 2026). Update 006 covered developments through April 3, including the CERT-EU European Commission breach disclosure, ShinyHunters' confirmation of credential sharing, Sportradar breach details, and Mandiant's quantification of 1,000+ compromised SaaS environments. This update consolidates five days of intelligence from April 3 through April 8, 2026.

More Honeypot Fingerprinting Scans, (Wed, Apr 8th)

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One question that often comes up when I talk about honeypots: Are attackers able to figure out if they are connected to a honeypot? The answer is pretty simple: Yes!

Most "medium interaction" honeypots, like the one we are using, are just simulating various systems. These simulations are incomplete. For example, we are using the "Cowrie" honeypot to emulate SSH and telnet servers. Once an attacker is connected, any package they are installing will appear to install. In the past, I have written about attackers attempting to install bogus packages. If the install appears to succeed, the attacker knows they are connected to a honeypot. Some attackers look for SSH artifacts, such as the number and types of ciphers supported by SSH.

Today, I noticed one attacker, (IP address %%ip:45.135.194.48%%), using another common trick: Cowrie will often allow attackers to connect "randomly". The effect is that various username and password combinations appear to work. In this case, the attacker used usernames and passwords that are highly unlikely to work. If they succeed, they know they are connected to a honeypot. Here are some of the usernames and passwords used:

username password
admin definitely_not_valid_creds
honeypot indexer
honeypotter imaginegettingindexed
xXhoneypotXx P@ssw0rd1337!
youjustgotindexed getindexedretard

Will we do anything to block these types of requests? Maybe… I am not sure it is important enough to "hide" honeypots. One advantage we have is that many of our honeypots are connected to home networks with dynamic IPs. As a result, any IP address list an attacker will create is somewhat ephemeral. Secondly, we are mostly interested in internet-wide scans. We are not going to detect targeted attacks or zero days. 


Johannes B. Ullrich, Ph.D. , Dean of Research, SANS.edu
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