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Scanning for malicious packages at deployment catches issues already inside your environment. Scanning at the pull request catches what your CI pipeline already ran. If security controls don’t see a malicious package until after ingestion, it already has multiple chances to execute.

This is an architecture problem that requires an architectural answer. A dependency firewall is that answer. It’s a security posture that places guardrails between your development environment and public registries, evaluating what enters before any build runs.

How malicious packages enter your build environment without inspection

Build tools connecting directly to public registries like 

, and packages arriving as requested is a common approach to open source software consumption. When scanning runs at the pull request, in the pipeline, or at deployment, the package is already inside your environment.

) tools identify known vulnerabilities in packages already present in a codebase. That's genuinely useful. But even with the best scanners, an architectural gap exists between ingestion and where the first scanner sits. Establishing a control point in that gap allows teams to better govern the packages that enter their environments.

Two patterns produce this gap consistently.

The first is when no artifact management exists at all and developers pull directly from public registries. Teams can’t implement security controls because the layer necessary to do so isn’t in place.

The second, and it’s more common than most organizations realize, is when an existing artifact management tool handles the storage of software artifacts but enforces no security controls on the consumption of those artifacts (for example: AWS CodeArtifact or GitHub).

A third pattern is emerging due to the use of AI coding tools.

These agents generate code that expands the attack surface faster than most security programs have adjusted for. Tools like Cursor, AI-assisted editors, and LLM-powered agents recommend and install packages as a normal part of their workflow. Teams need governance that can match the pace of AI-powered development.

Developers tend to accept those recommendations without the scrutiny they'd apply to a manual dependency addition. The packages arrive through the same registries and the same pipelines as they would with manually-written code, but the human review step between "

Modern attacks execute before the scanner runs

The latest supply chain compromises exploit trusted open source packages. The 

 attacks succeeded because they targeted specific packages developers already trusted and executed payloads at install time – before any scanner ran.

Cloudsmith's analysis of 2025 OSV.dev data, comparing when malicious versions were published to npm against when OSV flagged them, shows that 

99 percent of npm package compromise attacks are identified within 72 hours of publication

. A package scanned within that exposure window looks clean because no malware identifier exists yet. By the time the community flags the threat, pipelines are already running it.

Scans are snapshots – threat intelligence isn't

There's a second failure mode related to scanning that plays out more slowly. A package enters your environment, the scanner runs, nothing matches, and it's marked clean. That evaluation is correct at that moment because no known vulnerability exists. Then six months later, a researcher discloses a critical CVE. Nothing triggers a re-evaluation, so the original point-in-time scan remains the most recent one and the package is still in production.

. That package existed in production codebases for years before threat intelligence identified the vulnerability. The problem in cases like this is when you treat a one-time evaluation at the boundary as permanent clearance.

New advisories arrive constantly for packages that evaluate as clean at ingest. A scanner that runs once, when a package first arrives, doesn't protect against what threat researchers find next month or next year. The scan result ages out the moment new intelligence contradicts it.

That said, scanning remains essential. But the fluid nature of supply chain threats, packages that evaluate as clean at ingest and become risks as new intelligence emerges, reveals a gap that point-in-time evaluation can't close on its own. Addressing it means supplementing scanning with controls that operate earlier in the process, before packages enter an environment where they can execute.

The control point that doesn't depend on developer behavior

A dependency firewall addresses both failure modes described above by changing where and when evaluation happens. The first is early detection at the boundary: identifying threatening or malicious dependencies before they enter your environment, rather than after they had the chance to execute. The second is ongoing assessment: unlike a scanner that runs once and treats the result as permanent, this posture evaluates continuously as new threat intelligence arrives. A package that cleared evaluation at ingest doesn't remain cleared by default; it remains cleared only if current intelligence still supports that conclusion.

Developers start their work with packages that clear those guardrails, meaning the inputs to their work are more likely to be secure from the start. Vulnerabilities, threats, and license flags caught at the boundary save considerable rework compared to discovering the same issues in a PR review, a pipeline scan, or a post-incident forensic report.

For the vast majority of packages that clear evaluation, the process is entirely transparent and frictionless to developers. When friction occurs, like disabled scanner plugins, skipped linting rules, and manual approval workflows, developers create workarounds. Developers don’t need to bypass a control they don’t interact with. Compliance that relies on structural enforcement is more reliable than manual, behavior-based approaches.

For security and DevSecOps teams, the value is consistency at scale. Every developer, every AI agent, every pipeline, and every team in the organization hits the same guardrails. Policy applies universally and automatically, closing the gap between what policy says and what enforcement carries out.

A dependency firewall posture doesn't replace the scanning you already have. It moves the trust decision to the ingestion boundary, so your team uses safe, evaluated packages before anyone writes a line of code.

For a practical guide to building a dependency firewall, covering controlled ingestion, continuous policy evaluation, and the OPA-backed policy engine, read 

How to use Cloudsmith as a dependency firewall

Jason Myers

Technical Content Marketer

Dependency Firewall

A dependency firewall gives your scanner better inputs

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.

That control happens at the artifact registry, which is the one place every package, team, and pipeline passes through. A scanning tool that runs at PR time sees a dependency after it's already been fetched. A runtime tool sees it after it's deployed. Cloudsmith sits upstream of both: before a dependency reaches a developer, a build tool, or a CI job, Cloudsmith checks against current threat intelligence and uses your policies to clear or block it. The scanning you already have gets cleaner input. Cloudsmith provides the enforcement layer the security stack was missing.

Three capabilities form the foundation of a dependency firewall posture: controlled upstream ingestion, continuous metadata enrichment, and a customizable policy engine. Together they create a control layer that decides what enters your environment automatically and keeps re-evaluating that decision as intelligence updates.

Most security tools evaluate packages after they enter your environment. Cloudsmith governs what's allowed in before a build runs and checks everything in your repositories when new threat intelligence drops. No team has to configure its own pipeline rules to get that coverage. This guide covers upstreams, continuous enrichment, and policy as code built on OPA, from initial configuration through to enforcement.

 route all public registry requests through your Cloudsmith repositories rather than letting build tools pull from registries directly. Every package request resolves through a configured upstream. Cloudsmith fetches each package once and caches it permanently, serving it from your repository from that point forward. When a public registry has an outage, your builds continue.

The security consequence matters more than the availability benefit: you have a single point through which all package traffic flows. Without it, policies can't enforce consistently because downstream rules only catch what passes through them. The upstream is the prerequisite everything else builds on.

 closes dependency confusion attacks at the configuration level, before policy evaluation runs. When you mark a public registry as untrusted, Cloudsmith won't fetch a newer version of a package from that registry if a local version already exists. A higher version number on the public registry doesn't win. An attacker publishing 

. The attack surface closes at configuration, not after detection.

Cloudsmith evaluates every package against the 

 database (which aggregates records from GitHub Security Advisories, the OpenSSF Malicious Packages Project, the NVD, and language-specific datasets), 

 for real-world exploitability, and SPDX license metadata. For Docker images, Cloudsmith generates a full software bill of materials (

The part that separates this from point-in-time scanning: Cloudsmith polls its intelligence sources continuously. When a new advisory is published, Cloudsmith checks every package already in your repositories, not just what arrives after the disclosure. A package that cleared evaluation yesterday can be quarantined today, automatically, without anyone manually triggering a re-scan.

This is the enforcement model that most registries and scanners don't offer. A scan at ingestion tells you whether a package was safe when it arrived. Continuous re-evaluation tells you whether it's still safe now. When new malware signatures drop, or a previously low-severity vulnerability gets weaponized and its EPSS score spikes, Cloudsmith checks your inventory for a match and triggers policy enforcement.

Policy evaluation also runs at package promotion events and when new packages are published, and on a fallback schedule if nothing else has changed.

Cloudsmith's approach to policy management is built on Open Policy Agent (

), the same open standard used in Kubernetes environments and across the cloud-native ecosystem. Policies are written as policy as code in Rego and managed through Cloudsmith's 

, giving you full lifecycle management of rules, precedence, enabled state, and actions as code, all version-controlled alongside the rest of your infrastructure.

Cloudsmith puts policy enforcement, malicious package detection, vulnerability matching, and license governance in one platform, under one policy model. That means policy as code, using OPA and Rego, with structured exception workflows, instead of binary allow/block toggles spread across multiple UIs.

This normalizes package metadata across 30-plus formats, so a single policy as code definition, written in Rego, applies consistently whether you're managing npm packages, Docker images, Maven artifacts, or Python wheels. Policy input includes CVSS scores, EPSS scores, OSV malware identifiers, license type, fixed versions, repository names, uploader identity, build-time tags, and for Docker images, the full component-level SBOM.

Cloudsmith provides policy templates and a 

 to accelerate policy authoring. Every policy evaluation creates a decision log record, which includes the input data, what the policy matched, and which actions ran, giving you the traceability to audit decisions and roll back when needed.

Creating a baseline policy set for a dependency firewall

Four policies form a strong starting point. Each addresses a distinct risk; together they cover the primary ways malicious or non-compliant packages enter a production environment.

 Packages carrying an OSV malware identifier get quarantined across every repository in the workspace – current and future. The coverage is immediate and unconditional, and the signal quality is high enough that there's almost no argument against this policy. Apply it globally, set its precedence to #0 (first), and move on.

 A cooldown is a timed hold for newly fetched packages from public registries, enforced at the index level. Cloudsmith won't download or cache packages within the cooldown window, making them unavailable to build tools. Compromise attacks on trusted packages are typically identified within days of publication. A three- to seven-day hold gives threat intelligence time to catch up before a package reaches a pipeline. When a malware identifier matches during the window, the package gets quarantined. When a package clears the window clean, it downloads, caches, and becomes available automatically. Clean cases need no human decision.

Cloudsmith exposes both the CVSS score and the fixed version in policy input metadata, so policies can distinguish between risk profiles rather than applying a single blanket rule. A package with a 

 warrants a different response than one with the same score and 

. Blocking a team where no remediation path exists creates friction without reducing risk. 

 packages (e.g., GPL, AGPL) entering through transitive dependencies create legal exposure that surfaces late and expensively. A license policy flags or quarantines non-compliant license types at ingestion. Teams that catch a GPL dependency at the boundary have a straightforward remediation path. Teams that discover it during a security audit or acquisition due diligence do not.

When policies match a package, they take actions: quarantine, tag, or both. Tags trigger webhooks, which route to Slack notifications, Jira tickets, CI/CD events, or any supported alerting tool. Add a tag action to every policy. It makes enforcement decisions visible and actionable outside the Cloudsmith UI.

Before adding quarantine actions to a live policy, Cloudsmith's 

 runs the policy against your existing repository contents without creating decision log entries or touching any live package state. The response shows which packages would match, the reasons, and what actions would be taken.

This is how you measure blast radius before enforcement. A policy that looks narrow on paper can match more packages than expected when run against a production repository. Simulation lets you find that out before a build breaks. The recommended path: start policies with tag-only actions. Use decision logs to review what matched, assess signal quality, and surface false positives. When you're confident, add the quarantine action to the existing policy. The Rego logic stays the same. Moving from reporting to enforcement is a configuration change, not a rewrite.

Engineers trying to hit delivery commitments route around security controls that slow pipelines, generate unexplained blocks, or require manual steps outside normal workflows. A dependency firewall handles this by making the secure path the fast path: packages that evaluate clean pass through without any additional steps in the developer's workflow.

When a developer requests a specific package version that's in quarantine, they receive a 

 response with policy context in the body. The message is configurable: you set it in the policy description. Point developers to the exemption workflow, the right Slack channel, or a PR template for requesting a waiver. They know what caused the block and have a clear path forward.

When using a cooldown policy and a developer requests the latest version of a package with a quarantined release, Cloudsmith removes the quarantined version from the repository index and serves the most recent policy-compliant version.

Security policies need a structured exception path. Without one, practitioners route around controls entirely, which defeats the purpose of having them.

The recommended approach uses an allowlist-style 

 policy that marks specific packages as approved and releases them from quarantine. Developers enter the exemption flow through the 

 response, which points them to the process. From there, a GitOps workflow handles the rest: a developer raises a pull request to add their required package to the allowlist; the security team reviews and approves; automation merges the change, re-evaluates the policy, and sets the package to available. The PR review is the only manual intervention required for this process.

Git records every decision with a person, a timestamp, and a reason. The audit trail exists outside the platform, changes are reversible, and your CI tooling handles the reconciliation.

Decision logs record every policy evaluation: the exact input data used, what the policy matched, and which actions were taken. The 

 field contains the data that drove the decision, which is useful when you need to explain a quarantine to an auditor, a team lead, or a compliance reviewer. If you query for all policy matches in the last seven days and you get a concrete, reportable picture of your threat landscape.

Webhooks fire on quarantine events, tag additions, or releases from quarantine. Configure them to route notifications to your alerting tools of choice (Slack, Jira, etc.) to ensure the right people get the information they need without manual polling. Manage webhooks through infrastructure as code at the repository level to ensure every repository carries the same alerting setup.

Establishing a dependency firewall with Cloudsmith starts with controlled ingestion. Enable the malware block globally and put it into enforcement. Add cooldown, vulnerability threshold, and license policies with tag-only actions first. Use the simulation API to understand blast radius. Review decision logs to assess signal quality. When you're confident in what each policy matches, change the action from tag to quarantine. The policy logic doesn't change.

Teams that add Cloudsmith alongside Snyk, Wiz, or other security and scanning tools get pre-ingestion control where they didn't have it before and without replacing the scanning that already works. The packages that reach your downstream tools already clear evaluation at the registry. Your existing scanning layer gets cleaner input. And when new threat intelligence drops, Cloudsmith re-evaluates everything in your repositories automatically. Threat intelligence updates continuously, and with Cloudsmith enforcement does too.

 to see how a Cloudsmith dependency firewall fits alongside your current stack.

Cloudsmith Blog

A dependency firewall gives your scanner better inputs

How to use Cloudsmith as a dependency firewall