⚙Data measured · 2026-08-21·Source · DataForSEO · Google US, Google Trends, Reddit·14h MVPWorth Watching

Agentic Workflow Injection Auditor

Find and repair paths where untrusted content influences privileged AI agents in GitHub Actions, without uploading private code to an LLM.

At a glance

→🟡 Worth watching — validate before committing
→Measured entry keyword "GitHub Actions security scanner" — 20/mo · KD ? (⚙ not a guess)
→1. Versioned, sourced AWI-specific registry of sources, sinks, and agent actions.
→14h to an MVP · 2 competitors broken down

Market Evidence

20/momonthly searches⚙ Measured · 2026-08-21

→ Stable

2 direct competitors

Targets are AppSec, platform engineering, DevSecOps, open-source maintainers, and AI tooling vendors running agent-based issue triage, PR review, autofix, release, or maintenance.

Competitive Landscape

Named competitorsawilintagentic-workflow-guard

[Promptfoo code scan](https://www.promptfoo.dev/docs/code-scanning/github-action/) already scans prompt injection, PII, and excessive agency through a GitHub App/Action with PR findings and SARIF. awilint and agentic-workflow-guard provide local static AWI checks
TaintAWI and an online research analyzer exist. GitHub/VS Code also provide approvals, hidden-character filtering, permission controls, and logs. The wedge is not generic LLM code scanning. It is local multi-file workflow analysis that visualizes untrusted source → prompt boundary → agent capability → privileged sink, calculates cross-step/job blast radius, generates a minimal patch and regression fixture, and produces an audit-ready report. SEO cannot be the only acquisition channel.

Differentiation Opportunity

1. Versioned, sourced AWI-specific registry of sources, sinks, and agent actions.

03Traffic Verification ReportPRO

⚙ Measured · DataForSEO · 2026-08-21

★ Measured entry keyword

GitHub Actions security scanner

Volume/mo

—

+5 keywords verified

🔒 The playbook is behind the wall

Free readers get the opportunity and the evidence. Members get the measured keyword data, the SERP breakdown, how far this can rank and how fast, and the full build plan.

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This report unlocks for everyone on 2026-11-16

5-Axis Scoring

Market7/10

Gap7/10

Tech5/10

SEO7/10

Revenue6/10

Why Build This

Targets are AppSec, platform engineering, DevSecOps, open-source maintainers, and AI tooling vendors running agent-based issue triage, PR review, autofix, release, or maintenance. Traditional Actions linters catch shell injection and permission errors but do not model semantic prompt boundaries or model output flowing into scripts. One secret leak or unauthorized write costs far more than a $199 audit. 1. Versioned, sourced AWI-specific registry of sources, sinks, and agent actions. 2. Explainable cross-step/job flow graph rather than regex-only findings.

What to Build

Target User

Users: AppSec, platform engineering, DevSecOps, AI coding vendors, critical open-source maintainers.

Core Function

open one YAML or repository ZIP

parse GitHub expressions

identify issue/PR/comment/fork/commit sources

identify Claude/Codex/Gemini/Copilot/generic LLM actions and prompt boundaries

trace outputs to run, write, API, secret, artifact, and release sinks

Differentiation

1. Versioned, sourced AWI-specific registry of sources, sinks, and agent actions.

How to Monetize

Primary

$199 guided workflow audit.

Secondary

$39 signed self-serve report/fix pack.

🔒 The lines above are the model’s basic take — the full playbook is for members

The monetization playbook maps 4 paths — who pays, at what moment, how much — each checked against free alternatives, differentiation, and path friction, with measured CPCs as evidence of willingness to pay.

See how to unlock ↑

How to Build (8h MVP)

Next.js + Tailwind CSS

8h MVP Checklist

1.Define threat model and source/prompt/action/output/sink/gate schemas with 12–20 fixtures.
2.Build YAML parsing, expression normalization, positions, and multi-file index.
3.Build P2A/P2S cross-step dataflow plus permission/blast-radius analysis.
4.Build explainable findings, flow graph, severity, and evidence links.
5.Build safe patch recipes, diff, rescan, SARIF, and Markdown.
6.Add Rules, Incident, Pricing, About, FAQ, Privacy, and `$199` CTA.
7.Replay ten real open-source repositories and compare against awilint/Promptfoo before launch.

Don't Build

✕Do not become generic SAST, MCP scanning, or AI code review.
✕Never upload private repositories or send code to an LLM.
✕Do not build GitHub App/OAuth, monitoring, auth, membership, subscriptions, or admin first.
✕Do not issue unsourced critical findings or promise that a scan proves safety.
✕Never execute user workflows or arbitrary shell commands.
✕Do not trade launch speed for completeness, and **do not remove cross-step flow, blast radius, patches, rescan, or real fixtures to make it lightweight.**

SEO Keywords

GitHub Actions security scannerAI CI/CD security scanneragentic workflow injectionGitHub Actions prompt injectionsecure AI code review workflow

Risks

△Promptfoo/open-source price pressure, GitHub first-party mitigations, dynamic-action false positives/negatives, security liability, rule maintenance, private-code trust, and a small emerging keyword. Stop if 30 targets produce fewer than 4 qualified calls or zero payment; if 2 of 10 real repos contain critical flows the tool cannot explain; or rule maintenance exceeds four hours per week.

Full Analysis

Free preview · roughly the first quarter

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