Jobeze — agentic threat model
Jobeze presents moderate agentic risk primarily due to its automated job application feature, which acts on behalf of users across external platforms, combined with the handling of sensitive personally identifiable information (PII) in resumes.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 0.20 | |
| Non-Determinism | 0.40 | |
| Opacity & Reflexivity | 0.30 |
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.
Not certain from the listing — No details are provided regarding the underlying foundation models used for job matching and recommendations, leaving them vulnerable to standard adversarial prompt injection or model alignment risks.
Handles highly sensitive user data including resumes, work history, and contact details. This introduces significant risks of data exfiltration, resume-based prompt injection (where a malicious resume manipulates the parser), and PII leakage.
The orchestration framework manages automated job searching and application submission. Vulnerabilities here could lead to tool misuse, such as the agent spamming external job boards or submitting applications to unintended or malicious listings.
Not certain from the listing — While noted as open source, the hosting environment, sandboxing of the application-submission engine, and secrets management for user credentials on external job boards are unspecified.
Not certain from the listing — There is no mention of real-time guardrails, evaluation frameworks, or logging mechanisms to monitor the correctness and safety of automated applications.
As a recruitment platform handling candidate PII, compliance with regulations like GDPR or CCPA is critical. The listing does not detail access controls, encryption standards, or privacy policies governing user data.
The agent interacts with external ecosystems (job boards, applicant tracking systems). This creates risks of trust abuse, where the agent is treated as a trusted user by external platforms, potentially leading to IP blocking or cascading failures if external APIs change.
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.