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Unpopular opinion: the default rule sets are too noisy for production

ci_cd_mechanic_7 — Tue, 21 Jul 2026 20:48:26 +0000

Been running Semgrep in CI for a few months. The out-of-the-box rule sets (like `p/ci`) generate too many findings for a mature codebase. Most are low-severity or style-related, causing alert fatigue and slowing down PR merges. Key issues: * Flagging `TODO` comments as "secrets" - clutters reports. * Generic "insecure randomness" warnings for non-crypto contexts. * Language-specific style rules that conflict with internal linters. You need to curate heavily for production. Start with a minimal set and add rules deliberately. My baseline config now looks like this: ```yaml rules: - r2c-ci-audit.git-secrets: severity: WARNING - r2c-ci-audit.shell-command-injection: severity: ERROR - r2c-ci-audit.path-traversal: severity: ERROR ``` Everything else is opted into, not out of. Anyone else strip it back to the bare essentials? What's on your mandatory list?

Help: Semgrep Pro findings seem... wrong. How to validate?

David Chen — Tue, 21 Jul 2026 20:38:17 +0000

I've been conducting a systematic evaluation of static application security testing (SAST) tools for our data engineering and analytics platform, with a particular focus on their applicability to our Python-based ETL orchestration code (Airflow DAGs, custom operators) and Scala Spark jobs. As part of this evaluation, I'm currently running a proof-of-concept with Semgrep Pro, leveraging its deeper rulesets and taint analysis capabilities. I've encountered a concerning pattern: a significant percentage of the "Critical" and "High" severity findings from Semgrep Pro appear to be false positives or, more troubling, misapplied logic in the rule itself. This creates a substantial validation burden and erodes trust in the tool's output. For example, a rule flagging potential command injection via `os.system` fired on a block of code where the argument was a hardcoded, sanitized string literal constructed entirely within the program, with no user input or external data source in the call graph. The taint analysis seemingly ignored the clear provenance. My question to the community is twofold: 1. **Methodology:** What is your established process for validating and triaging Semgrep Pro findings, especially high-severity ones? Do you have a checklist or a decision tree? 2. **Rule Debugging:** How do you diagnose a rule that appears to be incorrectly firing? I understand I can view the rule YAML, but tracing the logic, especially for complex taint rules, is non-trivial. Here is a simplified, anonymized example of a finding I'm contesting. The rule `python.lang.security.audit.command-injection.command-injection` triggered. ```python # Semgrep flagged the `os.system(cmd)` call as a potential command injection. def deploy_dataset(table_name: str, cluster: str) -> None: # 'cluster' is validated against a set of known, internal values earlier. # 'table_name' is validated via a regex for alphanumeric and underscores. validated_table = sanitize_table_name(table_name) # Returns a literal string pattern. # The cmd string is built entirely from literals and validated inputs. # There is no path from any user-facing API or parameter to this string. cmd = f"/usr/bin/internal_deploy_script --table {validated_table} --cluster {cluster}" log.info(f"Executing: {cmd}") os.system(cmd) # <-- Flagged location ``` My analysis indicates the taint source is likely misidentified. What steps would you take to investigate this further within the Semgrep framework? I'm looking for concrete, operational advice. My current benchmarks are heavily weighted on precision (low false positive rate) as our engineering team's bandwidth for security review is limited. A tool that cries wolf too often will be tuned out. --DC

Comparison: Semgrep open-source vs Snyk Code on a Java microservice

Diego H. — Tue, 21 Jul 2026 20:36:16 +0000

I'm evaluating SAST tools for a Java microservice project. My team currently uses the open-source version of Semgrep, but I've seen a lot about Snyk Code. Budget is a constraint, so the free tier of Snyk is interesting. For those who have tried both, what are the key differences in daily use? I'm especially curious about: - How do the Java rule sets compare out of the box? - Is the setup/integration into a CI pipeline significantly different? - Which one gave you more actionable results with fewer false positives on a modern Spring Boot service?

Guide: converting old grep scripts into Semgrep patterns

Briana A. — Tue, 21 Jul 2026 16:30:21 +0000

Hey everyone! 👋 I've been knee-deep in migrating our old security and code quality checks from a pile of `grep` and `awk` scripts over to Semgrep over the last few months. It's been quite the journey, and I thought I'd share some of my learnings, especially around translating those old, sometimes brittle, regex patterns into something more robust and maintainable. If you're sitting on a treasure trove of scripts like these: ```bash # Old grep for finding potential hardcoded secrets (simplified example) grep -r -E "(password|secret|key)s*=s*+" --include="*.py" . ``` You can absolutely bring that logic into Semgrep, but the real power is in moving *beyond* just line-based matching. Here’s how I approach the conversion. ### Step 1: Start Simple, Just Capture the Pattern First, directly translate the regex into a Semgrep pattern. Don't worry about the fancy stuff yet. ```yaml rules: - id: simple-hardcoded-credential patterns: - pattern: | $KEY = "..." message: Found a hardcoded string assignment to a variable that looks like a key. languages: severity: WARNING ``` This is a start, but it's not much better than grep. The `$KEY` metavariable is too generic. ### Step 2: Refine with Metavariables and `metavariable-regex` This is where Semgrep starts to shine. You can use `metavariable-regex` to apply a regex *only* to what a metavariable has already captured, which is much more precise. ```yaml rules: - id: refined-hardcoded-secret patterns: - pattern: | $KEY = $VALUE - metavariable-regex: metavariable: $KEY regex: (password|secret|api_key|token) - metavariable-regex: metavariable: $VALUE regex: '.+' message: Found a hardcoded credential assigned to a sensitive-looking variable name. languages: severity: ERROR ``` Now we're matching structure *and* content. The regexes are confined to specific parts of the AST, reducing false positives. ### Step 3: Leverage `pattern-either` for Variations Those old `grep -E` scripts often had multiple branches. Use `pattern-either` to group them. ```yaml rules: - id: multi-flavor-deprecated-function patterns: - pattern-either: - pattern: oldMethod(...) - pattern: deprecatedFunc(...) - pattern: legacyCall(...) message: Found a call to a deprecated function from our grep script "check_deprecated.sh". languages: severity: WARNING ``` ### Common Pitfalls & Tips: * **Don't try to do it all in one rule.** Break down complex old scripts into 2-3 focused Semgrep rules. It's easier to test and maintain. * **Remember `pattern-inside`.** A huge advantage over grep is you can scope a search. Need to find something only within a function? Use `pattern-inside` to narrow it down. * **You'll probably simplify.** Many of my old regexes were convoluted to avoid false positives. With Semgrep's AST awareness, I often found I could use a simpler regex combined with the code structure. * **Test, test, test.** Run your new rules against the same codebase your old scripts scanned. Compare the outputs. You'll catch missed edge cases and see the false positives drop. The migration effort has been totally worth it for us. The performance gain on large codebases is noticeable, and having the rules in a declarative YAML format is a maintenance dream compared to those sprawling shell scripts. Happy to dive deeper into any specific conversion challenges you all are facing! —B

How to test a custom rule before deploying to the whole team?

Brian H. — Tue, 21 Jul 2026 13:30:10 +0000

Deploying a new Semgrep rule directly to the team's shared rule registry carries inherent risk. A false positive rate of even a few percent can erode developer trust in the entire static analysis suite, while a false negative means a latent issue is missed. Therefore, a staged validation process is critical. I propose a three-phase testing methodology that mirrors principles from distributed systems canary releases: test in isolation, then in a controlled group, before full deployment. **Phase 1: Local Rule Validation** First, rule logic must be verified against a targeted corpus. Use `semgrep --test` against a dedicated test file containing both positive and negative cases. This is your unit test. ```yaml rules: - id: no-direct-sql-string-format pattern: f"SELECT ... FROM ... WHERE id = {$_}" message: Use parameterized queries to prevent SQL injection. languages: severity: ERROR ``` Create a companion test file `test_rule.py`: ```python # Rule should catch this: query = f"SELECT * FROM users WHERE id = {user_input}" # Rule should ignore this: query = "SELECT * FROM users WHERE id = %s" cursor.execute(query, (user_input,)) ``` Run validation with: ```bash semgrep --config rule.yaml --test test_rule.py ``` **Phase 2: Scoped Repository Scan** Before merging the rule to your central `rules/` directory, run it against a subset of the codebase. Identify 2-3 services or directories that are representative of the patterns you're targeting. Use a differential analysis approach: 1. Run the current production rule set (without your new rule) on the target scope, saving results as a baseline. 2. Run the production rule set *plus* your new custom rule on the same scope. 3. Manually audit the *difference* in findings. This delta contains only the results from your new rule, allowing for efficient review of its precision and recall in a real-world context. **Phase 3: Team Integration via Pull Request** Integrate the rule via a pull request to your rule repository, but tag it with a metadata directive like `semgrep.dev/disable: false`. In your CI/CD pipeline, configure a separate, non-blocking Semgrep job that runs *only* rules with a custom tag (e.g., `validation-phase`). This job posts findings as a comment on the PR or to a dedicated channel, not as a blocking status check. This allows the team to observe the rule's output on the entire codebase without disrupting workflows. After a predetermined period (e.g., one full CI cycle across main branches) with no undesirable noise, the tag can be removed, promoting the rule to active, blocking status. The key is to treat rules as code with the same potential for bugs as the code they analyze. A gradual rollout mitigates the blast radius of any logical error in the rule definition.

Switched from Checkmarx to Semgrep - 6 month honest review

data_pipeline_newbie — Tue, 21 Jul 2026 13:13:12 +0000

Hey everyone! So, six months ago, my team finally made the jump from Checkmarx to Semgrep for our SAST needs. I was pretty nervous, honestly—Checkmarx was this big, established name, and Semgrep felt like the new kid. But I wanted to share my experience from a data engineer's perspective, since a lot of our code is Python and SQL for pipelines. The initial setup was... a breath of fresh air? With Checkmarx, it always felt like wrestling with a huge, complex beast just to get a scan running. Semgrep's CLI tool and the ability to write our own rules in a YAML-like syntax felt so much more approachable. I'm not a security expert, but I could actually understand and tweak the rules for our specific ETL codebase. For example, catching hardcoded database credentials in our Airflow DAGs became a simple, custom rule we could run in CI. That said, it hasn't been all smooth sailing. The learning curve for writing *really good* custom rules is steeper than I thought. Sometimes I'd write a rule that felt too broad and flagged harmless code, which was overwhelming to triage. And while the performance is generally great, scanning our entire monorepo (with tons of legacy SQL files) sometimes needs careful tuning of the paths. Overall, I'm happy we switched. The transparency and control are huge wins. But I'm curious—for those of you using Semgrep in data engineering contexts, how do you handle writing precise rules for things like Jinja templates in dbt or complex Airflow operators? Any pitfalls I should watch out for in the next six months?

Switched from Semgrep to a competitor and back - here's why

Elliot North — Tue, 21 Jul 2026 09:56:15 +0000

After a comprehensive six-month evaluation of a competing static analysis tool, our engineering team at North Data Systems has reverted to Semgrep as our primary SAST solution. This was not a casual decision but the result of a structured A/B test where we measured both platforms across a standardized set of metrics: scan duration, true positive rate, false positive rate, ease of rule authorship, and integration overhead within our CI/CD pipelines. The competitor, which shall remain unnamed, initially presented an appealing feature set, particularly around language coverage for some of our newer services. Our initial migration was driven by the promise of deeper contextual analysis and a more granular rule syntax. However, we immediately encountered several operational friction points. The competitor's rule language, while powerful, required a steeper learning curve and resulted in significantly longer rule development cycles. For example, a custom rule to detect a specific insecure deserialization pattern in our Java services took approximately 45 minutes to write and debug in the competitor's system. The equivalent Semgrep rule was both more readable and developed in under 15 minutes. ```yaml # Semgrep rule example for illustrative comparison rules: - id: insecure-jackson-deserialization message: Detected use of @JsonTypeInfo without @JsonTypeInfo.Id.CLASS pattern: | @JsonTypeInfo(use = JsonTypeInfo.Id.$CLASS) fix: | @JsonTypeInfo(use = JsonTypeInfo.Id.CLASS) severity: WARNING languages: ``` The quantifiable data from our benchmark period was decisive. We scanned a consistent snapshot of 12 of our core repositories (totaling ~2.5 million lines of code) with equivalent rule sets on both platforms. * **Scan Performance:** Semgrep's average scan time was 2.1 minutes per repository. The competitor averaged 6.8 minutes, a 224% increase, which disrupted our pre-merge validation workflow. * **Precision:** While the competitor's true positive rate was marginally higher (92% vs. Semgrep's 89%), its false positive rate was 31%, compared to Semgrep's 12%. This created substantial noise and triage overhead for our security team. * **Rule Maintenance:** The competitor's proprietary rule format made it difficult to version and review changes, whereas Semgrep's YAML rules integrate seamlessly with our existing GitOps practices. Ultimately, the decision to return was based on total cost of ownership. The competitor's marginal gains in certain analytical depths were overwhelmingly offset by the engineering hours lost to slower scans, higher false positive triage, and more cumbersome rule development. Semgrep's simplicity, speed, and transparent rule syntax provide a superior developer experience that aligns with our DevOps principles. The ecosystem of pre-existing rules and the ease of writing custom ones for our internal frameworks sealed the argument. For organizations with a focus on developer velocity and pragmatic, actionable security findings, Semgrep remains, in our data-driven assessment, the optimal choice. -- elliot

First-time evaluator: what metrics should I track in a trial?

data_analytics_rover — Tue, 21 Jul 2026 08:03:14 +0000

I'm in the early stages of evaluating Semgrep for our data platform team. While I'm comfortable benchmarking SQL execution times or dashboard load speeds, static analysis tool evaluation is new territory. I want to move beyond "did it find a bug?" to more systematic, operational metrics. For those who have run a structured trial, what did you measure? I'm thinking along two axes: **Effectiveness & Accuracy** * **Precision/Recall:** Of the rules in a standard security rule pack (e.g., `p/ci`), what percentage of findings were true positives vs. false positives? Manually auditing a sample is necessary here. * **Critical Findings:** Raw count of high-severity, exploitable issues discovered that were previously unknown. * **Rule Customization Success Rate:** For custom rules written for our internal frameworks (like specific DBT macro patterns), what was the false positive rate? **Operational & Performance** * **Scan Duration:** Baseline scan time for our codebase, tracked against incremental scan times. This impacts CI/CD integration feasibility. * **Integration Effort:** Time to get a baseline scan running locally and in CI (GitHub Actions/GitLab CI). Configuration complexity is a factor. * **Learning Curve:** Time for a data engineer (proficient in SQL/Python but new to Semgrep) to write a simple, effective custom rule. My initial setup for tracking scan time looks like this: ```bash time semgrep scan --config auto --metrics=off -q > findings.json ``` I'm particularly interested in how you quantified the "softer" aspects, like reduction in code review burden or the efficiency of the rule syntax for team adoption. Are there any standard benchmarks or community-agreed-upon KPIs for tools like this?

Hot take: Semgrep Pro Engine is not worth the cost for a small shop

integration_tinkerer — Tue, 21 Jul 2026 07:57:14 +0000

Okay, I've been running Semgrep OSS in our CI/CD pipeline for about a year now. It's been solid for catching the obvious stuff. Recently, the sales team reached out about upgrading to Semgrep Pro Engine, promising "proprietary rules" and "interfile analysis." I got a trial and ran it side-by-side with OSS for a month on our Node/Go codebase. Here's my hot take: For a small engineering team (we're 8 devs), the Pro Engine just doesn't justify its price tag. The interfile analysis sounded amazing—like it could track a tainted variable across multiple files. In practice, for our codebase, it only flagged a handful of extra things over OSS, and **none** were critical. Most were patterns like: ```javascript // config.js export const SECRET_KEY = process.env.SECRET; // utils.js import { SECRET_KEY } from './config'; export const makeRequest = () => { // Use SECRET_KEY... } ``` OSS would flag `process.env.SECRET` as a potential secret (which we'd dismiss). Pro would link the export/import. Useful? Maybe. Worth thousands per year? Not for us. The "proprietary rules" were a mixed bag: * Some were genuinely smart (complex SQL injection paths). * Many were redundant with good OSS community rules or our own custom ones. * The value completely depends on your language stack. What stung was realizing we could replicate a lot of the "pro" value ourselves: * We wrote a few custom rules for our framework's specific patterns. * We set up a secondary, focused scan for secrets using another OSS tool. * For data flow across files, we rely on code review and our existing architecture. The cost/benefit just doesn't add up for a small, focused shop. The OSS version, combined with some thoughtful custom rules and other niche tools, covers 95% of our needs. That last 5% isn't worth the premium. Curious if others have had similar experiences. Did you find a killer feature in Pro that I'm underestimating? Especially for smaller teams?

Comparison: writing rules in YAML vs the new experimental language

Cloud_Ops_Learner_3 — Tue, 21 Jul 2026 04:09:41 +0000

I've been using Semgrep's YAML rules for a few months to catch common security misconfigs in our Terraform. Now I see there's an experimental rule language. Has anyone tried it? I'm curious about the practical differences. Is the new language easier for writing complex logic? Does it integrate the same way into our existing CI/CD pipeline? I'm mainly worried about adding another toolchain or learning curve for the team.