Deckdrop.io — agentic threat model

Not certain from the listing — likely utilizes commercial LLMs for pitch deck analysis and summarization. The primary threat is indirect prompt injection via adversarial text embedded within uploaded pitch decks, which could manipulate the agent's evaluation or extract sensitive system prompts.

Not certain from the listing — relies on data enrichment pipelines, web scraping, and vector databases for competitor mapping. Threats include data poisoning of external market intelligence sources and unauthorized access or leakage of uploaded proprietary pitch decks stored in vector databases.

Not certain from the listing — orchestrates research steps such as parsing PDFs, querying search APIs, and synthesizing reports. Threats include insecure tool execution (e.g., SSRF or remote code execution via malicious PDF parsers) and prompt injection hijacking the research flow.

Not certain from the listing — hosted as a closed-source SaaS platform. Key threats include insecure storage of sensitive uploaded PDF pitch decks and a lack of strict tenant isolation, which could allow one VC user to access another's proprietary research.

Not certain from the listing — requires strict monitoring to prevent hallucinated financial data or incorrect competitor mapping. Gaps in observability could lead to silent failures in due diligence reports, resulting in poor investment decisions.

Not certain from the listing — handles highly confidential pre-revenue startup data and VC investment strategies, requiring robust access controls (RBAC) and data privacy compliance, but no specific compliance certifications (like SOC2) are listed.

Not certain from the listing — primarily operates as a standalone research tool with no explicit multi-agent or marketplace integrations mentioned, limiting ecosystem-level cascading risks.