ResumeBoostAI — agentic threat model

Not certain from the listing — likely relies on third-party commercial LLMs via API. Primary threats include prompt injection to bypass freemium restrictions or generate unauthorized content, and potential model-use data leakage if user inputs are not opted out of training.

Not certain from the listing — processes highly sensitive PII (full names, contact info, employment history). Risks include insecure storage of uploaded resumes/CVs, lack of data sanitization before processing, and potential data exfiltration via indirect prompt injection embedded in job descriptions.

Not certain from the listing — likely uses a basic orchestration wrapper for document parsing and text generation. The main threat is insecure integration with document parsing libraries (e.g., PDF/DOCX parsers) which may be vulnerable to remote code execution or denial of service.

Not certain from the listing — deployed as a web application with API access. Standard web infrastructure risks apply, including insecure API endpoints, lack of rate limiting on the freemium tier, and insufficient sandboxing of document processing environments.

Not certain from the listing — no mention of LLM guardrails, evaluation frameworks, or observability tools. This creates blind spots regarding prompt injection attempts, generation drift, or abusive API usage.

Not certain from the listing — likely uses standard web authentication. Compliance is a major concern here, as storing and processing resumes requires strict adherence to GDPR, CCPA, and other data privacy regulations regarding user data deletion and consent.

No multi-agent or marketplace interactions are described in the listing; the agent operates as a standalone productivity tool, meaning ecosystem risks are currently negligible.