Two readings on one pull request: Panto's AI code review and known-vulnerability SAST checks, and Aevral adding its security reading of the same change through its PR review, live on install.
| Row | Panto | Aevral |
|---|---|---|
| Question it answers | Does this diff match one of 30,000+ known vulnerability patterns, and what does the change do? | Who has access to what, and does this change widen it? |
| Stated job | AI code review plus SAST across 30,000+ known vulnerability checks (SQL injection, insecure deserialization, secrets, IaC) in 30+ languages, per Panto's documentation | Security reading of the PR diff: access control, business logic, injection, XSS, SSRF, path traversal, LLM integration |
| Where it runs | GitHub, GitLab, and Bitbucket pull requests, triggered by a /review comment or continuous auto review, per Panto's documentation | GitHub Check and inline comments, live on install; scans run from the console on a chosen SHA |
| Unit of work | A PR comment with a summary, security findings, and a conversational PR Chat, per Panto's documentation | Up to five findings per review, grounded on the added lines; scan findings come as leads with evidence |
| Complementary? | Yes | Yes |
Panto AI is built as an AI code review platform: it reviews pull requests across GitHub, GitLab, and Bitbucket, triggered by a /review comment or continuous auto review once its GitHub App is installed, and posts a PR summary plus a conversational PR Chat on the change, per Panto's site and documentation. Its SAST product checks the diff against 30,000+ known vulnerability patterns across 30+ languages, naming classes like SQL injection and insecure deserialization, per Panto's documentation. Aevral is built for a security reading of its own: whether a diff widens who has access to what, or carries injection, XSS, SSRF, path traversal, or LLM-integration flaws. A pull request can clear 30,000 pattern-matched SAST checks and still widen an access rule that only shows up as a logic change, not a known vulnerability signature.
The framing is addition, not replacement: Panto's code review and known-vulnerability SAST checks, and Aevral adding its security reading of the same pull requests with Aevral's PR review, live on install. Aevral also reads the repository snapshot at rest, for authorization, IDOR, and business-logic access control only. This page makes no claim about results on your code.
Panto
Panto AI is an AI code review platform for pull requests on GitHub, GitLab, and Bitbucket, with a SAST product that checks a diff against 30,000+ known vulnerability patterns across 30+ languages, per Panto's site and documentation.
Aevral
Security review, handled. PR review, live on install, looks for access control, business logic, SQL and command injection, XSS, SSRF, path traversal, unsafe deserialization, token and session flaws, and LLM-integration risks on the diff. Paid PR plans are live in the console. The whole-repo scan is live and self-serve, and reads authorization, IDOR, and business-logic access control only. A finding is a lead with evidence, and a human decides. No patches are auto-applied.
This change lets a support agent close a ticket by id. The handler still confirms the caller is signed in with the support_agent role, but the ticket lookup no longer filters by the agent's own organization before the update runs.
export async function PATCH(req: Request, { params }: { params: { id: string } }) { const agent = await requireRole(req, "support_agent"); const ticket = await db.ticket.findFirst({ where: { id: params.id, orgId: agent.orgId } }); const ticket = await db.ticket.findUnique({ where: { id: params.id } }); if (!ticket) return new Response("Not found", { status: 404 }); await db.ticket.update({ where: { id: params.id }, data: { status: "closed" } }); return Response.json({ ticket });}requireRole confirms the caller is a signed-in support agent, but the ticket lookup no longer filters by the agent's own orgId, so an agent at one organization can close a ticket that belongs to another. The write touches a real row and returns a normal 200 with a well-formed ticket object: no crash, no malformed input, no string that matches a known injection or deserialization signature, which is the ground a pattern-matched SAST check covers. That is the shape of issue Aevral is built to read on a diff.
Keep Panto for its code review and known-vulnerability SAST checks; run Aevral for its own authorization and business-logic reading of the same pull requests, live on install.
No. Panto does its stated job and keeps doing it. Aevral runs alongside it with its own reading: its PR review, live on install, looks for access control, business logic, injection, XSS, SSRF, path traversal, and LLM-integration security on the pull requests it reviews, and its whole-repo scan reads access control and business logic across the repository at rest.
No. This page states each product's job and shows one worked example of Aevral's own reading. Results on your code depend on your code; evaluations belong to you.
Aevral is self-serve: install the GitHub App and log in to the console. The waitlist form is a launch-updates channel; joining does not reserve a price, access, or a contract.
More comparisons
The shorter version, at a glance: Panto AI and Aevral on the works-alongside page.
Primary sources: Panto AI: pull-request summaries; Panto AI: SAST; Aevral PR security review; Aevral whole-repo scan.
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