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Cloudsmith provides a control plane at the registry layer, enforcing policies against any ingested artifact to bolster your security posture. Here, we dig into the mechanics of the specific Cloudsmith capabilities that make enforcement at the registry work, and what makes each one meaningfully different from what you can do with a CI gate or a standalone scanner.

Policy management: Write policy against a breadth of risk signals

 is built on Open Policy Agent (OPA) and uses Rego as its written language. In Cloudsmith, the power of policy management comes from what you can write policy against.

Other enforcement approaches often rely on blunt, binary policies. This lack of granularity can result in blocking approved packages, which creates noise and disrupts developer workflows. For example, a binary control may block a package with a CVE score that exceeds a threshold. But CVE is only one of many potential threat signals. CVE severity tells you how bad a vulnerability 

 be. It doesn’t say anything about the likelihood of exploitability. Policy management lets you combine severity with EPSS scores. A package with a critical CVE with a 0.1% EPSS score and a package with a medium CVE currently being actively exploited are not equivalent risks, and your policies don't have to treat them as if they are.

Beyond vulnerability data, policies can evaluate against license type, upstream publish date, package age, custom metadata, and, for Docker images, SBOM contents. That constitutes a much broader range of signals for security teams to use when deciding whether a dependency belongs in their environment. Cloudsmith makes all that data available as policy inputs.

To aid with set-up, provide pre-configured policy templates covering the most common enforcement scenarios. Users can edit and amend these policies as their needs evolve. Legacy artifact management tools that claim similar policy-as-code capabilities added it by way of acquisition. This kind of bolt-on solution often forces users to manage admin configurations in multiple places. Cloudsmith policy management is native to the platform with all controls and options under the “Policies” tab. These policies also apply across Cloudsmith’s multi-format repos, so you can write a policy once and apply it globally.

Cooldown policies: Closing the remediation window

It’s impossible to predict what packages attackers will target next. So, when a new supply chain attack occurs against a package you depend on, what do you do in the window between when the attack begins and a CVE surfaces? That’s the most dangerous time because you may pull a compromised package without realizing it. Without a cooldown policy, that package enters your environment unchecked.

 address zero-day threats by hiding newly published packages from the index for a configurable period (e.g., 3-7 days). Threat researchers typically identify and report malicious packages within the first few hours or days after publication. The cooldown window provides a default buffer for threat researchers to catch up. If no advisory emerges during the cooldown window, the package releases into your environment once it ends. For security-focused teams managing active CVE incidents, this is one of the more operationally specific controls in the feature set; it targets the exposure window directly, rather than just blocking on severity at a static point in time.

Continuous risk detection: Enforcement that reflects current threat data

Vulnerabilities can go undetected for years after a package is published, or a dependency that was clean on Monday may have a critical CVE attached to it by Thursday. Regardless of how long a package has been in your private repository, you need a way to check it against current threat data.

. Rather than rescanning packages on a schedule, Cloudsmith extracts metadata from each package at sync time and continuously matches it against OSV.dev. When a new CVE, EPSS update, or malicious package record lands in the feed, and Cloudsmith finds a match with a package in your registry, this triggers your policies to re-evaluate the package and take the associated action, e.g., quarantine.

OSV.dev draws on a wide range of data sources, including NVD, GitHub Security Advisories, ecosystem-specific databases like PyPI and RubyGems, and malicious package records covering npm, NuGet, PyPI, and RubyGems, among others. EPSS scores are embedded directly, making them available as policy inputs without any additional integration work.

The result is policies that evaluate against a live view of current threat data, not whatever the feed showed when the package first arrived.

Every policy evaluation Cloudsmith runs produces a decision log entry: what triggered, what data informed the decision, what action the policy took, and why.

In a compliance review, e.g., SOC 2 Type II, DORA, FedRAMP, PCI-DSS, or CRA, log data is the evidence that your controls are real and operating as specified. In an incident response scenario, it’s the audit trail that tells you what was in your environment and what your policies did about it.

Decision logs are queryable via API, which means they integrate into whatever reporting or observability tooling your team already uses.

Why these capabilities reinforce each other

These capabilities cascade into and connect with each other. Continuous risk detection updates the risk profile that policy management evaluates against. Policy evaluations, including cooldown windows, produce decision log entries. Decision logs close the audit gap that makes enforcement defensible in a compliance review.

Cloudsmith delivers a continuous control plane for safely consuming open source dependencies. That's the structural difference between a security program built on Cloudsmith’s security features and one that relies on point-in-time scanning and downstream pipeline gates.

 about what security enforcement looks like for your environment.

Jason Myers

Technical Content Marketer

How Cloudsmith security features work: policy, risk detection, and the audit trail

On August 4, 2026, a self-propagating worm began 

publishing malicious versions of the npm packages keyv and cacheable

, is still developing as we publish, and the list of affected 

 packages has grown through the day to 2,236 package versions. Below we cover what we know so far, how to check whether your organization is exposed, and the controls that stop this class of attack from reaching your builds.

Beginning around 10:53 UTC on August 4, an attacker started pushing malicious releases to legitimate, well-established 

 packages. We know these were not a set of newly published typosquatted packages; the attacker is publishing new 

 of real packages that teams already depend on.

The scale of this attack is significant - tracking so far points to roughly 

 published across them. Several packages received a dozen or more malicious versions in a matter of hours, and new versions were still appearing past 13:20 UTC.

The affected packages sit deep in the JavaScript dependency graph. 

 are transitive dependencies of a large range of common tooling, so most teams that install them never explicitly chose them directly. Weekly download volume for some of these packages rivals the most popular libraries on 

, which gave the campaign a very large potential blast radius.

The activity carries the hallmarks of the 

. The malicious payload is delivered through install-time lifecycle (preinstall) scripts. Once executed on a developer or CI machine, it harvests credentials and tokens and uses any recovered 

 publish tokens to push malicious versions to other packages the compromised account controls. That self-propagation is why a handful of initial compromises turned into hundreds of packages within hours.

It’s also important to note that if you’re running 

, released roughly a month ago, it no longer runs lifecycle scripts by default. Organizations that have upgraded and not re-enabled script execution are substantially protected even if a malicious version was pulled, because the payload never runs on install. However, teams still on older 

 clients, or that have explicitly enabled install scripts, do not have that inherent protection.

Exposure comes down to two questions: did you install an affected version, and could it run on install?

 Compare the packages and versions in your lockfiles (

 against the current indicators. Community trackers are maintaining live lists; the tagged dataset at 

 are both being updated as the campaign expands. Because the affected set is still growing, treat any single snapshot as a lower bound and re-check.

Match on whole package names, not substrings.

 When scripting your own checks, anchor on exact package names. Loose substring matching produces false positives (for example, flagging an unrelated package that merely contains an affected name as a fragment) and wastes triage time.

 The malicious versions were published in a bounded window on August 4. A version of an affected package that predates that window is not part of this campaign. Confirm the specific version, not just the package name.

Check whether install scripts can execute.

 client version and whether lifecycle scripts are enabled in your environments. If you are on 

 with scripts disabled by default, an affected version that was pulled but never executed represents materially lower risk.

 If a malicious version could have run in a developer or CI environment, rotate 

 tokens, cloud credentials, and any secrets that were reachable from that environment, and review recent publish activity on your own packages.

Search your Cloudsmith registry for any cached versions & quarantine. 

If your organization doesn’t have a cooldown or malware policy in place, there's a chance a malicious version was already requested by a developer or pipeline and cached in Cloudsmith - search for any affected versions and quarantine them.

If you find only pinned references in old, dormant projects, the immediate risk is usually low, since a locked dependency tree will not pull a new malicious version on its own. The real exposure is a fresh install or a lockfile refresh that resolves to a compromised version.

This attack is just another example of why pulling directly from a public registry into your builds is a risk worth removing. Cloudsmith sits between the public registry and your developers and pipelines as a single, policy-enforced control point, which changes the outcome in three ways.

While there are many client-side cooldown solutions built into native package managers, this can often be difficult to manage at scale for enterprises. Regardless of client version or script settings, Cloudsmith moves enforcement to the server-side by removing newly published upstream versions for a defined window before they become installable. Attacks like this one depend on speed: a malicious version is published and pulled into builds within minutes or hours, then withdrawn. 

 means those versions are never served to your teams during the window when they are most dangerous, buying time for the ecosystem to flag and remove them.

 Packages flowing through Cloudsmith are scanned for malware and also matched against our comprehensive database of malware advisories. With 

, you can define policies that block versions flagged as malicious or matching known indicators. When a package is identified as compromised, the policy stops it from being distributed to any consumer behind Cloudsmith, without waiting for every developer to update a manual blocklist.

 Because installs resolve through Cloudsmith rather than directly from the public registry, you get one place to enforce controls, one audit trail of exactly what entered your environment, and the ability to answer "were we exposed?" from your own records instead of reconstructing it across every machine and pipeline. Cooldown and malware policies only protect what actually routes through them, so 

 is ensuring your package installs go through the proxy rather than reaching upstream directly.

By applying these safeguards in Cloudsmith, you can greatly reduce the likelihood of being affected by the frequent supply chain attacks we are seeing across the open source ecosystem.

Cristian Garcia

Senior Customer Success Manager

Keyv and Cacheable npm packages compromised in active supply-chain attack

The software supply chain landscape changed forever in early-to-mid 2026. What began as a series of isolated credential-theft incidents quickly mutated into a highly coordinated, multi-registry, self-propagating 

 campaign.

By abusing trusted relationships, hijacking CI workflows, poisoning build caches, and even embedding execution scripts inside compiled native binaries, the threat actors behind this campaign (tracked as 

) bypassed traditional static scanner defences.

As of the time of writing, this is a somewhat complete, chronological teardown of the three distinct waves of this campaign: 

Shai-Hulud, Mini Shai-Hulud and similar worms

The Shai-Hulud worm lineage first surfaced in late 2025 as a direct credential stealer targeting the npm ecosystem. However, on May 11th, 2026, a highly automated, modernised variant called Mini Shai-Hulud struck with vastly expanded scope. While it heavily impacted npm, compromising more than 170 packages across the TanStack, Mistral AI, UiPath, and OpenSearch ecosystems, the worm simultaneously breached the broader software supply chain, orchestrating coordinated attacks against PyPI, RubyGems, Docker Hub, and even malicious extensions within VS Code.

Unlike the earliest waves of Shai-Hulud that relied specifically on stolen static developer credentials, Mini Shai-Hulud (believed to have been created by a different group to TeamPCP), gained entry to these diverse environments via entirely new approaches, like pull request workflow misconfigurations in GitHub Actions (as seen in the 

 compromise).

The basic workflow looked like this. An attacker submitted a pull request from a fork. This triggers a workflow that grants write access to the base repository's cache. The attacker proceeds to poison the cache with malicious payloads. Hours later, a legitimate maintainer merges a commit, triggering the standard release workflow. And this all concludes with the release run pulling the poisoned cache and executing the malicious payload invisibly.

Rather than exposing static tokens, the worm scraped live tokens directly from the GitHub Actions runner’s active memory (the 

). Depending on the target ecosystem, it then exchanged these temporary workflow credentials for registry-specific publish tokens, using OIDC token exchange endpoints for 

, or harvesting raw API keys for RubyGems and container registries like 

.

This allowed the published packages, container images, and extensions to carry valid SLSA Build track provenance signatures. To defenders, the updates appeared completely legitimate and officially attested across all affected registries. It’s the equivalent of deepfakes for the software supply chain.

Perhaps the most malicious aspect of this multi-registry wave was its defence mechanism. Once inside a developer's local machine or build server (whether pulled via a Python 

 package, or a VS Code extension) the payload established a background daemon named 

. This daemon monitored whether its stolen tokens had been rotated or revoked, acting as a dead man’s switch. If a defender revoked the token, the daemon immediately triggered a local script that executed 

, wiping the victim's entire home directory to eliminate forensic traces and punish the developer.

 represents the third evolution of the Shai-Hulud family of worm attacks, compromising hundreds of 

 components (including packages within the 

 namespaces).

In this workflow, a simple 

 command that drops the Miasma payload. Again, the tactics and techniques of this Shai-Hulud worm have changed multiple times so far this year. Most security scanners and manual reviewers focus closely on scripts blocks in 

 triggers).

Miasma bypassed this completely by abusing 

, for context, is a legitimate config file used to compile native C++ Node.js add-ons. When a package containing this file is installed, the npm client automatically spawns 

. The attackers embedded direct command execution wrappers inside the 

 target configs. The system executes the malicious payload during compilation, completely avoiding the need for suspicious 

 lifecycle scripts.

Miasma focused heavily on capturing cloud identities instead of static keys, implementing collectors specifically designed to steal active GCP and Azure SSO credentials. Furthermore, if the infected CI container had access to the host's Docker socket (

), the malware would’ve performed a Docker escape. It identifies and terminates security monitoring containers (such as Step Security’s 

), creating a privileged Alpine container to write passwordless 

, and altered local DNS resolution to block telemetry reporting domains.

Discovered on June 8th, 2026, straight after the Miasma variant, the campaign expanded to the Python ecosystem, targeting critical academic genomics and phenotype analysis packages on 

).

In this attack, a simple Python Import loads a 

 extension which executes embedded javascript. The embedded javascript code executes a final Bun payload which exfiltrates to a GitHub endpoint.

In a major evasion upgrade, the attackers did not modify any Python source files. Instead, they embedded the malicious JavaScript loader inside the compiled Rust/C++ binary extension (

).

When a researcher imports the package via Python (

 file. The shared object discreetly initialises and runs an embedded JavaScript engine to execute the payload. This makes the new attack completely invisible to code-reviewers looking at Python source files.

The payload's primary file, 

, contains a massive block of comment text representing a fake LLM jailbreak prompt. The decoy simulated classified briefings, asking for technical steps on weaponising biological agents and designing nuclear devices. This was a deliberate attempt to pollute static analysers and AI-assisted tooling, causing them to choke, flag false positives on safety boundaries, or even worse, misclassify the file as a prompt-injection experiment rather than a credential-stealing worm.

Instead of a standard server, the Hades variant registered dozens of GitHub repositories using the compromise-controlled user account 

. The repositories used underworld-themed naming conventions (like 

. The exfiltrated data was stored in an encrypted envelope format, signed with GPG keys.

In August 2026, the open-sourced Mini Shai-Hulud codebase was weaponised in one of the most widespread multi-registry campaigns to date. The outbreak began with the compromise of 

's account. This was the primary developer behind popular, heavily downloaded packages like 

.

From this single entry point, the worm exponentially expanded into over 444 packages across 2,000+ published releases, hit 100 packages in the 

 ecosystem alone, and aggressively turned every infected machine into an automated propagation node.

The execution chain relies on the 

 dropper. To evade standard JavaScript static analysis tuned for Node.js patterns, the 

 file detected the host architecture, downloaded a bundled 

 runtime, and proceeded to execute a massive, heavily obfuscated payload named 

.

Rather than stopping at passive credential dumping, this variant actively probed and harvested deeper infrastructure credentials: It actively queried AWS IMDSv2 and ECS endpoints, enumerated AWS SecretsManager paths, read mounted Kubernetes ServiceAccount tokens, and extracted HashiCorp Vault secrets across KV v1 & v2 mounts. It even hunted local crypto wallets like bitcoin wallet files. It was observed that exfiltration calls were routed to a dynamic domain endpoint. Crucially, if the server returned an execute payload in its JSON response, the worm immediately executed it using 

, giving the threat actors live remote code execution on infected dev machines and CI runners. Finally, if the primary C2 server failed, the worm fell back to using stolen GitHub tokens to automatically create public GitHub repos containing base64-encoded stolen data, tagging the repo descriptions with a distinctive campaign marker. This time 

.

To ensure propagation, the worm scraped 

 creds or even exchanged GitHub Actions OIDC tokens to auto-publish patch updates of packages the maintainer controlled. It inserted 

 as an optional dependency, allowing the payload to silently cascade into downstream installs without causing build failures. Furthermore, it even expanded on the infection vectors directly inside dev workflows by writing malicious GitHub Actions workflows disguised with the name 

, as well as altering AI-related dev tooling via poisoned 

Shai-Hulud-style worms will continue to change shape

 or leaking of a complex piece of malware like the Shai-Hulud worm typically triggers a significant shift in the threat landscape. When a sophisticated framework (originally tightly controlled by an advanced group like TeamPCP) becomes publicly accessible, it lowers the barrier to entry for a much wider, more disorganised pool of actors. Independent operators, smaller cybercriminal syndicates, and even security researchers looking to understand its mechanics can dissect the original codebase. This democratisation of advanced tech means that features previously requiring high-level engineering, such as automated supply-chain propagation or complex credential harvesting, become plug-and-play modules for anyone with basic development skills.

Without a centralised command structure like TeamPCP directing its deployment, the evolution of Shai-Hulud has naturally fragmented into highly specialised or experimental variants (such as 

). Newer threat actors are combining Shai-Hulud’s core self-propagating mechanisms with different payloads or evasion techniques tailored to their specific objectives. For instance, some variants strip away the heavier components to create lightweight stealth tools, while others swap out the exfiltration methods to target different developer ecosystems beyond 

. This fragmentation complicates defense strategies, as defenders can no longer rely on a single set of behavioural signatures or specific infrastructure indicators associated with the original group.

Furthermore, this post-TeamPCP era accelerated the timeline of dual-use engineering, where threat actors repurpose and optimise techniques from Shai-Hulud in unexpected ways (as seen in the 

). Independent devs often modify the codebase to patch flaws left by the original authors (such as fixing loud fallback routines or refining how the worm handles OIDC token exchanges). Because these new variants are built and deployed by completely unrelated clusters of users, the attribution of attacks becomes incredibly muddy. The signature architecture of Shai-Hulud is no longer a reliable indicator of 

 is attacking, but rather a reflection of a shared, open-source repository of commodity supply-chain threats. This is glaringly the case with alleged 

If the evolution of Shai-Hulud proves anything, it's that attackers are exceptionally creative and relying purely on reactive remediation is a losing strategy. When malware can pass SLSA attestations, bypass 

 inspection via native builds, and hide inside compiled binaries, you simply cannot predict every vector. It only takes one compromised token to trigger a massive downstream disaster. While rapid incident response remains essential, putting heavy effort into proactive preventions, such as universal package cooldown policies and strict pipeline boundaries is the only way to neutralise these threats before they execute. Stopping the attack at the gate will always be easier than cleaning up the damage after a worm gets through.

If you found this breakdown useful and want to keep up with the evolving threat landscape of malicious dependencies and open-source malware, join our upcoming webinar: 

Inside the Open Source Malware Attack Landscape

) to discuss real-world attack vectors and how to protect your software pipelines.

Nigel Douglas

Head of Developer Relations

The evolution of Shai-Hulud-style worms

If time’s just a construct of human perception, does that mean all package managers interpret time differently? And what does that have to do with cooldown policies?

 help protect your software supply chain from zero-hour threats by holding packages for a set period of time to allow threat intelligence to catch up. But where does a package get held, and how does that impact how you configure your cooldown policy?

You can implement cooldown controls on either the client-side and on the server-side. This post highlights the difference between where the cooldown policy lives, and how that impacts your ability to implement and scale that policy. We’ll explore how they can be defined differently in various ecosystem package managers, and why all this flexibility actually might hinder (not help) the security posture of our software supply chain.

JavaScript developers most commonly pull 

 registry. The most popular Javascript package manager, 

, included the concept of a minimum release age (

 represents the number of days a dev must wait before they can receive a newly-released software dependency, as a single number unit. However, not all JavaScript package managers work the same way. The 

 client, for example, chose a completely different unit of time. We can see that 

 specifically treats cooldown (within the 

 registry also introduced the cooldown field 

) represents the configured cooldown period in 

) supports days and weeks in a more humanly-readable approach, similar to 

This leaves JavaScript teams in a situation where securing a polyglot Node.js environment requires a math degree and a cheat sheet! If your team wants to implement a cooldown policy and uses a mix of 

 (as only a few examples), you need to translate a basic 48-hour quarantine policy into days, minutes, seconds, and human-readable string tags simultaneously. This arbitrary config drift is more than a tedious task, it’s a genuine security liability. When security policies are this fragmented across tools running in the same language ecosystem, misconfigurations are more likely.

The Python ecosystem is similar to JavaScript when it comes to representations of time. Astral’s 

 package manager provided cooldown controls in 

. It uses relative durations, but also supports a bunch of other duration formats (inside 

The result in the Python ecosystem is a case of duplicate standards. While more modern, lightning-fast tools like 

 embrace highly flexible relative text strings, standard 

 leans heavily into the rigid syntax of ISO 8601. For security teams trying to enforce a consistent supply chain guardrail across a variety of developer endpoints and pipelines, this introduces a layer of translation friction. We aren't simply configuring the time window, we’re also managing entirely different syntactical philosophies from what was discussed in the JavaScript ecosystem - just to keep a volatile package at bay.

Package Managers with no support for Cooldown Policies

And that brings us to the lack of support for cooldown policies in many other popular language ecosystems. As of the time of writing, languages like 

 provide client-side support for cooldown controls in any direct configuration field.

In the absence of built-in cooldowns, devs without server-side or private artifact repository solutions rely on strict version pinning as their primary line of defence. But version pinning is a double-edged sword:

Pinning to an older version should (generally speaking) keep you safe from a zero-day compromise in today's release, but it leaves you exposed to older, known CVEs.

Pinning only works if you trust the version you are pinning. If you update your config to pin a newly released version and that version happens to be a compromised release from a hijacked maintainer account, well then, your client will happily download and index that malware regardless.

Without a cooldown policy acting as a quarantine buffer, devs updating their pinned dependencies are essentially playing Russian roulette with the latest release.

On a positive note, the open source supply chain is evolving all the time. Where cooldown controls don't exist today, they could very well exist tomorrow. What’s important to software developers and security teams is that we can curate or enforce security controls that seamlessly apply across the wide variety of languages and runtime ecosystems.

That’s where an artifact management platform like Cloudsmith comes to the rescue. The platform provides cooldown policy support for 

, with additional format support planned:

Cloudsmith supports cooldown policies in npm and Python

If time really is just a construct of human perception, package managers have certainly taken that philosophy to heart. But the real headache is trying to actually implement security controls in the real world. 

This is fundamentally a client-side crisis

We are forcing devs and their pipelines to rely on local, fragmented CLI tooling to enforce security policies that should be uniform. The fragmentation here is a massive vulnerability:

Different naming conventions and arbitrary time units across clients make universal configurations very challenging.

Some client-side tools allow granular exclusions, while others offer no cooldown protection at all.

While centralised registries like Cloudsmith work overtime to curate, scan, and secure upstreams, they need to account for rogue, outdated, or poorly designed local clients that bypass standard safeguards.

Trying to secure a polyglot codebase where the security posture of your entire pipeline is dictated by the weakest client-side tool in your stack is a logistical nightmare.

Cloudsmith solves this systemic headache by providing a single control plane for your entire software supply chain. By defining a universal policy that sits above individual ecosystems, you don’t waste time tweaking configurations across a wide range of runtimes. With Cloudsmith, you define the policy once, and enforce it across your entire workspace.


If you’re interested in learning more about Cloudsmith 

How to configure time for cooldown policies

If you were hoping for a relaxing summer break without a software supply chain attack, the latest news will disappoint. Early on July 14th 

 identified that an attacker compromised AsyncAPI repositories and used the projects’ own CI/CD pipelines to publish four malicious npm packages. Together these packages see more than 2.9 million downloads a week, with @asyncapi/specs accounting for roughly 2.7 million of them.

Here’s what we know so far, why so many conventional defenses would have let these packages through, and what a governed control layer does about it.

The attacker exploited a misconfigured GitHub Actions workflow in the asyncapi/generator repository to steal a highly privileged token belonging to the project's automation account. They used that token to push a commit that triggered the release pipeline, which published trojanized packages to npm under the trusted @asyncapi namespace. A second repository in the same organization was then used to publish the highest-impact package, @asyncapi/specs.

For a full breakdown of the attack, Wiz’s post provides an excellent 

This attack has two details that matter for what follows. First, the malicious code was hidden inside otherwise legitimate source code and it ran on package import rather than install. Second, once triggered it installed a persistent credential stealer that swept for browser passwords, SSH keys, npm and GitHub tokens, and cloud credentials.

Why security scanners may have allowed these packages through

None of the usual signals a build environment relies on would have stopped these downloads.

 The packages came from the correct namespace, published through the project’s real release pipeline using a real maintainer token.

The versions were brand new, so no intelligence existed yet: 

At the moment of publication, no advisory named these versions as malicious. A team pulling @asyncapi/specs@latest in the minutes or hours after publication would have received a clean bill of health from every feed, because the community hadn't identified the compromise yet. This is the zero-hour window, and pulling directly from a public registry means running straight into it.

Install-time scanning looked in the wrong place:

 Many defenses focus on preinstall and postinstall hooks, because that's where supply chain payloads have historically lived. This payload executed on import instead. Tooling watching only the install lifecycle would have seen nothing.

 Even a developer inspecting the source would have had to scroll past hundreds of blank spaces to find the payload.

This pattern is consistent across many of this summer’s software supply chain attacks. When packages flow directly from a public registry into a build without a control point in between, a compromise can reach your environment because nothing was positioned to intercept it.

Check your lockfiles for the compromised versions

Assume secrets are exposed on any developer machine or CI runner that installed an affected version. Rotate npm tokens, GitHub tokens, SSH keys, AWS access keys, and browser stored credentials.

Audit CI/CD secrets for any pipeline that ran npm install during the compromise window.

The steps above are helpful but reactive, taking place only after a malicious package is already inside your environment. These steps also don’t scale, relying on someone to spot the advisory, trace which builds pulled what, and rotate the right credentials before an attacker uses them.

Artifact management platforms can add a control layer ahead of package pulls to prevent some of these dangers from ever becoming present. Simply put, when detection and response move upstream and run automatically, compromised packages are stopped or contained upstream.

Cloudsmith is that governed control layer. Every package that enters a Cloudsmith workspace, whether proxied from a public registry or published internally, is evaluated against policies that assess risk, and permitted or blocked before it reaches a developer machine or CI pipeline. Two capabilities map directly to how the AsyncAPI attack played out.

With Cloudsmith as a private registry proxying npm, PyPI, Maven Central, and others, npm install routes through Cloudsmith rather than straight to the public registry. Every package, including transitive dependencies, flows through one control point you can see and govern. Without that layer, there's no place to intercept a poisoned @asyncapi/specs on its way into a build.

 to help businesses vet new packages for risk: they set a minimum age a package must reach before it can be used. That closes the window most of these attacks target, the gap between a malicious package going live and an advisory going out, Cloudsmith’s cooldown policies are enforced at the index level. Versions younger than the age you set are filtered out before the package manager ever sees them, with builds resolving cleanly to the next available version.

Cooldown with Cloudsmith this summer. Relax knowing that Cloudsmith checks every open source dependency your team pulls, keeping malware like this out of your artifacts. 

 today about defending against malicious package attacks.




Meghan McGowan

Product Marketing Manager

Inside the AsyncAPI npm supply chain attack

The tools security teams reach for first inspect code at different points in the development process: at the PR stage, at build time, or monitoring what's running in production. They're solving real problems, and they're good at them. What they don't do is govern which packages are allowed to enter your environment in the first place.

Teams and Access Controls

More articles

How Cloudsmith security features work: policy, risk detection, and the audit trail

Keyv and Cacheable npm packages compromised in active supply-chain attack

The evolution of Shai-Hulud-style worms

How to configure time for cooldown policies

Inside the AsyncAPI npm supply chain attack

How to use Cloudsmith as a dependency firewall