Semgrep alternative — sandbox proof + auto-fix, not just pattern matches

Pattern-matching has an unavoidable noise floor

Example: Semgrep's rule engine matches against code shape. A rule like `$FUNC(..., user_input, ...)` where `$FUNC` is known-unsafe (like `exec`) fires whenever the syntactic pattern appears, regardless of whether `user_input` is actually attacker-controlled in execution. On Next.js Server Actions where `formData.get('message')` is passed into any function, the pattern hits constantly — including on routes where the formData is schema-validated by Zod two lines earlier.

Impact: The signal-to-noise ratio is not a tunable knob; it is a property of the approach. Teams hire or allocate engineers specifically to write suppression rules on top of the base rules — a second layer of rules that exists only to silence the first layer's noise. The meta-rule maintenance becomes its own workload.

Auto-fix beyond simple cases is a suggestion, not a fix

Example: Semgrep's autofix syntax (`fix: $REPLACEMENT`) applies a textual substitution on match. For simple cases (add a `.escape()` call, rename a dangerous function), the substitution is safe. For framework-specific cases (the Supabase RLS policy needs `auth.uid()` in a WITH CHECK clause, not USING), the substitution cannot encode the semantic constraint — the rule-writer would have to model the entire Supabase auth chain in the rule to get it right, and most rule-writers do not.

Impact: The autofix either applies textually and introduces a different bug, or does not apply at all because the rule-writer did not encode the framework semantics. Most non-trivial fixes in Semgrep are advisory — the rule says 'here is the shape, now a human should fix it appropriately'. That shifts the auto-fix burden back to the developer.

Supply-chain (SCA) is a paid add-on and narrower than Snyk

Example: Semgrep Supply Chain (the SCA product) requires a paid subscription separate from the base Semgrep product. It scans `package.json`, `requirements.txt`, and similar manifests against a curated vulnerability database. Coverage is narrower than Snyk or Dependabot — some language ecosystems (Cargo, Go modules) have had partial coverage or later-to-ship database updates.

Impact: Teams who adopted Semgrep for SAST and then discovered they also needed SCA face a second purchase decision — either add Supply Chain (mid-five-figures at enterprise scale) or pair Semgrep with a dedicated SCA tool (Dependabot, Snyk SCA, Socket). The effective stack becomes two tools where buyers imagined one.

Data residency options are narrow

Example: Semgrep Cloud runs primarily in US regions; EU residency is available on enterprise contracts but not self-service. FedRAMP authorization is not in-place as of early 2026. For regulated customers (healthcare, government, EU-data-residency mandatory), this forces an awkward choice: use the Semgrep CLI locally with CI/CD integration (which loses the Semgrep AppSec Platform's triage UI) or wait on the enterprise residency path.

Impact: Smaller regulated teams cannot adopt Semgrep Cloud's premium features without significant enterprise negotiation. Larger teams with FedRAMP Moderate requirements cannot adopt Semgrep Cloud at all today. Either path means losing value relative to the marketing promise.

Community rules for Supabase RLS and AI features are partial

Example: Semgrep's strength is community-contributed rules, and the registry covers a long tail of patterns. The specific coverage for Supabase Row-Level-Security and for LLM prompt-injection / tool-scope-abuse is early-stage as of Q1 2026 — a handful of rules exist, maintained by community contributors, without the depth of a dedicated specialist. The rules match the pattern 'CREATE POLICY ... USING (true)' but not the nuanced 'USING (auth.role() = \'authenticated\')' pattern that looks correct and is still broken.

Impact: Teams on Supabase who believe Semgrep covers their RLS exposure are partially covered — the glaring USING (true) bugs are flagged, but the subtle auth.role() vs auth.uid() bugs that are most common in AI-generated code slip through. False sense of coverage is the worst kind.

Sandbox beats pattern

Semgrep matches code shapes; Securie reproduces exploits. If the pattern-matched finding isn't actually exploitable, you don't see it.

Framework-aware patch

Not just a suggestion — a tested, merge-ready PR.

Supabase + AI-native coverage

Semgrep's community rules for these are partial; Securie has dedicated specialists.

Step 1: Run Securie alongside Semgrep on every PR for two weeks

What: Add the Securie GitHub App with the same repo access Semgrep has. Both tools emit findings; do not dismiss or deduplicate during the discovery window.

Why: The goal is to see the actual overlap. Expect Semgrep to flag more total findings and Securie to flag fewer but with higher real-bug rate. Measuring both sides honestly prevents the 'fewer findings means worse' fallacy.

Gotchas: Semgrep's rule registry updates weekly. If a new rule lands during your two-week window, its first days are high-noise before the community tunes it. Note this in your comparison.

Step 2: Tally real-bug precision per tool

What: For each finding in each tool, record: did the engineering team make a code change based on this finding? (yes / no / dismissed as noise). Weekly total: `real-bug-count / total-findings-count` per tool.

Why: Semgrep's real-bug precision on AI-generated code typically sits at 15-30% in public case studies. Securie's is close to 100% because the sandbox is the filter. The ratio, not the absolute count, is the comparison.

Gotchas: Edge case: a finding that prompts a 'defensive' change even when the bug was not exploitable. Count these as real bugs only if the change actually reduces attack surface — not if it just suppresses the scanner.

Step 3: Decide based on your AppSec investment

What: If your team writes and maintains 3+ custom Semgrep rules per month and those rules catch org-specific patterns, keep Semgrep alongside Securie — they complement. If your team is primarily triaging base-rule findings with no custom-rule authoring, Semgrep's role is replaceable by Securie's specialists for the AI-built-app slice.

Why: Semgrep's unique value is custom-rule authoring. If you are not using it, you are paying for a feature that does not return value, and Securie covers the scanning-only use case with better precision.

Gotchas: Some 'custom rules' are just suppression rules — rules that silence base-rule noise. Those are not custom value; they are maintenance of Semgrep itself. Do not count them when deciding.

Step 4: Consolidate or keep both based on the outcome

What: If you keep both: use Semgrep for custom org-specific rules and Securie for the framework-native + sandbox layer. Configure Semgrep to scan only the custom ruleset to reduce noise; use Securie for the default coverage. If you consolidate on Securie: export your Semgrep findings history to SARIF for audit continuity, then cancel the Semgrep subscription at your next renewal.

Why: The two tools address different problems at their best. Teams that successfully run both treat them as specialist (custom rules) + generalist (scanning). Teams that consolidate are usually ones where Semgrep was being used as a generalist and Securie does that better for AI-built apps.

Gotchas: Semgrep Supply Chain and Semgrep Secrets are separate add-ons. If you use them, re-evaluate each independently — canceling the core Semgrep subscription usually requires canceling the add-ons too.