scrape.new — agentic threat model

Not certain from the listing — likely uses a commercial LLM to generate CSS selectors and parse text. The primary threat is indirect prompt injection, where malicious instructions embedded in a target website's HTML hijack the LLM's extraction logic or force it to output malicious payloads.

Not certain from the listing — processes ephemeral web data on demand. Threats include data poisoning of the target website to feed corrupted or misleading data to the user, and potential data leakage if scraped content is cached insecurely.

Not certain from the listing — orchestration likely involves a simple fetch-and-parse loop. The main threat is tool misuse, specifically the scraping tool being manipulated to target internal network addresses (SSRF) or restricted local files.

Not certain from the listing — hosted as a web application. If the scraping infrastructure is not properly sandboxed, an attacker could exploit the scraper to perform port scanning, access cloud metadata services, or execute lateral movement within the hosting environment.

Not certain from the listing — no observability or guardrail mechanisms are mentioned. Gaps here could allow attackers to abuse the service for large-scale scraping, credential harvesting, or bypass rate limits without detection.

Not certain from the listing — being a free, closed-source tool, it likely lacks formal compliance certifications (e.g., SOC2, ISO) or robust access control policies, posing compliance risks if used to process sensitive or proprietary data.

Not certain from the listing — operates as a standalone utility with no described multi-agent or ecosystem integrations, minimizing direct agent-to-agent trust threats.