Securie vs Semgrep
Semgrep is an open-source pattern-based SAST with a paid supply-chain add-on. Securie is an autonomous security engineer that reproduces each finding as a working exploit and writes the fix. Side-by-side feature, pricing, and best-fit comparison.
Semgrep and Securie are often compared because both run on pull requests, both produce security findings, and both ship auto-fix suggestions. The similarity is superficial. Semgrep is a pattern engine with a hackable rule format and a strong custom-rule culture; Securie is a specialist fleet with sandbox verification. The architectures are different, the workflows they optimize for are different, and the team shapes that benefit from each are different.
This page is for teams considering the architectural tradeoff. Semgrep's strength is rule-authoring ergonomics — if your security team enjoys writing custom rules, Semgrep is genuinely one of the best tools in the market for that activity. Semgrep's weakness on AI-generated code is the same weakness every pattern engine has: patterns match shapes, not exploits, and AI-generated bugs often look correct at the pattern level.
Securie's sandbox primitive changes the ticketing model. Only exploits that can be reproduced ship as findings; everything else is dropped before it reaches your queue. For teams whose pain is noise rather than coverage gaps, this shift is material. For teams whose strength is custom-rule authoring, the shift is orthogonal to their workflow and they may prefer Semgrep's model regardless.
The comparison here walks both perspectives honestly. There is no universally correct answer; there is a correct answer for your specific team shape and risk profile.
Semgrep is great at what it does — fast, hackable, rule-based SAST. It is also the clearest example of the limits of pattern-based tooling: narrow data-residency, no FedRAMP, basic role controls, and no auto-patch loop. Securie complements Semgrep by adding sandbox-verified exploits and framework-aware auto-fix PRs; many teams run both during early access.
Feature comparison
| Securie | Semgrep | |
|---|---|---|
| Finding verification | Sandboxed exploit reproduction | Pattern match |
| Auto-fix PR | Framework-aware patch, one-tap | Suggestion text (no sandbox verify) |
| Custom rule authoring | Managed specialists; framework-native rules | First-class: Semgrep's core strength |
| Supabase RLS specialist | Yes, first-class | Community rules, partial coverage |
| AI-feature security | Dedicated specialists | Community rules, early-stage |
| SCA (dependencies) | Launch: malicious npm + fast-CVE-block | Semgrep Supply Chain (paid add-on) |
| Data residency | SaaS sealed enclave or Customer-VPC | Narrow enterprise residency options |
| Audit artefact | Signed in-toto + SLSA attestation | Findings export |
Where the difference shows up in practice
A custom org-specific rule for detecting hardcoded customer IDs
Semgrep: Semgrep's custom-rule format is purpose-built for this. A rule like `customerId = 'cust_${string}'` catches hardcoded customer IDs across the codebase. The rule is auditable source, contributed to a shared internal registry, and runs on every PR. The ergonomics are excellent.
Securie: Securie's launch specialist fleet does not support user-authored custom rules. Custom rule authoring is Series-A roadmap. For this specific use case, Semgrep OSS (running locally or in CI) is the right tool, and Securie does not replace that slice of the workflow.
A subtle Supabase RLS bug: USING (auth.role() = 'authenticated') instead of USING (auth.uid() = owner_id)
Semgrep: Semgrep community rules for Supabase catch USING(true) and some obvious patterns but do not typically catch the auth.role() vs auth.uid() distinction. The rule would need to model Supabase's auth chain semantics — not just the SQL pattern — and most community rules do not go that deep.
Securie: Securie's Supabase specialist parses CREATE POLICY statements with Supabase semantics — distinguishes USING from WITH CHECK clauses, knows which auth functions actually filter rows by user, and flags the auth.role() pattern as 'authorized user can read ALL rows, not only their own'. The sandbox confirms by authenticating as User A and reading User B's rows. The finding ships with the correct fix: auth.uid() = owner_id.
Auto-fix for a command injection in a Server Action
Semgrep: Semgrep's autofix is a textual substitution. For a command-injection pattern `exec(\`rm -rf ${path}\`)`, a rule can propose `exec('rm -rf ' + shellEscape(path))`. For simple cases this works. For framework-specific cases (the Server Action should not be executing shell commands at all; the right fix is a different API entirely), the textual substitution cannot express that design-level correction.
Securie: Securie's patch generator models the Server Action's intent and proposes architecturally appropriate fixes — in the shell-exec case, replacing the exec call with a library-based operation (fs.rm with path validation) rather than trying to sanitize the shell input. The patch is tested in the sandbox: if the attack payload still succeeds against the patched code, the patch is discarded and a different shape is tried.
A prompt-injection vulnerability in an LLM tool-call
Semgrep: Semgrep's community rules for LLM security are early-stage and mostly cover obvious patterns (user input spliced directly into f-strings before prompt construction). The architectural bugs — tool-scope too broad, system prompt not delimited, RAG corpus loaded without quarantine — are pattern-complex and typically not covered.
Securie: Securie's prompt-injection specialist reads the LLM call site, models the trust boundary between system prompt and user input, analyzes the tool-registry scope against the agent's documented purpose, and flags architectural mismatches. The sandbox reproduces the attack (crafts a prompt that exploits the architectural gap) and verifies the tool-call is vulnerable.
The deeper tradeoff
Semgrep's pattern language is one of the best technical achievements in the SAST category. The idea that you can write a rule that looks like the code it matches — `user.password = $ANY` catches variable assignments of that shape — is both pedagogically clear and expressively powerful. r2c (now Semgrep Inc.) built a genuine improvement over traditional SAST DSLs, and the open-source ecosystem has responded with thousands of community rules covering a long tail of vulnerability patterns.
The architectural limit is the same one every pattern engine faces: patterns match code shapes, and the question 'is this code shape actually exploitable in this specific application?' requires running the code. Semgrep Pro Engine adds interfile dataflow — a significant step — but dataflow analysis is still static; it traces paths that exist in source without verifying whether those paths are reachable in execution. AI-generated code produces many patterns that are reachable-in-source but unreachable-in-execution because some upstream middleware, schema validation, or framework convention blocks the path.
Securie's sandbox is a different primitive. For each candidate finding, a Firecracker microVM boots a shadow clone of the application, the specialist generates an exploit payload appropriate to the bug class, and the payload is delivered to the running application. If the exploit succeeds — the SQL injection executes, the RLS policy permits the unauthorized read, the SSRF request reaches an attacker-controlled URL — the finding ships. If the exploit fails, the finding is silently dropped. The sandbox is not a layer on top of pattern matching; it replaces pattern matching as the decision primitive.
For AI-generated code, this architectural difference is material. The bugs hide not in code shapes but in semantic misuse of correct-looking code. A Server Action that uses Zod validation (correct shape) but with a permissive schema (subtle bug); a Supabase RLS policy that names auth.uid() (correct function) but in the USING clause where it does not constrain INSERT (subtle bug); a middleware matcher that looks specific (correct shape) but does not cover a specific child route (subtle bug). Semgrep's pattern engine catches none of these reliably; Securie's sandbox catches them by executing the exploit.
For classical web application bugs on traditional stacks — SQL injection in Rails, XSS in Django, path traversal in Express — Semgrep's pattern engine is often sufficient and the rule-authoring ergonomics are a real workflow advantage. The comparison depends on what share of your bug surface is classical-pattern versus AI-native-semantic. For pure AI-built-app teams, the semantic slice dominates and Securie's sandbox architecture is materially advantaged. For mixed or classical teams, Semgrep's rule-authoring is often the right primary tool with Securie as a complement.
Pricing
Free during early access. No credit card.
Open source core: free. Team: $35 per contributor per month. Enterprise: mid-five-figures+
Migration playbook
Step 1: Clarify Semgrep's role on your team before comparing
What: Is Semgrep being used for custom-rule authoring (security engineers writing rules weekly) or as a general-purpose SAST (default rules catching whatever they catch)? Document the actual usage pattern.
Why: Semgrep's value profile is very different in each mode. Custom-rule-authoring mode is where Semgrep's design shines and where Securie cannot yet replace it. General-purpose mode is where Securie's architecture is more competitive.
Gotchas: 'Custom rules' that are really suppression rules silencing false positives do not count as custom-rule authoring — they are maintenance of Semgrep itself, not extension of Semgrep's value.
Step 2: Run Securie in parallel for two weeks
What: Install Securie's GitHub App on your Semgrep-scanned repositories. Let both run on every PR. Log real-bug-catches and triage-hours per tool.
Why: The comparison is real-bug precision and engineer-time cost. Semgrep will typically produce higher finding count; Securie will typically produce higher real-bug ratio. The relevant metric depends on what your team spends time on.
Gotchas: Semgrep's weekly rule updates can introduce temporary noise — new rules are high-FP until tuned. Note which rules contribute to noise during the window.
Step 3: Evaluate the custom-rule workload
What: Over two weeks, count: how many new Semgrep custom rules did your team author or modify? How many existing custom rules caught bugs? What was the unique value of those rules that a framework-native specialist could not have caught?
Why: If the custom-rule workload is material and produces unique value, Semgrep stays regardless of what Securie does on the general-purpose side. If custom rules are minimal or duplicate what a specialist would catch, Semgrep's differentiator is not active for your team.
Gotchas: Many custom rules are hand-me-downs from prior employers or security engineers. Review each for 'is this still doing work for us?' before counting it as active.
Step 4: Consolidate or run both based on the data
What: If custom-rule authoring is active: keep Semgrep for that slice, add Securie for sandbox-verified general scanning. If custom-rule authoring is minimal and Securie caught most of what Semgrep caught (plus AI-native bugs Semgrep missed): consolidate on Securie, downgrade Semgrep to OSS CLI for ad-hoc local use.
Why: The decision is about match to your team's workflow, not feature lists. Data collected on your own repository during the evaluation window is the only trustworthy input.
Gotchas: Semgrep Cloud subscriptions have cancellation windows. If you downgrade, ensure the OSS CLI is configured to run locally for the use cases Semgrep Cloud currently covers, so you do not lose coverage at the switch.
When to pick Semgrep
You want to author and share custom SAST rules across a very polyglot codebase (Ruby + Scala + Terraform + Python), you have security engineers who enjoy writing rules, and you can triage pattern matches at scale.
When to pick Securie
You ship Next.js + Supabase + Vercel and you want every finding to carry a working exploit and a ready-to-merge fix.
Bottom line
Pick Semgrep if you want to write and share custom rules across a polyglot codebase and you are comfortable triaging pattern-matched findings. Pick Securie if you want provable bugs and one-tap fixes on your Next.js + Supabase + Vercel stack.
FAQ
Can I use both Semgrep and Securie?
Yes, and it is a common pattern. Semgrep for custom org-specific rules; Securie for stack-specific sandbox-verified bugs.
Does Securie support custom rules?
Custom rules are a Series-A roadmap item. At launch the specialists are managed and framework-native.
What about Semgrep Pro Engine and interfile analysis?
Pro Engine adds interfile dataflow across modules, which is a real improvement over single-file pattern matching. But interfile analysis is still static; it does not execute code, and dataflow paths that are reachable statically but unreachable dynamically still fire as findings. Sandbox verification addresses the reachability question by executing.
Does Semgrep Pro handle Supabase RLS?
Semgrep Pro's interfile analysis does not specifically model Supabase RLS semantics. The community ruleset for Supabase has basic coverage (detecting USING(true) policies) but does not parse CREATE POLICY with full Supabase auth.uid() / auth.role() semantics. Securie's Supabase specialist does, and the difference matters on AI-generated RLS policies where the bugs are subtle rather than glaring.
If we already pay for Semgrep Cloud, should we still consider Securie?
Yes, and run them in parallel rather than replacing. Semgrep's custom-rule strength and Securie's sandbox + AI-native specialist strength are complementary for many teams. After a quarter of dual operation, most teams find they can trim Semgrep Cloud subscription (keeping OSS CLI for local use) while consolidating the hosted scanning on Securie — but the decision is data-driven, not vendor-pitched.