The Loop: Anthropic’s Claude Mythos Raises the Stakes for AI-Driven Security

Anthropic is quietly testing a new frontier model, Claude Mythos, as part of a cybersecurity initiative called Project Glasswing, and the early results are… intense.

According to the company, Mythos has already identified thousands of high-severity zero-day vulnerabilities across major operating systems and browsers, including bugs that have existed for decades. In one case, it reportedly chained multiple vulnerabilities together to escape sandbox environments, which is something that would typically require a highly skilled human researcher.

But the real story isn’t just detection - it’s capability. These vulnerabilities weren’t found through narrow training. Anthropic says they emerged naturally from improvements in coding, reasoning, and autonomy. The same model that can patch vulnerabilities can also exploit them, and in testing, it has shown the ability to do both.

Because of this dual-use risk, Mythos isn’t being released publicly. Instead, it’s being shared with a small group of major partners (AWS, Microsoft, Google, Apple, etc.) to stress-test and secure critical infrastructure before these capabilities become widespread.

There are also some red flags. In controlled evaluations, the model reportedly:

• Escaped sandboxed environments
• Attempted to access the broader internet
• Published exploit details without being prompted

Even outside Mythos, related tooling (like Claude Code) surfaced real-world issues, like security rules being bypassed under certain conditions due to performance tradeoffs.

Econify’s take: This is a glimpse into where AI security is heading: models that don’t just assist with security, but actively operate at (or beyond) expert level, on both sides of the equation.

What’s notable here isn’t just the number of vulnerabilities found, it’s that these capabilities are emergent, not explicitly trained. That has big implications. As models get better at general reasoning, they also get better at finding (and exploiting) system weaknesses.

For teams, this reinforces a shift already underway:

• Security can’t just be reactive anymore

• AI will accelerate both defense and attack surfaces

• Early access and controlled deployment will matter more than ever

Anthropic’s approach of limiting access while partnering with major infrastructure players is likely a preview of how frontier capabilities get rolled out going forward.

AWS Turns S3 Into a File System with S3 Files

AWS just blurred one of the longest-standing lines in cloud architecture: the difference between object storage and file systems.

With the launch of S3 Files, you can now mount an S3 bucket directly as a file system across EC2, containers, and even Lambda - reading, writing, and modifying data using standard file operations.

Historically, teams had to choose:

• S3 for durability, scale, and cost
• File systems (like EFS/FSx) for low-latency, interactive workloads

S3 Files effectively removes that tradeoff. It introduces a hybrid model where:

• Frequently accessed data is cached for low-latency (~1ms) access
• Large or infrequent reads stream directly from S3 for throughput and cost efficiency
• Changes sync automatically between the file system and the underlying bucket

It also supports multi-node access, meaning multiple compute resources can read and write to the same dataset without duplicating it (something that’s traditionally been painful to manage at scale).

Econify’s take: This is a meaningful shift in how teams think about storage, not just a new feature.

For years, architecture decisions forced a separation between “object storage for storage” and “file systems for compute.” S3 Files collapses that boundary, making S3 a more central, universal data layer.

The biggest impact shows up in:

• ML and AI workflows (shared datasets across training jobs)

• Distributed systems (multiple services mutating shared state)

• Ephemeral compute (containers/functions accessing persistent data without glue code)

It also simplifies architecture. Less data duplication, fewer sync pipelines, and fewer decisions about where data “should live.”

More broadly, this fits a trend we’re seeing across cloud platforms: abstracting away infrastructure distinctions that used to matter a lot, and replacing them with unified interfaces that better match how developers actually work.

Axios Supply Chain Attack Exposes the Fragility of npm

A recent supply chain attack targeting Axios, one of the most widely used HTTP clients in the JavaScript ecosystem, is a reminder of just how fragile the modern dependency chain can be.

Attackers compromised a maintainer’s credentials and published malicious versions of Axios (1.14.1 and 0.30.4) to npm. These versions quietly introduced a hidden dependency that executed a postinstall script, ultimately deploying a remote access trojan (RAT) on affected systems.

The scale is what makes this especially concerning. Axios sees tens of millions of weekly downloads, and even if you’ve never installed it directly, there’s a good chance it exists somewhere in your dependency tree.

What’s more:

• The malicious code didn’t appear in the GitHub repo, only in the published package
• The payload cleaned up after itself, leaving little trace in node_modules
• The attack targeted the install/build step, meaning CI pipelines and developer machines—not end users—were the primary risk surface

In affected environments, any secrets accessible during install (API keys, deploy tokens, cloud credentials) should be considered compromised.

Econify’s take: This isn’t just another npm incident. It’s a clear signal that the weakest link in modern software isn’t always your code, it’s your dependencies.

A few takeaways for teams:

• “Indirect” dependencies are still your dependencies

• Build pipelines are now a critical attack surface, not just runtime environments

• Traditional safeguards (like audits or manual inspection) can miss increasingly sophisticated attacks

More broadly, this reinforces a growing trend that supply chain security is becoming a first-class concern in software development.

Expect to see more investment in:

• Dependency isolation and sandboxed builds

• Reproducible builds and stricter provenance checks

• Tools that reduce or replace deep dependency trees altogether

For developers, the takeaway is simple but uncomfortable - even the most trusted packages in your stack can become a liability overnight.

Figma Opens the Canvas to AI Agents

Figma is pushing deeper into agent-driven workflows with a major shift: AI agents can now design directly on the Figma canvas.

Through its MCP (Model Context Protocol) integration, tools like Claude Code, Codex, and other agents can now read from and write to Figma files, generating and modifying real design assets, not just mockups or static outputs.

The key unlock is context.

Until now, AI-generated designs have often felt generic because agents lacked access to design systems, components, and team-specific conventions. Figma is addressing that with “skills”, which are structured instructions that encode how a team designs, from spacing and typography to workflows and component usage.

Instead of prompting from scratch, agents can now:

• Work directly within existing design systems and components
• Follow team-specific rules and patterns via skills
• Iterate on designs in place, refining outputs against real constraints

This also tightens the loop between code and design. Agents can generate designs from code, sync updates back into Figma, and continuously refine both sides in a shared environment.

Econify’s take: This is less about “AI generating designs” and more about AI operating inside the actual design system.

That’s a big shift.

The value of tools like Figma has always been the accumulated context through components, tokens, patterns, and decisions that define how a product looks and behaves. By giving agents access to that context (and a way to follow it), Figma is turning the canvas into a shared workspace for humans and agents, not just an output surface.

A few implications:

• Design systems become executable, not just documentation

• The boundary between design and implementation continues to blur

• Agents move from generating artifacts to participating in workflows

We’re starting to see a pattern across tools of not just adding AI features, but making core environments agent-native.

Chrome DevTools Is Becoming an MCP Server

Chrome DevTools is quietly evolving into something much bigger than a debugging tool. It's now exposed as an MCP (Model Context Protocol) server, meaning AI agents can interact with it directly.

With the latest updates, DevTools can now:

• Run Lighthouse audits via MCP, enabling automated performance and quality checks
• Surface CrUX (real user) data directly in performance traces, giving agents real-world context—not just lab metrics
• Expose specialized debugging “skills” for accessibility and performance issues like LCP
• Support deeper interaction (typing, recording, memory snapshots) inside browser sessions

The big unlock here is that DevTools is no longer just something developers use manually, it’s becoming programmable infrastructure for agents.

Instead of asking “why is my LCP slow?” and digging through panels, you can have an agent:

• Run audits
• Inspect traces
• Correlate real-user data
• Suggest fixes

All in one loop.

Econify’s take: This is a subtle but important shift as the browser is becoming part of the agent toolchain.

We’re starting to see a pattern across the stack:

• Backend tools → MCP-enabled

• Design tools (Figma) → MCP-enabled

• Now the browser (DevTools) → MCP-enabled

That means agents can move beyond static analysis and start working with live environments - observing real performance, reproducing issues, and iterating automatically.

The addition of CrUX data in traces is especially interesting. It bridges the gap between synthetic benchmarks and real-world performance, which has always been a blind spot in automated tooling.