PerfectParser — agentic threat model

Not certain from the listing — likely utilizes commercial or open-source multimodal LLMs for document extraction. The primary threat is indirect prompt injection, where malicious instructions embedded within uploaded invoices or contracts manipulate the model to ignore extraction rules or leak data.

Not certain from the listing — processes highly sensitive business documents (invoices, contracts, receipts). Key threats include unauthorized data access, lack of tenant isolation in document storage, and potential data exfiltration if uploaded documents are cached or used for downstream training without consent.

Not certain from the listing — likely uses a basic orchestration framework to map natural language prompts to structured JSON/CSV schemas. Threats include insecure parsing of user-defined extraction prompts and lack of validation on the generated structured output.

Not certain from the listing — requires robust hosting to handle file uploads and parsing libraries. Threats include server-side request forgery (SSRF) or remote code execution (RCE) via exploits in underlying PDF/image parsing libraries (e.g., ImageMagick, PDFMiner) if not properly sandboxed.

Not certain from the listing — no mention of built-in monitoring or guardrails. Gaps in logging could allow attackers to silently exfiltrate data or perform bulk document scraping without triggering anomalies.

Not certain from the listing — handles financial and legal data but does not specify compliance standards (e.g., SOC 2, GDPR, HIPAA). This lack of visible compliance controls poses a significant risk for enterprise adoption.

The agent operates as a standalone utility tool for document parsing and does not interact with external agent ecosystems or marketplaces, making multi-agent cascading failures a non-issue.