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Offer Bull — agentic threat model

6.2AIVSS 6.2 · Medium

Offer Bull is a low-autonomy, human-in-the-loop interview assistant presenting low systemic risk, with primary threats centered around the privacy of candidate PII, real-time audio data exfiltration, and potential prompt injection leading to sabotaged interview responses.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 5.3AARS uplift 0.94Factor sum 2.0/10Threat ×1.0Mitigation ×1.0
Autonomy of Action
0.10
Goal-Driven Planning
0.20
Self-Modification
0.00
Dynamic Tool Use
0.10
Persistent Memory
0.20
Contextual Awareness
0.50
Dynamic Identity
0.00
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.

L1 · Foundation Models⚠ not certain from listing

Not certain from the listing — likely relies on third-party foundation models for speech-to-text and text generation. Main threats include prompt injection via interview questions designed to hijack the model, and output hallucinations that could sabotage the candidate's interview.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — must ingest real-time audio or text transcripts of interviews, alongside candidate resumes. Threats include unauthorized retention or exfiltration of sensitive personal data (PII) and proprietary interview questions.

L3 · Agent Frameworks⚠ not certain from listing

Not certain from the listing — likely uses a lightweight orchestration framework to map audio transcripts to prompt templates. Threats include insecure session state management and context leakage across different interview sessions.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — likely deployed as a web application or browser extension. Threats include insecure WebSocket connections for real-time audio streaming and standard web application vulnerabilities like cross-site scripting (XSS).

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — no mention of real-time guardrails or output filtering. Threats include a lack of monitoring for toxic, biased, or highly inaccurate generated answers during live high-stakes scenarios.

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

Not certain from the listing — no compliance certifications (such as GDPR or SOC2) are mentioned. Threats include lack of explicit consent mechanisms for recording/processing third-party interviewer audio, posing legal and compliance risks.

L7 · Agent Ecosystem✓ mapped

The agent operates as a standalone vertical tool for a single user and does not interact with other agents or marketplaces. Threat of multi-agent cascading failures is 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.