Sancheck

Company: Sancheck

Self-reported basis: The description is entirely self-reported and unverified, based on an author-submitted write-up for the OpenAI 2026 hackathon. No external corroboration or historical data exists.

What it appears to be: A security tool that scans URLs for prompt injection risks in AI-assisted developer workflows, with a CLI and web UI. It is built using Codex and GPT-5.6, and includes middleware logic for URL validation and agent safety.

What changed: The project was submitted as part of a hackathon, suggesting it is early-stage or experimental. No evidence of prior development, funding, or commercial traction exists.

Single most important open question: Is there any evidence that this tool has been used in real AI-assisted workflows or integrated into developer environments?

The description states that Sancheck is a secure middleware designed to identify prompt-injected URLs and prevent them from reaching the Codex agent. It performs live DNS checks, blocks private/reserved network targets, validates HTTPS/TLS, follows redirects safely, samples page content, detects prompt-injection patterns, and looks up domain age through RDAP.

It includes:

• A CLI gate that scans direct URLs or extracts links from stdin (e.g., prompts, Markdown files).
• A local web UI, built with Vite, React, and HeroUI primitives.
• A Python-based scanner core using standard library tools for local execution.
• A local Node server to bridge the UI to the Python scanner.

The tool is described as a pre-flight gate in AI-assisted developer workflows, where links are treated as executable instructions rather than simple strings.

Inference: The product appears to be a URL validation and safety tool for AI agents, not a general-purpose security scanner or phishing detection system.

Not evidenced: No evidence of actual deployment, usage, or integration into existing systems.

The author states that Sancheck was inspired by the gap in security tools that treat links as simple strings, while agent workflows treat them as instructions and executable next steps.

It positions itself as:

• A pre-flight gate for AI-assisted developer workflows.
• A tool that blocks private network targets by default, which is critical for agent safety.
• A real-time scanner that avoids mocked verdicts, using real reputation providers when API keys are configured.

Claim: The tool addresses a specific risk in AI agent workflows — prompt injection via URLs.

Not evidenced: No evidence of market positioning, customer feedback, or competitive differentiation beyond the hackathon submission.

The description implies that Sancheck targets:

• AI-assisted developers who use tools like Codex or similar agents.
• Organizations concerned with agent safety and prompt injection risks in workflows.

It is not clear if it targets:

• End users, developers, or enterprise customers.
• Specific industries (e.g., fintech, healthcare) or use cases beyond developer workflows.

Inference: The ICP likely includes developers or teams using AI agents in code-assisted environments.

Not evidenced: No evidence of target customer segmentation, personas, or adoption data.

The description does not mention:

• Any pricing model.
• Revenue streams.
• Monetization strategy.
• Subscription tiers or licensing.

It only describes the tool’s functionality and architecture.

Not evidenced: No business model or pricing information is provided.

Inference: If commercialized, it might be sold as a middleware or CLI tool, possibly with optional API integrations for reputation providers.

The project was built using:

• Codex and GPT-5.6, which accelerated development of URL normalization logic, DNS resolution, TLS validation, HTTP client implementation, and prompt injection detection.
• A Python core with standard library for local execution.
• A local Node server to bridge the UI to the Python scanner.
• A web UI built with Vite, React, and HeroUI primitives.

The tool:

• Normalizes URLs.
• Resolves DNS.
• Applies SSRF protections.
• Validates TLS.
• Fetches bounded response samples.
• Analyzes content for hostile agent instructions.
• Outputs a weighted findings report in JSON format.

Inference: The architecture is lightweight and designed for local execution, with a clear middleware contract (stdin in, JSON out).

Not evidenced: No evidence of scalability, cloud deployment, or performance benchmarks.

The project was submitted to the OpenAI 2026 hackathon, indicating it is:

• Early-stage.
• Experimental.
• Possibly a prototype or proof-of-concept.

There is no evidence of:

• Revenue.
• Customers.
• Product adoption.
• Prior funding or team history.
• Public usage or integration.

Not evidenced: No traction or maturity data beyond the hackathon submission.

Inference: The tool is likely in an early development or prototyping phase.

The description does not mention:

• Competitors.
• Existing tools in the URL safety or AI agent security space.
• Market size or competitive positioning.

It only states that current security tools treat links as simple strings, while agent workflows treat them as executable instructions.

Not evidenced: No competitive landscape or market analysis provided.

Inference: The tool addresses a niche but growing concern in AI-assisted development environments.

• No evidence of real-world usage: Submitted to a hackathon, with no known adoption or integration.
• Unverified claims: All descriptions are self-reported and unverified.
• Limited scope: The tool is described as a CLI and web UI, but lacks enterprise features or integrations.
• No monetization strategy: No indication of how the product would be sold or funded.
• Dependency on API keys: Reputation providers require credentials, which may limit usability without them.

Not evidenced: No evidence of risk mitigation, scalability, or long-term viability.

Inference: The tool is experimental and lacks commercial traction or clear path to market.

1. What specific AI-assisted workflows or environments are you targeting with this tool?
2. Have you tested Sancheck in real-world developer environments or agent workflows?
3. Are there any known limitations or blind spots in the current scanning logic?
4. How do you plan to monetize or scale the product beyond a hackathon prototype?
5. What is your roadmap for integrating with CI/CD pipelines, browser extensions, or enterprise platforms?
6. Have you considered how to handle false positives or user feedback on scan results?