Having integrated numerous static analysis tools into CI/CD pipelines for API and data flow validation, I've been evaluating Semgrep as a potential addition to our security and quality gates. The promise of a unified, polyglot pattern matcher is compelling for our heterogeneous service landscape, which spans legacy REST APIs, modern webhook handlers, and various middleware transformation layers.
My preliminary tests, however, have surfaced a significant concern regarding signal-to-noise ratio. In a controlled scan of a representative Java Spring Boot service and a Node.js Express service, the default rule sets (specifically `java.lang.security.audit` and `js.lang.security.audit`) generated a notable volume of alerts that, upon manual triage, were determined to be benign or contextually inappropriate. This is problematic for CI integration, where a high false-positive rate breeds alert fatigue and can lead to critical findings being ignored.
To ground this discussion, I'll share a concrete example from a data serialization module. Semgrep flagged a potential injection vulnerability using rule `java.lang.security.audit.unsafe-deserialization.unsafe-deserialization`:
```java
// Code flagged by Semgrep
ObjectInputStream ois = new ObjectInputStream(inputStream);
MyDataObject obj = (MyDataObject) ois.readObject();
```
The context, omitted from the rule's logic, is that this stream is strictly processing internally generated, cryptographically signed payloads within a isolated Kubernetes pod network—a risk profile the rule cannot inherently discern. This is a classic case of a valid pattern lacking environmental context.
My primary questions for the community are therefore procedural and technical:
* **Rule Tuning Strategies:** For those running Semgrep in CI, what has been your most effective approach for managing false positives? Are you primarily:
* Creating granular rule exclusions via `paths:` or `metavariable-regex` in your configurations?
* Forking and modifying the upstream rule sets to better fit your architecture's assumptions?
* Implementing a post-processing step to filter results based on a bespoke allow-list?
* **Configuration Specifics:** Could you share snippets of a production `.semgrep.yml` CI job configuration that demonstrates a balanced approach? I'm particularly interested in the interplay between `--severity` filters, `--exclude-rule` directives, and the use of custom rule directories.
* **Language-Specific Experience:** Has the false-positive rate varied significantly across the languages you scan? Anecdotal evidence suggests tighter ecosystems like Go might yield cleaner results than more dynamic ones like Python or JavaScript.
The goal is not to eliminate all false positives—that's unrealistic—but to achieve a manageable baseline where the CI step fails only for genuinely actionable findings. I am less interested in theoretical capabilities and more in practical, sustained implementation patterns that have proven stable over months of deployment.
-- Ivan
Single source of truth is a myth.
Yeah, that `unsafe-deserialization` rule is a classic one for noise. I ran into the same thing where it flagged our use of `ObjectInputStream` in a completely isolated, internal testing harness that never touches user data.
What worked for us was immediately moving to custom rules and a tiered approach. We created a `.semgrepignore` file to mute that specific rule path for certain, safe directories. More importantly, we started writing our own, context-aware rules for the actual risky patterns in our codebase.
The secret sauce? Don't just run the full audit suite in CI. We built a pipeline stage that first runs a small, curated set of "blocker" rules we maintain ourselves. They have near-zero false positives. The full scan with the community rules runs nightly, and findings go to a dashboard for the security team to review, not to the dev PR. It cut the CI noise down by about 90%.
— francesc
Totally agree on the tiered approach! We do something similar.
Our CI runs a tiny "gatekeeper" rule set, like 5-10 rules max, that must pass. The full audit runs on a schedule and posts to Slack.
One caveat: getting that curated rule set right takes real work. We had to tweak our "blocker" SQLi rule three times before it stopped yelling about our query builder. The path patterns in `.semgrepignore` were a lifesaver for test utilities.
Let's build better workflows.