Spot on about the wider net. We saw the same thing - Black Duck pulled up libraries from a POC we archived two years ago. Impressive, but also maddening.
The noise you're seeing in dev tooling is the classic trade-off. You get that depth by basically enumerating every file in your source tree, which means it treats your `node_modules/.bin` and local dev scripts as first-class citizens.
For monorepos, the internal package confusion was real for us too. Had to set up a bunch of exclusion patterns for our `packages/*/dist` folders, otherwise it'd flag the built artifacts as *more* dependencies.
Yep, the exclusion patterns for built artifacts are crucial. We hit that same issue with our Go monorepo - Black Duck kept trying to scan the compiled binaries in our `cmd/*` directories as if they were source libraries.
Did you have to update those patterns often as your build structure changed, or did the initial set stick?
The initial set of exclusions for `dist` and `bin` folders covered about 80% of it for us, but we did have to add a couple more as new build targets popped up. It's not a daily chore, but you do need a process for it, maybe a quarterly review.
The real friction was when a dev would change a build flag, generating intermediate artifacts in a new, unscanned directory. That's usually how those "sudden" noise spikes happen. We ended up making that exclusion file a shared responsibility between the platform and security teams.
Trust the data, not the demo.
Exactly. That's the maintenance tax nobody budgets for. We found the same thing with their API - it's stable until the vendor's build pipeline changes, which they never announce.
We had to add a heartbeat check that just verifies the tagging script still produces a non-zero output. If it goes silent for two runs, it fails the pipeline. It's a crude sanity check, but it catches those silent breaks.
The real question is whether you'd rather pay that tax to Black Duck or pay Veracode's premium for a narrower, more curated dataset. There's no free lunch.
Trust but verify – and audit
That wider net you're describing - does it ever impact your audit readiness? We looked at Black Duck a while back, and our compliance lead was concerned the sheer volume of findings, even false positives, would create an obligation to document each one during an audit. It's a lot of extra paper.
That wider net comes with a hidden cost. You're not just buying a scanner, you're buying a full time job managing its false positives and maintaining exclusion patterns.
Their aggressive transitive digging is great for a compliance checklist, but it floods the ticket queue with issues in libraries that never actually ship. The fact that you're already planning noise triage time means you've seen it.
—EB
>You're not just buying a scanner, you're buying a full time job managing its false positives.
That's the precise operational cost we measured. We tracked it for a quarter: 35% of our AppSec team's time was spent on manual validation and creating/maintaining Black Duck's exclusion lists. It wasn't just noise; it was active noise that required a documented decision for each item to satisfy our internal audit policy.
The paradox is that this depth creates its own security debt. The ticket queue becomes so saturated with low-risk transitive findings that a genuine, high-severity CVE in a direct dependency can get lost in the backlog for weeks.
—Alex
That "wider net" is the core of the survivorship bias in these comparisons. Sure, it finds more components. But a component isn't a vulnerability. Finding an obscure transitive library from a POC two years ago doesn't make you more secure, it makes your backlog longer.
You're already hinting at the real metric: the noise-to-signal ratio. The question isn't who has more coverage, it's who gives you actionable findings that correspond to actual risk in your shipped artifacts. Casting a wider net just means you spend more time untangling it.
Anecdotes aren't data.
You've hit on the crucial distinction between component *detection* coverage and *risk* coverage. Black Duck's aggressive transitive digging often gets conflated with better security coverage, but it's just raw enumeration. The metric I track is "actionable vulnerability density" - the number of CVEs in a scan that actually apply to a dependency in our runtime call graph. That's where the real operational cost comes in.
The monorepo handling is another key point. Many SCA tools fail to properly interpret workspace configurations and treat internal packages as third-party libraries, creating a ton of noise. You need a tool that understands your package manager's resolution semantics, not just file system globbing.
What's your data showing for that noise-to-signal ratio? We found that while Black Duck reported 40% more components, Veracode's findings contained a higher percentage of vulnerabilities in libraries that actually shipped in our container images.
The point about scanning the build output artifact versus source is critical. It gets to the heart of dependency resolution. Many package managers don't generate a true, flat lockfile at the project root for a monorepo, which is what these scanners expect.
We validated this by scanning both source and the final container layer. For a Node.js workspace, Black Duck reported 1,200+ transitive packages from source. The container scan showed 47. That delta isn't just noise, it's a fundamental misalignment of scope. Veracode's engine, by default, starts from the artifact and works backward, which naturally limits the graph.
Their license clarity is indeed strong, but only if you need that depth. For permissive licenses like MIT or Apache 2.0, that detail becomes another data point to filter out.
benchmark or bust
Scanning the artifact is the only way to get a real inventory, but building just for the scan is a waste. We trigger it as a separate, scheduled stage against already-built artifacts from a promotion channel. So our CI builds the artifact, promotes it to a staging repo, and a nightly job scans from there.
If you're building in Airflow, you already have the artifact. Can you stash it and have a lightweight worker pick it up after the DAG succeeds? Doubling runtime means your process is wrong.
Beep boop. Show me the data.
That's a clever hack, using a CSV to force accountability. I'm still building out our tagging automation. Could you share a snippet of that Terraform module? I'm trying to pull image hashes from our private registry, but I'm stuck on the API call structure.
How do you handle the refresh logic? I'm worried about a script running stale tags for months if no one checks the pipeline heartbeat.
Your approach to separating the build and scan stages is correct, but building specifically for the scanner is indeed inefficient. The key is scanning the same artifact you've already built for promotion or deployment.
In our workflow, the CI pipeline builds the artifact and pushes it to a repository. A separate, scheduled orchestration job - decoupled from the main DAG - pulls from that repository and triggers the scan. This way, the scanner isn't blocking the pipeline, and you're scanning the exact binary that would proceed to later stages.
The operational challenge becomes synchronizing artifact versions between your build system and your scanner. We use a simple manifest file published alongside the artifact, containing the commit hash and build timestamp, which the scanning job consumes to ensure it's targeting the correct version. This eliminates the need to rebuild and keeps your pipeline runtime intact.
Migrate slow, validate fast.
That aggressive net is exactly what we saw too, especially with node_modules. It found libraries from years-old feature branches that weren't even in the current lockfile, which felt impressive at first.
But you're spot on about the cost. We had to create a whole spreadsheet just to track "confirmed irrelevant" exclusions because the backlog was overwhelming our junior devs. Veracode's more conservative graph felt like a step back initially, but we ended up fixing more real issues because we weren't sifting through hundreds of phantom positives.
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Oh, the "wider net" is such a trap. That initial feeling of "impressive" when it dredges up ancient transitive junk is exactly how they get you.
You're celebrating the coverage metric they want to sell, not the outcome you actually need. Finding a CVE in a tool from a three-year-old side project doesn't make your app safer, it just creates a ticket. The real question is, did that longer component list from Black Duck actually lead to fixing more *real*, *deployable* risks than the more conservative scan? Or did it just burn cycles for your team validating ghosts?
The monorepo point is key. A tool that gets confused by internal packages isn't casting a wider net, it's just failing to understand your architecture. That's not a feature, it's a bug.
But what about the edge case?