Set Up Semgrep SAST Scanning: 13 Steps, $40/Mo Tier [2026]

Security teams had a rough September and October of 2026. CrowdStrike’s September Patch Tuesday breakdown flagged dozens of fresh Microsoft fixes, Fortinet’s FortiMail flaw (CVE-2026-104286) landed on CISA’s Known Exploited Vulnerabilities catalog after confirmed active exploitation, and Atlassian shipped an out-of-band advisory for CVE-2026-21589, a pre-authentication arbitrary file read bug hitting almost its entire Data Center product line. SonicWall followed on October 7 with a pre-auth SSRF patch for its SMA 1000 appliances. Every one of those bugs traces back to a pattern that could have been caught before the code ever shipped: a missing auth check, an unsanitized path, a server making requests it shouldn’t.

That is exactly the gap Semgrep is built to close. It is a static application security testing (SAST) tool that scans source code for the patterns behind these bugs, running the check in seconds inside a pull request instead of months later inside an incident report, long before a DFIR team ever needs to get involved. This tutorial walks through installing Semgrep CLI, running your first scan, writing a custom rule, wiring it into CI/CD, and triaging findings like a security engineer rather than a tool operator. By the end you will have a working pipeline that blocks the next SSRF or path traversal bug before it merges.

This matters beyond any single CVE headline. Most software security programs still lean heavily on penetration tests and bug bounty submissions, both of which find bugs after the code has already shipped to production and, in many cases, after an attacker has already had a window to find the same bug first. Shifting that detection earlier, into the pull request itself, is the core idea behind what the industry calls “shift-left” security. Semgrep is one of the more practical ways to do that shift without hiring a dedicated application security team, because the open source engine runs on a laptop in seconds and the rule syntax is readable enough that a backend engineer with no security background can write a usable rule in an afternoon.

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What Semgrep Actually Does (and What It Doesn’t)

Semgrep is an open source static analysis engine that searches source code using patterns written in a syntax that looks almost exactly like the code itself. Instead of compiling a full abstract syntax tree and running deep symbolic execution the way heavier tools like CodeQL do, Semgrep matches structural patterns fast enough to run on every commit. According to the project’s own GitHub repository, the engine currently supports more than 30 languages and ships with over 2,000 community and official rules covering security, correctness, and dependency vulnerabilities. The most recent CLI release, version 1.179.0, shipped on October 1-2, 2026, and added parsing support for Bun’s text-format bun.lock files inside the Semgrep Supply Chain module, alongside updates to C/C++ parsing.

What Semgrep is not: a dynamic scanner, a fuzzer, or a replacement for a web application firewall. It reads code without running it, so it will not catch a misconfigured cloud bucket or a logic flaw that only shows up at runtime. It also will not catch every zero-day pattern a nation-state research team invents. What it is extremely good at is the boring, repetitive classes of bugs that keep showing up in CVE disclosures: hardcoded secrets, SQL built with string concatenation, path traversal from unsanitized user input, SSRF from unchecked outbound requests, and missing authentication decorators. Those four categories alone account for a large share of the critical CVEs disclosed across 2026, and several of them map directly to the OWASP Top Ten categories tracked by the OWASP Foundation.

It also helps to understand how the engine reaches a verdict, because that shapes how much you should trust any single finding. Semgrep parses your source file into an abstract syntax tree, then checks whether any part of that tree structurally matches the pattern defined in a rule, largely ignoring superficial differences like variable names, whitespace, or argument order. That is faster than full data-flow analysis because it does not need to trace a value across every function call in the program, but it also means the engine can occasionally flag code that looks dangerous in isolation but is actually safe because of validation happening somewhere else entirely. Pro-tier taint analysis narrows that gap by tracking a value from its source (user input) to its sink (a dangerous function call) across file boundaries, which is the main technical reason the paid tier exists at all rather than being a pure feature-gating decision.

Prerequisites and Versions

Before starting, make sure your environment matches the versions below. Semgrep moves fast and the CLI is backward compatible, but the commands in this guide assume the following baseline.

Requirement	Minimum Version / Spec	Notes
Semgrep CLI	1.179.0 (released Oct 1-2, 2026)	Install via pip, Homebrew, or Docker
Python	3.9 or newer	Only needed for the pip install path
Operating System	macOS, Linux, or WSL2 on Windows	Native Windows support is limited; WSL2 is recommended
Git	2.30+	Required for diff-aware scanning
Docker (optional)	24.x or newer	Alternative install path, useful for CI runners
RAM	4 GB minimum, 8 GB recommended	Scales with repository size and rule count
Disk space	2 GB free	For the CLI, cached rules, and scan output
GitHub/GitLab account	Any tier	Needed for the CI/CD integration steps

You do not need a Semgrep account to follow steps 1 through 9. The free Community tier runs entirely on your machine with no login. Steps covering the hosted AppSec Platform are optional and clearly marked.

Step 1: Install Semgrep CLI

There are three supported install paths. Pick whichever fits your existing toolchain; all three install the same underlying binary.

# Option 1: pip (works on macOS, Linux, WSL2)
python3 -m pip install semgrep

# Option 2: Homebrew (macOS/Linux)
brew install semgrep

# Option 3: Docker (no local Python needed, best for CI)
docker pull semgrep/semgrep:latest

Confirm the install worked and check your version against the current release:

semgrep --version
# Expected output: 1.179.0 (or newer)

If the command is not found after a pip install, your Python user scripts directory is probably missing from your shell’s PATH. On most Linux distributions that directory is ~/.local/bin; add it to your shell profile and reopen the terminal.

Step 2: Run Your First Scan with the Auto Config

Navigate to any project directory and run Semgrep with the auto config, which pulls a curated rule set matched to the languages it detects in your repo.

cd /path/to/your/project
semgrep scan --config auto .

On a mid-sized repository (roughly 50,000 lines of code) this typically finishes in under two minutes on a 4-core laptop. The output lists each finding with a file path, line number, severity, a short explanation, and a snippet of the offending code. A clean first run on a well-maintained codebase might surface a handful of findings; a repository that has never been scanned before often surfaces dozens, most of them low-severity.

Example Output

┌──────────────────┐
│ 7 Code Findings  │
└──────────────────┘

  app/routes/export.py
     ❱ python.lang.security.audit.path-traversal.path-traversal
          Found variable derived from user input used in a file path.
          This could lead to a path traversal vulnerability...
          42┆ filepath = os.path.join(UPLOAD_DIR, request.args.get("file"))

  app/services/fetch_remote.py
     ❱ python.requests.security.disabled-cert-validation
          Server Side Request Forgery (SSRF) risk: certificate validation
          disabled on outbound request.
          18┆ requests.get(target_url, verify=False)

Ran 2,164 rules on 318 files: 7 findings.

Notice the two findings above map almost exactly to the bug classes behind the SonicWall SSRF patch and the Atlassian path traversal advisory mentioned earlier. That overlap is not a coincidence: path traversal and SSRF are two of the most common root causes behind the critical CVEs disclosed across the industry this year, and they are also two of the easiest patterns for a SAST engine to flag. The MITRE-maintained CWE Top 25 ranks both categories among the most dangerous software weaknesses tracked industry-wide.

Step 3: Understand Severity and Confidence Levels

Every Semgrep finding carries two separate ratings that beginners frequently confuse: severity and confidence. Severity (ERROR, WARNING, INFO) describes how bad the bug would be if real. Confidence (HIGH, MEDIUM, LOW) describes how sure the rule author is that the match is a true positive rather than a false alarm. A finding can be high severity and low confidence at the same time, which means it is worth a human look but should not auto-fail a build on its own.

Severity	Typical Meaning	Recommended CI Action
ERROR	Exploitable vulnerability class (SSRF, injection, hardcoded secret)	Fail the build, block merge
WARNING	Risky pattern, exploitability depends on context	Comment on PR, do not block
INFO	Style or best-practice deviation	Log only

Filter output by severity early so your first CI integration does not flood engineers with INFO-level noise and get disabled out of frustration within a week. That single mistake is responsible for more abandoned SAST rollouts than any technical limitation of the tool itself.

Step 4: Scope the Scan to What Changed

Scanning an entire repository on every commit wastes CI minutes. Semgrep supports a diff-aware mode that only evaluates files touched since a baseline commit, which is what you want for pull request checks.

# Scan only files changed vs the main branch
semgrep scan --config auto --baseline-commit main .

# Scan only staged changes before a commit
git diff --name-only --cached | xargs semgrep scan --config auto

This is also where most teams first hit a real limitation: diff-aware scanning can miss a vulnerability that was introduced months ago in a file nobody is currently editing. Run a full, unscoped scan on a schedule (weekly is a reasonable cadence for most teams) in addition to the fast diff-aware checks on every PR.

Step 5: Write a Custom Rule

The built-in registry covers general patterns well, but every codebase has internal APIs, wrappers, and conventions the public rules know nothing about. Semgrep rules are written in YAML and the pattern syntax mirrors real code, so most engineers can write a usable rule within an hour of first trying.

Here is a rule that flags any call to an internal run_shell() helper where the command argument is not a string literal, which is a common way homegrown command-injection bugs sneak into a codebase:

rules:
  - id: unsafe-run-shell-call
    languages: [python]
    severity: ERROR
    message: >
      run_shell() called with a non-literal command argument. This can allow
      command injection if the value traces back to user input. Use
      run_shell_safe() with an argument list instead.
    patterns:
      - pattern: run_shell($CMD)
      - metavariable-pattern:
          metavariable: $CMD
          patterns:
            - pattern-not: "..."
            - pattern-not-regex: '^".*"$'
    metadata:
      category: security
      cwe: "CWE-78: Improper Neutralization of Special Elements used in an OS Command"
      owasp: "A03:2021 - Injection"

Test it against a single file before rolling it out org-wide:

semgrep scan --config ./rules/unsafe-run-shell-call.yaml app/legacy/

Keep custom rules in a version-controlled directory inside your repo (a top-level .semgrep/ folder works well) so they go through the same review process as application code. The OWASP Cheat Sheet Series is a useful reference when deciding which internal patterns deserve their own rule.

Step 6: Tune Out False Positives

No SAST tool ships zero false positives, and chasing that goal wastes more time than it saves. Semgrep gives you three ways to suppress a specific match without disabling the rule everywhere.

# Inline suppression with a required justification comment
filepath = safe_join(UPLOAD_DIR, filename)  # nosemgrep: python.lang.security.audit.path-traversal.path-traversal -- filename is validated by safe_join()

# Suppress a rule for an entire file via .semgrepignore
echo "tests/fixtures/*.py" >> .semgrepignore

# Suppress a rule globally via config
semgrep scan --config auto --exclude-rule python.flask.security.audit.insecure-default-dict

Require a short justification after every nosemgrep comment in your PR review checklist. An unexplained suppression is indistinguishable from a developer silencing a real bug, and six months later nobody will remember which case it was.

Step 7: Integrate with GitHub Actions

Add a workflow file so every pull request gets scanned automatically. This example fails the build only on ERROR-severity findings, following the triage policy from Step 3.

name: Semgrep SAST
on:
  pull_request:
    branches: [main]

jobs:
  semgrep:
    runs-on: ubuntu-latest
    container:
      image: semgrep/semgrep
    steps:
      - uses: actions/checkout@v4
      - run: |
          semgrep scan \
            --config auto \
            --baseline-commit ${{ github.event.pull_request.base.sha }} \
            --severity ERROR \
            --error

The --error flag makes Semgrep exit with a non-zero status when ERROR-level findings exist, which GitHub Actions reads as a failed check. Teams on GitLab CI follow the same logic with a near-identical .gitlab-ci.yml stage using the same Docker image.

Step 8: Add a Pre-Commit Hook for Instant Feedback

CI feedback can take minutes. A pre-commit hook catches the same issues in seconds, before the code ever leaves a developer’s machine. Install the pre-commit framework and point it at Semgrep:

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/semgrep/semgrep
    rev: v1.179.0
    hooks:
      - id: semgrep
        args: ['--config', 'auto', '--error', '--severity', 'ERROR']

pip install pre-commit
pre-commit install

Pin the rev field to a specific release rather than tracking a moving tag. Semgrep’s rule registry updates frequently, and an unpinned hook can start failing commits with no code changes on your side when a rule definition shifts upstream.

Step 9: Scan Dependencies with Semgrep Supply Chain

Source code is only half the attack surface. Semgrep Supply Chain analyzes lockfiles and package manifests to flag known-vulnerable dependencies, and per the project’s own repository documentation it currently covers 12 languages across 15 package managers. The October 1-2, 2026 release of version 1.179.0 added parsing for Bun’s text-format bun.lock files alongside package.json, including dependency-relationship tracking, and an earlier update in the same release cycle extended attribution to Gradle module build files rather than only the root manifest.

# Requires linking to the Semgrep AppSec Platform (free tier available)
semgrep login
semgrep ci --supply-chain

Unlike the pure code-scanning rules, Supply Chain findings require a logged-in session because the vulnerability database it checks against is hosted, not bundled locally. The Community tier includes this feature at no cost; it is not gated behind the paid plan.

Step 10: Triage Findings Like a Security Engineer

A scan is only useful if someone acts on it. Use this triage order when a scan returns more findings than your team can fix immediately:

• Fix every ERROR-severity finding in code that handles authentication, file paths, or outbound network requests first — these map directly to the bug classes behind the Atlassian, SonicWall, and FortiMail disclosures this year.
• Fix hardcoded secrets next, regardless of severity rating, and rotate the exposed credential immediately — a secret in git history is compromised the moment it is committed, even if you delete it later. Pairing Semgrep with a dedicated secrets scanner like GitGuardian ggshield at the pre-commit layer catches these before they even reach a remote branch.
• Batch WARNING-level findings into a backlog ticket per module rather than fixing them one commit at a time.
• Revisit suppressed findings quarterly; a false positive today can become a true positive after a refactor changes how a function is called.

Step 11: Compare Community, Team, and Enterprise Tiers

Everything covered so far runs entirely on the free, open source Community tier. Paid tiers add centralized dashboards, cross-file taint analysis, and proprietary Pro rules. According to Semgrep’s official pricing page, paid Semgrep plans start at $30 per month per contributor, covering Pro Rules, cross-file and taint-flow analysis, an AI-assisted triage feature, and SSO. Enterprise pricing is quote-based and was not publicly listed as of this writing.

Tier	Price	Key Capabilities
Community	Free	Open source CLI, 2,000+ community rules, unlimited scans and repos, basic CI/CD integration, Supply Chain scanning
Team / Pro	From $30/month per contributor (per Semgrep, 2026)	Pro Rules registry, cross-file analysis, taint-flow tracking, AI Assistant, SSO, one-click deployment
Enterprise	Custom quote	Dedicated support, advanced compliance reporting, custom SLAs

Most teams should run Community-tier Semgrep in CI from day one and only evaluate the paid tier once the backlog of ERROR findings is under control. Paying for cross-file analysis before you have a working single-file triage process just produces a bigger backlog, faster.

Step 12: Monitor Scan Performance at Scale

As a monorepo grows past a few hundred thousand lines, full scans slow down. Three adjustments keep CI fast without sacrificing coverage:

• Use --jobs to match the CPU count of your CI runner instead of the Semgrep default.
• Split rule packs by language with targeted configs (for example, a Python-only ruleset for a Python-only service) instead of running the full auto config on every polyglot monorepo.
• Cache the Semgrep rule registry download between CI runs; re-downloading 2,000+ rules on every job adds unnecessary network latency.

semgrep scan --config auto --jobs 4 --metrics off .

Step 13: Build a Complete Working Project

Put it all together with a minimal but complete setup you can drop into any repository. Create this directory structure:

your-project/
├── .semgrep/
│   └── custom-rules.yaml
├── .semgrepignore
├── .pre-commit-config.yaml
└── .github/
    └── workflows/
        └── semgrep.yml

With the custom rule from Step 5, the ignore file from Step 6, the pre-commit hook from Step 8, and the GitHub Actions workflow from Step 7 all in place, you now have three layers of defense: instant local feedback before a commit, a blocking check on every pull request, and a scheduled full-repository sweep for drift. Run the full sweep manually once to confirm the baseline is clean:

semgrep scan --config auto --config ./.semgrep/custom-rules.yaml --json --output semgrep-baseline.json .

Commit that baseline JSON file to a private location (not the public repo) so the next engineer who joins the project has a reference point for “what did we already know about and accept.”

Common Pitfalls to Avoid

These mistakes account for most failed or abandoned Semgrep rollouts, based on how teams typically misuse SAST tooling in general.

• Blocking merges on WARNING and INFO findings from day one. This buries engineers in noise and the integration gets disabled within weeks. The fix is almost always political rather than technical: get buy-in on a severity policy like the one in Step 3 before the first rule ever runs in CI, not after the first round of complaints lands in a team chat channel.
• Running the full unscoped scan on every PR. This burns CI minutes and slows down every single merge, not just the ones that need review. Teams that skip the diff-aware configuration from Step 4 often end up with a five-minute CI check on a one-line documentation fix, which is exactly the kind of friction that gets a security tool quietly removed from the pipeline a quarter later.
• Leaving the rule registry on auto-update in production CI without pinning a version. A new rule can introduce new findings with zero code changes, breaking builds unpredictably and making it look like the CI pipeline itself is flaky when the real cause is an upstream rule change nobody on the team approved or even knew about.
• Suppressing findings with nosemgrep and no justification comment. Six months later nobody remembers why, and the suppression often outlives the reason for it. Treat a bare suppression comment the same way you would treat an unexplained // TODO left in production code for a year: technically harmless today, and a liability the day someone refactors the surrounding function.
• Treating Semgrep as a complete security program. It catches code-pattern bugs, not misconfigurations, business logic flaws, or runtime attacks — pair it with a runtime detection layer like Falco and a network-facing vulnerability scanner like OpenVAS, not instead of them. A clean Semgrep scan is evidence that your code has fewer known bad patterns, not evidence that your application is secure end to end.
• Writing custom rules without a test file. A rule with no negative test case will silently stop matching after a refactor and nobody will notice, which means the protection you thought you had quietly disappears and the team only finds out when the exact bug the rule was meant to catch ships anyway.
• Scanning generated code, vendored dependencies, or test fixtures by default. These inflate finding counts with irrelevant noise; exclude them in .semgrepignore from the start, because a dashboard showing 400 findings in vendored code trains engineers to stop reading the findings list at all, including the handful of real issues buried inside it.

Troubleshooting Guide

Here are the issues most likely to come up during setup, along with fixes.

• “semgrep: command not found” after pip install. The pip user scripts directory is not on your PATH. Add ~/.local/bin (Linux) or the equivalent pip binary path on macOS to your shell profile, then open a fresh terminal session so the updated PATH actually takes effect rather than testing in the same shell where the install happened.
• Scan hangs or runs extremely slowly on a large repo. Add --exclude patterns for build artifacts, node_modules, and vendored code; these are frequently scanned by mistake and contribute zero security value. A repository with a checked-in vendor/ directory or a stale dist/ build folder can easily double or triple scan time for no security benefit.
• Different results locally vs in CI. Check that both environments are pinned to the same Semgrep version; the registry rule set changes over time and an unpinned CLI will drift, meaning the exact same commit can pass locally and fail in CI purely because the two environments pulled the rule registry on different days.
• Docker-based scan can’t find files. Make sure you mount the working directory correctly: docker run --rm -v "${PWD}:/src" semgrep/semgrep semgrep scan --config auto /src. A common mistake is mounting the host path but then scanning the container’s default working directory instead of the mounted path.
• Custom rule never matches anything. Test the pattern in isolation against a minimal snippet first using semgrep --config rule.yaml --verbose to see whether the parser is even recognizing the target language correctly. A mismatched languages field in the rule YAML is the single most common reason a seemingly correct pattern matches nothing at all.
• Pre-commit hook blocks every commit, even trivial ones. You likely forgot to scope the hook to changed files only; pre-commit does this automatically, but a misconfigured args list can override that behavior and force a full-repository scan on every single commit, including a one-character typo fix.
• Supply Chain scan returns “not logged in” error. Supply Chain findings require a linked AppSec Platform account even on the free Community tier; run semgrep login first and confirm the session with semgrep whoami before re-running the scan.
• CI fails with no findings printed. Check your exit code logic; the --error flag by design returns non-zero on ERROR findings, which some CI runners interpret as a pipeline failure separate from the findings report itself, so the job log can show a failure with the actual findings buried several scroll-screens above the final status line.
• GitHub Actions workflow times out on a monorepo. Split the scan by directory using matrix jobs, or restrict the auto config to only the languages actually present in each sub-path, since scanning a Go service directory with the full polyglot rule set wastes time evaluating rules for languages that are not even present in that folder.

Advanced Tips for Scaling Semgrep Across Teams

Once the basic pipeline is stable, a few advanced moves separate a token integration from a security program engineers actually respect.

First, map every custom rule to a specific CWE and OWASP category in its metadata block, as shown in the Step 5 example. This lets you generate compliance reports for frameworks like SOC 2 or ISO 27001 directly from scan output instead of manually cross-referencing findings against a spreadsheet during audit season. Teams already maintaining detection logic for their SOC, such as Sigma rules for log-based threat detection, often reuse the same CWE-tagging discipline across both layers so a single taxonomy covers code-level and log-level findings alike.

Second, set up per-team rule ownership. A rule written by the platform team to catch misuse of an internal auth library belongs in a shared registry that every service repo pulls from, versioned the same way a shared library would be. Treat rule drift the same way you would treat an outdated dependency.

Third, track mean time to remediation (MTTR) for ERROR-severity findings as a team metric, not just raw finding counts. A team that finds 50 bugs and fixes them within a day is in better shape than a team that finds 5 bugs and lets them sit for a quarter. Finding count alone is a vanity metric; remediation speed is the number that actually correlates with reduced breach risk.

Fourth, when evaluating whether to also run a heavier tool like CodeQL alongside Semgrep, remember they solve overlapping but distinct problems: Semgrep’s pattern-matching approach trades some depth for speed and is cheap to run on every commit, while deeper semantic analysis tools catch more complex data-flow bugs at the cost of slower scans. Running both in different stages of your pipeline (Semgrep on every PR, a deeper tool on a nightly schedule) is a reasonable middle ground that several AppSec teams have converged on independently.

Semgrep vs CodeQL vs Snyk Code: Where Each Tool Fits

Semgrep is not the only static analysis option, and teams researching this space usually end up comparing it against GitHub’s CodeQL and Snyk Code at some point. All three read source code without executing it, but they make different tradeoffs between scan depth and scan speed, and none of the three vendors has published a head-to-head benchmark that this article can cite with confidence, so treat the comparison below as a guide to architecture and positioning rather than a scored shootout.

Tool	Analysis Approach	Best Fit	Open Source Core
Semgrep	Pattern matching on AST, optional cross-file taint analysis on paid tier	Fast PR-level checks, custom internal rules, polyglot monorepos	Yes (Community CLI)
CodeQL	Full semantic database query language, deep data-flow tracing	Deep audits, nightly scans, finding complex multi-step vulnerabilities	Yes (free for public repos and research)
Snyk Code	Proprietary ML-assisted static analysis with hosted backend	Teams already using Snyk for dependency and container scanning	No (commercial product)

In practice, the choice is rarely exclusive. Semgrep’s speed makes it the natural fit for a check that runs on every single pull request, where a two-minute scan is acceptable but a twenty-minute one is not. CodeQL’s deeper semantic model is better suited to catching multi-step vulnerabilities where a tainted value passes through several functions before reaching a dangerous sink, the kind of bug that a simpler pattern match can miss entirely. A reasonable architecture many AppSec teams land on independently is to run Semgrep on every PR for fast feedback and schedule a deeper CodeQL or Snyk Code pass nightly or weekly, treating the two tools as complementary layers rather than competing choices.

IDE Integration: Catching Bugs While You Type

Everything covered through Step 8 catches issues at commit time or pull request time. Semgrep also ships editor extensions for VS Code and JetBrains IDEs that run the same rule engine in the background as you write code, underlining a finding before you have even saved the file. This is the cheapest feedback loop available: a developer who sees a squiggly underline under a hardcoded API key fixes it in the same minute they typed it, rather than after a CI job flags it five minutes later or, worse, after a reviewer flags it in a pull request comment a day later.

Install the VS Code extension from the Marketplace by searching “Semgrep,” then sign in with the same account used for the Supply Chain and AppSec Platform features if you want custom org-wide rules to appear inline. The extension respects the same .semgrepignore file and .semgrep/ rule directory as the CLI, so there is no duplicate configuration to maintain. Teams rolling out Semgrep for the first time often see faster adoption when they lead with the IDE extension rather than the CI gate, since developers experience it as a helpful autocomplete-style nudge rather than a blocking gatekeeper standing between them and a merged pull request.

Mapping Findings to Compliance and Audit Requirements

Security engineering and compliance reporting are usually treated as separate jobs inside a company, but a well-tagged Semgrep rule set collapses a surprising amount of that distance. Every rule in the public registry, and every custom rule you write following the Step 5 pattern, can carry metadata fields for the relevant CWE identifier and OWASP Top Ten category. Once that metadata is in place consistently, a scan’s JSON output becomes a ready-made input for an audit trail, because an auditor asking “how do you detect injection vulnerabilities before release” has a direct, falsifiable answer: a specific rule ID, tied to a specific CWE, running on every pull request, with the historical scan logs to prove it.

This matters most for frameworks like SOC 2, ISO 27001, and PCI DSS, all of which ask for evidence of a secure development lifecycle rather than just a policy document describing one. Export scan results in JSON or SARIF format on a schedule, store them somewhere durable (a dedicated S3 bucket or equivalent with retention rules works fine for most teams), and point your compliance team at that archive instead of asking engineers to manually screenshot dashboards during audit season. The upfront tagging work in Step 5 is what makes this possible later; a rule with no CWE or OWASP metadata is a rule an auditor cannot easily trace back to a named control.

Why This Matters Right Now

The vulnerability disclosures piling up this October are not an anomaly, and they fit a pattern this site has been tracking across 2026: CISA added 31 vulnerabilities with confirmed active exploitation to its KEV catalog in August 2026 alone, and the pattern repeats almost every month: a pre-authentication flaw in enterprise software, often tracing back to a missing input check that a pattern-matching scanner would have flagged in a code review. Teams that bake static analysis into the commit workflow catch these bug classes before a researcher or an attacker finds them in production. Teams that rely entirely on penetration tests and bug bounties find them after the fact, usually with a CVE number and a public disclosure attached.

Semgrep will not single-handedly prevent your organization from appearing in next month’s vulnerability roundup. But a five-minute setup that blocks the next hardcoded secret, unsanitized file path, or unchecked outbound request from reaching main is a cheap trade against the alternative: an out-of-band advisory, a scramble to patch, and a disclosure with your company’s name in the headline. If your team is still building out the rest of the vulnerability management lifecycle around scanning results, aggregating findings from Semgrep alongside other scanners in a tool like DefectDojo is a logical next step once this pipeline is stable.

Frequently Asked Questions

Is Semgrep really free to use?
Yes. The Community tier is open source, requires no account for CLI-only scanning, and includes unlimited scans across unlimited repositories using the full public rule registry of 2,000+ rules. Supply Chain dependency scanning is also included in the free tier, though it requires a logged-in session.

How is Semgrep different from a dependency scanner like Dependabot?
Dependabot and similar tools check whether your third-party packages have known CVEs. Semgrep’s core engine checks your own source code for insecure patterns, independent of any dependency. Semgrep Supply Chain adds dependency-level checking on top, but the two tools solve different layers of the same problem and work well together.

Will Semgrep slow down my CI pipeline significantly?
A diff-aware scan on a typical pull request usually finishes in well under a minute. Full, unscoped scans on very large monorepos can take several minutes, which is why Step 4 recommends reserving those for a scheduled job rather than every PR.

Can Semgrep catch the same bugs as the Atlassian or SonicWall CVEs mentioned in this article?
Semgrep can flag the general code patterns behind those bug classes, such as unsanitized path construction or SSRF-prone outbound requests, when they appear in your own codebase. There is no confirmed public record of Semgrep rules specifically matched to those exact CVEs; the comparison here is about the underlying vulnerability class, not a claim of direct detection of those specific disclosures.

Do I need to write custom rules, or are the built-in ones enough?
The built-in registry covers general security patterns across common frameworks reasonably well. Custom rules become valuable once you have internal libraries, wrappers, or conventions that the public rules have no visibility into, which is true for most codebases beyond a certain size.

What happens if a rule produces too many false positives?
Exclude or tune that specific rule rather than abandoning the whole integration. Use --exclude-rule for a global disable, or scope suppressions file-by-file with .semgrepignore if the issue is limited to test fixtures or generated code.

Does Semgrep support languages beyond Python and JavaScript?
Yes. According to the project’s GitHub repository, the core engine supports more than 30 languages, including Go, Java, Ruby, C/C++, TypeScript, and Terraform, among others.

Should small teams bother with the paid tier?
Not usually, at least not initially. The Community tier covers everything in this tutorial, including CI integration, custom rules, and Supply Chain scanning. The paid tier’s cross-file taint analysis and centralized dashboard become more valuable once a team is managing findings across dozens of repositories rather than a handful.