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ReviseCV — agentic threat model

7.0AIVSS 7.0 · High

ReviseCV is a low-autonomy, document-focused AI assistant with minimal agentic risk, primarily presenting data privacy (PII) and document parsing security risks rather than systemic agentic threats.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 6.5AARS uplift 0.53Factor sum 1.6/10Threat ×0.95Mitigation ×1.0
Autonomy of Action
0.10
Goal-Driven Planning
0.20
Self-Modification
0.00
Dynamic Tool Use
0.10
Persistent Memory
0.30
Contextual Awareness
0.30
Dynamic Identity
0.00
Multi-Agent Interactions
0.00
Non-Determinism
0.40
Opacity & Reflexivity
0.20

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.

L1 · Foundation Models⚠ not certain from listing

Not certain from the listing — likely relies on commercial LLMs via API for resume rewriting and cover letter generation. Primary threats include prompt injection via user-provided job descriptions and potential leakage of system instructions.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — processes highly sensitive PII (resumes, contact info, work history). Risks include insecure storage of uploaded documents, lack of data retention controls, and potential parsing vulnerabilities (e.g., PDF/DOCX exploits).

L3 · Agent Frameworks⚠ not certain from listing

Not certain from the listing — orchestration is likely a straightforward linear pipeline (input -> LLM -> template rendering) rather than a complex agentic framework. Main threat is indirect prompt injection from untrusted job descriptions.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — standard web application hosting. Threats include insecure cloud storage buckets containing user resumes, lack of isolation in document generation environments, and typical web application vulnerabilities.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — likely lacks advanced LLM-specific observability. Risks include a lack of detection for adversarial inputs designed to break the resume parser or inject malicious content into generated PDFs.

L6 · Security & Compliance (cross-cutting)⚠ not certain from listing

Not certain from the listing — requires user authentication for the freemium model. Key threats include broken object-level authorization (accessing other users' resumes) and non-compliance with privacy regulations like GDPR/CCPA regarding PII deletion.

L7 · Agent Ecosystem⚠ not certain from listing

Not certain from the listing — operates as a standalone horizontal tool with no indicated multi-agent or ecosystem integrations. Ecosystem threats are currently negligible.

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.