INJECT.md

INJECT.md provides a comprehensive prompt-injection audit service for large language model (LLM) applications, identifying vulnerabilities that arise from untrusted input data such as web pages, emails, PDFs, and tool responses. It offers a battery of 46 payloads across five attack classes to test and score the resistance of LLM endpoints, helping developers structurally harden their systems against prompt injection attacks that can lead to unauthorized data leaks or actions.

Who is it for?

Developers and security teams building and maintaining LLM applications

Problem

Prompt injection is a critical security risk for LLM applications where malicious instructions hidden in untrusted input can cause unauthorized data leaks or actions. Existing prompt-based protections are ineffective because injection attacks exploit the data the model reads, not just the prompt itself.

Solution

INJECT.md runs a comprehensive battery of injection payloads against LLM endpoints to identify vulnerabilities, scores resistance per attack class, and provides detailed reports with remediation steps and a hardening playbook. It runs locally to keep keys secure and integrates into CI pipelines for continuous protection.

What makes it unique

• Comprehensive 46-payload battery covering multiple injection attack classes
• Structural approach focusing on architecture rather than prompt wording
• Runs locally ensuring keys never leave the user's environment
• Provides scored, detailed reports with remediation guidance and continuous updates

• Injection Payload Battery

  Runs 46 payloads across 5 attack classes to test LLM endpoint vulnerabilities.

  01
• Scored Resistance Report

  Provides a detailed score and breakdown per attack class with pass/fail results.

  02
• Remediation Guidance

  Includes a hardening playbook with structural fixes mapped to failed attack classes.

  03
• Local Execution

  Runs scans locally to ensure API keys never leave the user's environment.

  04
• Continuous Integration Integration

  Supports CI pipelines with non-zero exit on failure to gate deployments.

  05
• Scheduled Re-testing

  Automates periodic re-runs to catch regressions and new attack classes.

  06
• White-label Agency Reports

  Offers unlimited endpoints and clients with branded reports and API access.

  07

Business model

SAAS

Audience

B2B

Platforms

web · api

Pricing model

subscription

Value proposition

INJECT.md measures and reports prompt injection vulnerabilities in LLM applications, providing actionable fixes to structurally harden systems against attacks.

Analytics

VVercel Analytics

Metadata

OOpen Graph

Other

VercelReact

Web framework

Next.js