Evidence-backed application vulnerability validation workbench

For

Security engineers and development teams responsible for application and code security

Solves

Vulnerability scanners produce unverified findings, so teams cannot tell which issues are real, which matter most, or what to fix first.

Delivers

Reviewer-approved validated vulnerability findings with proof-of-concept evidence

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$12,500 for the MVP, $42,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce unverified findings and manual triage while keeping security decisions under human review.

1. Scan authorized code and applications for security flaws.
2. Generate proof-of-concept evidence to confirm real vulnerabilities.
3. Prioritize findings by risk and exploitability.
4. Provide remediation guidance for each confirmed issue.
5. Integrate into CI/CD pipelines to catch issues early.
6. Produce detailed findings and remediation reports.
7. Support multiple programming languages and frameworks.
8. Check each coding-agent tool call before execution using request and session context.
9. Inspect script contents, dependencies and session history to predict action effects.
10. Keep repository contents and tool outputs on the local machine.
11. Support common coding agents out of the box.
12. Run background monitoring continuously without manual toggling.
13. Allow inspection, customization and contribution via an open-source codebase.
14. Enable quick setup with minimal configuration.
15. Suggest pull requests with fixes for confirmed findings.
16. Compare the reviewed result with the recorded baseline and value assumptions.
17. Capture corrections and named-owner approval before consequential use.
18. Export a versioned reviewer-approved validated vulnerability findings with proof-of-concept evidence with source references and unresolved questions.

Everything these tools do, in one app

• Automated vulnerability detection Scans code and applications to find security flaws without manual review.Found in Hacktron, Gecko Security, Strix
• Proof-of-concept validation Validates findings by generating proof-of-concept evidence to confirm real vulnerabilities.Found in Hacktron, Strix
• Risk prioritization Helps teams focus on the most critical security issues first.Found in Hacktron, Gecko Security
• Remediation guidance Provides actionable steps or insights to fix identified vulnerabilities.Found in Hacktron, Gecko Security
• CI/CD integration Fits into continuous integration and delivery pipelines to catch issues early.Found in Hacktron, Gecko Security, Strix
• Detailed reporting Generates comprehensive reports on findings and remediation.Found in Gecko Security, Strix
• Multi-language support Supports a wide range of programming languages and frameworks.Found in Gecko Security
• Pre-execution tool call checks Evaluates each action a coding agent tries to take before it runs, using request and session context.Found in Harden
• Script content inspection Analyzes script contents, dependencies, and session history to determine what an action would do.Found in Harden
• Local-only operation Keeps repo contents and tool outputs on your machine, not sent to external servers.Found in Harden
• Coding agent support Works out of the box with multiple coding agents like Cursor, Claude Code, and others.Found in Harden
• Background monitoring Runs continuously without needing to be toggled on or off.Found in Harden
• Open-source codebase Allows inspection, customization, and contribution via a public repository.Found in Strix
• Quick setup Enables fast deployment and integration with minimal configuration.Found in Hacktron, Harden, Strix
• Suggested pull requests Allows developers to submit fixes or suggested pull requests directly.Found in Hacktron

What goes in, what comes out

The workflow

1. In
   Start with

   Authorized source code, application builds, dependency manifests, runtime configuration and scan history

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect authorized source code

4. 3

   Application builds

5. 4

   Dependency manifests

6. 5

   Runtime configuration and scan history

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewer-approved validated vulnerability findings with proof-of-concept evidence

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One authorized repository and one application build; final severity and remediation decisions remain with qualified security reviewers. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Scan intake and scope, Editable validation workspace, Client proof and delivery. Use a thumbnail gallery for scans, a large central canvas for findings and evidence, and a right-hand panel for severity, proof, remediation and comments. Let users compare raw findings against validated findings side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant finding. Make the task-specific outcome reviewer-approved validated vulnerability findings with proof-of-concept evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Authorized repositories, CI/CD pipelines, issue trackers and coding agents. Cloud asset storage, design-file import/export and publishing destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: scan authorized code and applications for security flaws; generate proof-of-concept evidence to confirm real vulnerabilities. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will security engineers and development teams responsible for application and code security use it to solve "vulnerability scanners produce unverified findings, so teams cannot tell which issues are real, which matter most, or what to fix first"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Confirmed vulnerabilities per review hour and false-positive rate after validation.
4. Measure, then decide. Track confirmed vulnerabilities per review hour and false-positive rate after validation; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One authorized repository and one application build; final severity and remediation decisions remain with qualified security reviewers. Implement one approved input format, a bounded representative case set and the first two task modules: scan authorized code and applications for security flaws; generate proof-of-concept evidence to confirm real vulnerabilities. Support the third module with operator review: prioritize findings by risk and exploitability. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewer-approved validated vulnerability findings with proof-of-concept evidence. Retain the explicit scope boundary: One authorized repository and one application build; final severity and remediation decisions remain with qualified security reviewers.

What the build depends on. Asset upload and preview, asynchronous scan jobs, editable version history, reviewer access and tested export formats. High-fidelity security validation requires specialist review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One authorized repository and one application build; final severity and remediation decisions remain with qualified security reviewers.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined repository and application package. Offer a monthly validation allowance after repeat demand. Quote complex multi-repository or specialist compliance work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved validated vulnerability findings with proof-of-concept evidence. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce unverified findings and manual triage while keeping security decisions under human review. Demonstrate a concrete reviewer-approved validated vulnerability findings with proof-of-concept evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Security engineers and development teams responsible for application and code security professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved validated vulnerability findings with proof-of-concept evidence from a small authorized input set, with a transparent calculation of confirmed vulnerabilities per review hour and false-positive rate after validation and no promised savings.

The first 30 days

1. Week 1: interview five security engineers and development teams responsible for application and code security and inspect a recent example of vulnerability scanners produce unverified findings, so teams cannot tell which issues are real, which matter most, or what to fix first.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure confirmed vulnerabilities per review hour and false-positive rate after validation, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Confirmed vulnerabilities per review hour and false-positive rate after validation. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Confirmed vulnerabilities per review hour and false-positive rate after validation; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved validated vulnerability findings with proof-of-concept evidence. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved validation patterns, exploit evidence and review examples, together with reliable delivery for a narrow security niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for security engineers and development teams responsible for application and code security. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Hacktron, Gecko Security, Harden, Strix, manual code review and generic static analysis tools. Compare this product with the buyer's present method on confirmed vulnerabilities per review hour and false-positive rate after validation. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Scan compute, proof-of-concept execution, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved validated vulnerability findings with proof-of-concept evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, evidence integrity and usage permissions. Security reviewers approve substantive findings and disclosure scope. One authorized repository and one application build; final severity and remediation decisions remain with qualified security reviewers. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.