Email AI Extractor — agentic threat model

Not certain from the listing — likely utilizes a commercial or open-source LLM to parse unstructured text. The primary threat is indirect prompt injection, where malicious instructions embedded in a target website hijack the model's behavior during extraction.

Not certain from the listing — processes transient web data to generate CSV files. Threats include CSV injection (Formula Injection) if the agent extracts malicious payloads (e.g., '=cmd|' /C...') from websites and writes them unescaped into the output file.

Not certain from the listing — likely uses a basic scraping and file-writing orchestration framework. Threats include insecure tool integration if the scraping tool lacks limits on file sizes, redirect depth, or rate limits.

Not certain from the listing — could be deployed as a local CLI tool, browser extension, or hosted service. If hosted, it is highly vulnerable to Server-Side Request Forgery (SSRF) if users can force it to scrape internal network addresses.

Not certain from the listing — no observability or guardrail mechanisms are mentioned. This creates a blind spot where malicious inputs or anomalous scraping behaviors go undetected.

Not certain from the listing — being open-source/freemium, it lacks explicit compliance controls. Mass scraping of email addresses presents significant regulatory risks under GDPR, CCPA, and CAN-SPAM regulations.

Not certain from the listing — operates as a standalone utility. Ecosystem risks are minimal unless integrated into automated downstream marketing or email-sending pipelines.