Kusari vs Dependabot
For teams running Dependabot who need a defensible way to decide which of its alerts to act on first.
Read the comparisonMost teams evaluating Kusari are already running tools like Snyk, Dependabot or JFrog Xray, and usually more than one. This is an honest read on what each does well, where teams run into its limits, and what changes when you put a layer above them.
Built by the team that co-created GUAC and SLSA.
For teams running Dependabot who need a defensible way to decide which of its alerts to act on first.
Read the comparisonFor teams with a large Snyk install who have more findings than they can action and no defensible way to rank them.
Read the comparisonFor teams whose supply chain security is anchored to the artifact repository, and who need provenance further upstream than the artifact itself.
Read the comparisonFor teams who have already bought the reachability argument and are deciding how the dependency graph underneath it should be built.
Read the comparisonFor teams who bought Socket for malicious-package detection at install time and now need the dependency inventory and audit evidence underneath it.
Read the comparisonMost software composition analysis tools read your dependency manifest and check what it declares against a vulnerability feed. That works, and for plenty of teams it's enough.
It stops working the moment your software contains something the manifest never declared. 95% of open source vulnerabilities live in transitive dependencies, and the average JavaScript project carries 683 of them against 10 direct ones (J.P. Morgan, Eye on the Market, July 2026). A manifest declares what software is supposed to contain. It doesn't record what the build resolved.
Kusari builds the dependency graph from source instead, keeps it continuously updated as code moves, and ranks what it finds by what's exploitable in your environment rather than by severity alone.
If you're still drawing up a shortlist rather than comparing two finalists, the eleven criteria we'd evaluate any of these tools against is the more useful place to start.
A scan is a snapshot. It tells you whether you were exposed at the moment it ran, which was reasonable when the gap between disclosure and exploitation was measured in weeks. A CVE gets disclosed against a dependency you shipped in March. Nothing in your repository moved, your risk changed, and your last scan has no way to know that.
By 2026 around 80% of exploitations happen on or before the day of disclosure, and the median dependency runs 278 days behind its latest major version (J.P. Morgan, Eye on the Market, July 2026).
Most tools read the manifest and take its word for everything underneath. Kusari resolves the tree from source, so the graph reflects what's genuinely there rather than what the lockfile says should be, and it stays maintained rather than being rebuilt on request.
That's also why it can say when it doesn't know. Kusari's tooling declares whether its dependency tree data is complete or incomplete, with confidence-level evidence attached. Most tools don't.
| What's being compared | Manifest-read scanning | Source-built graph |
|---|---|---|
| Where the dependency list comes from | The manifest or lockfile, as declared | Resolved from source at build |
| What it misses | Anything that arrived another way: vendored code, pinned commits, transitive packages the manifest never named | Fewer blind spots, though C and C++ estates stay genuinely hard |
| Current as of | The last scan | Continuously updated as code moves |
| Unknowns | Usually indistinguishable from "nothing there" | Declared explicitly, with confidence evidence |
| Ranking | Typically CVSS severity, sometimes with reachability | Kusari Score: severity weighed against how widely the component is used across your estate, with KEV and EPSS setting floors and reachability removing what can't be hit |
| Remediation | Version bump pull requests, sometimes with a suggested fix | Root-cause fix across the transitive chain, validated in CI and re-scanned before it reaches you |
| Where it's the right tool | Fast, cheap, broad coverage, mature integrations. If your estate is one ecosystem with clean manifests, this may be all you need | Estates where provenance, audit evidence and transitive depth are the actual problem |
Most teams we talk to aren't running one clean ecosystem. They're running npm and Python and .NET, some of it built this year and some of it inherited, with a scanner or three already in place and nobody keen to rip any of them out.
Kusari works across all of it. It connects through what you already use: GitHub or GitLab for source, and GitHub Actions, GitLab CI, Jenkins, Azure DevOps, CircleCI, Bitbucket Pipelines or TeamCity for builds. Findings go back into Jira or ServiceNow, alerts into Slack or Teams. There are no agents to roll out and no pipelines to rebuild.
It sits above your scanners rather than replacing them, the intelligence layer above your existing stack, unifying every repo, dependency, SBOM and scanner finding into a single graph. The ones you already own keep running and become inputs.
That's also what makes the blast radius question answerable. When something drops, "do we have it, and where" is a query against one graph rather than a week of correlating outputs from four tools that don't agree with each other.
Kusari Score weighs exploitability against two other things: the technical severity of the vulnerability, and how widely the affected component is used across your estate.
A listing in CISA's Known Exploited Vulnerabilities catalog sets the score no lower than 9 out of 10. Where there's no KEV listing, EPSS sets the floor instead. Above 0.10 the minimum is 7.0, and above 0.50 it's 8.0. And when reachability analysis or a VEX document you provide establishes that a vulnerability isn't reachable in a given application, that application comes out of the calculation entirely.
The reachability agent works against your actual codebase rather than a rule, asking whether the vulnerable function is used at all, whether user input can reach it, whether it's production code or test-only, and whether anything already mitigates it.
Every ranking keeps its reasoning attached, which is the part that matters when someone asks you to defend it.
Opening a pull request isn't the hard part, and most tools do it. What happens between deciding a finding is worth fixing and handing you something safe to merge is where they differ.
Kusari traces the finding to its root cause across the transitive chain and works out the smallest change that resolves it, rather than bumping everything and hoping. The pull request it generates is specific to your environment, covering code, config and lockfile together.
Then it checks its own work. CI runs against the change, and a separate inspection agent re-scans the result for anything the fix introduced: a new vulnerability, a typosquatted package, a dependency nobody asked for. Anything it isn't confident about gets escalated rather than shipped.
Existing pull requests get updated rather than duplicated, which is the difference between a remediation queue you can work and one you stop reading.
Nothing merges itself. You review, and you decide.
Nobody rips out a scanner overnight, and we wouldn't ask you to.
Kusari runs alongside the scanners you already run, so the usual first step is running it alongside what you have and comparing the two lists for a few weeks. That's less painful on your teams than a migration, and it's the only way to find out whether the difference we're describing shows up on your estate specifically.
One of our health insurer customers answered a zero-day in seconds rather than days once the graph was in place, and had 90% of its vulnerability noise stripped out by reachability and exploitability analysis. If you're in a regulated industry, it's also worth reading what DORA, PCI DSS and NYDFS require of a software inventory before you decide what evidence your current tool has to produce.
Three things we will need you to do:
Building the graph from source needs CI access, so this starts with a conversation with whoever owns your pipelines. Most teams go repo by repo rather than all at once, which makes that a smaller ask than it sounds.
There's no manifest to read, so dependencies arrive as vendored source somebody copied in four years ago, submodules pinned to a commit, and system packages resolved at build time on whatever machine happened to run it. You will not resolve all of that. What you get is a documented boundary between what's confirmed and what isn't, which is what an auditor accepts.
If your current tool is wired deep into ticketing and exception workflows, rebuilding that plumbing takes engineering time you'll have to find. Running Kusari alongside it buys you time here.
Software composition analysis identifies the open source components in your code and checks them against known vulnerabilities. Software supply chain security is the broader problem: where each component came from, what pulled it in, whether the build that produced your artifact can be trusted, and whether you can prove any of it to an auditor. SCA is one input to that rather than a substitute for it.
Almost certainly. Kusari is built for mixed estates rather than a single ecosystem: npm, Python, .NET, or all three in the same organization. It connects through GitHub or GitLab for source control, and GitHub Actions, GitLab CI, Jenkins, Azure DevOps, CircleCI, Bitbucket Pipelines or TeamCity for builds. Findings go into Jira or ServiceNow, alerts into Slack, Teams or a webhook. It supports CycloneDX and SPDX, and there are no agents to roll out.
A manifest is a declaration of what the software is supposed to contain. It's written before the build and doesn't record what the build actually resolved, so anything that arrived another way, including transitive packages, pinned commits and vendored code, never appears in it. Since 95% of open source vulnerabilities live in transitive dependencies (J.P. Morgan, Eye on the Market, July 2026), a tool reading only the manifest is reasoning about a subset of your software and reporting confidently on the rest.
It opens pull requests. It doesn't merge them. Kusari traces a finding to its root cause across the transitive chain, works out the highest impact change that resolves it, and generates a pull request covering code, config and lockfile for your environment. CI runs against that change and a separate inspection agent re-scans the result for anything the fix introduced, such as a new vulnerability or a typosquatted package. Low-confidence fixes are escalated rather than shipped, and existing pull requests get updated rather than duplicated. Review and merge stay with you.
Kusari Score weighs the technical severity of a vulnerability against how widely the affected component is used across your estate, then adjusts for whether it's being exploited. A CISA KEV listing sets the score no lower than 9 out of 10. Without a KEV listing, EPSS sets a floor: above 0.10 the minimum is 7.0, above 0.50 it's 8.0. Reachability analysis or a VEX document removes an application from the calculation entirely when the vulnerable code path can't be reached. Every ranking keeps its reasoning attached so it can be defended rather than only asserted.
Less than thirty minutes on a public repository. We run it live, you watch, and you keep any pull requests it opens.
We run Kusari on a public repo of your choice, live, for thirty minutes. It resolves the dependency tree, sorts what's affected from what isn't, and ranks what's left by Kusari Score. If you want us to, we'll open pull requests for anything currently open and fixable before the time is up, and you keep those whether or not you decide to keep talking to us.