JobHuntr.fyi — agentic threat model
JobHuntr.fyi presents a moderate-to-high risk profile due to its high autonomy in executing real-world actions (LinkedIn automation, direct messaging) on behalf of the user, combined with its execution as a native macOS application. While its on-device, Ollama-powered architecture significantly mitigates cloud-based data leakage risks, a compromise of the local application or session credentials could lead to severe identity theft or account takeover.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.70 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.50 | |
| Opacity & Reflexivity | 0.40 |
Scored with the canonical OWASP AIVSS formula (AIVSS calculator reference); agentic risk factors estimated from the agent’s described capabilities.
MAESTRO 7-layer threat model
Per-layer threats for this agent. Layers tagged “not certain from listing” are general, caveated commentary where the public description didn’t pin that layer.
Uses Ollama-powered local LLMs. This eliminates cloud-based model-stealing and data-leakage risks, but the model remains susceptible to local prompt injection attacks if malicious job descriptions or recruiter messages are processed.
Processes highly sensitive personal data (resumes, cover letters, application history) locally. While this protects privacy from external cloud providers, local storage must be secured against unauthorized access by other local processes on the macOS host.
Orchestrates multi-step workflows including semantic filtering, cover letter generation, and automated LinkedIn interactions. Vulnerabilities in the automation scripts or browser integration could lead to unintended actions, such as spamming recruiters or submitting incorrect applications.
Deployed as a native macOS desktop application running Ollama locally. The primary threat is local privilege escalation or lack of application sandboxing, which could allow a compromised agent to access broader system resources.
Not certain from the listing — there is no explicit mention of built-in guardrails, content filtering, or detailed audit logging to monitor the LLM's generated messages before they are sent to hiring teams.
Not certain from the listing — while open-source and privacy-focused, there is no mention of formal security certifications, automated dependency scanning, or compliance with platform policies (e.g., LinkedIn's terms of service regarding automation).
Not certain from the listing — the application appears to operate as a standalone single-user agent interacting directly with web platforms, with no explicit multi-agent collaboration or marketplace integrations.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.