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Pinkfish AI — agentic threat model

9.0AIVSS 9.0 · Critical

Pinkfish AI presents a high-risk profile due to its powerful combination of browser automation, API execution, and 200+ enterprise integrations. While deterministic execution options provide some control, the self-healing and agentic capabilities could be exploited to perform unauthorized actions or exfiltrate sensitive enterprise data if compromised.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 8.5AARS uplift 0.98Factor sum 6.2/10Threat ×1.05Mitigation ×0.95
Autonomy of Action
0.80
Goal-Driven Planning
0.70
Self-Modification
0.50
Dynamic Tool Use
0.90
Persistent Memory
0.50
Contextual Awareness
0.70
Dynamic Identity
0.60
Multi-Agent Interactions
0.40
Non-Determinism
0.50
Opacity & Reflexivity
0.60

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 — The specific foundation models powering Pinkfish AI are not disclosed, leaving risks like model-specific adversarial vulnerabilities or data poisoning unverified.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — While the platform integrates with data-rich systems like Salesforce and Zendesk, the underlying data operations, vector stores, and RAG mechanisms are not detailed.

L3 · Agent Frameworks✓ mapped

Pinkfish AI orchestrates complex workflows using natural language, browser automation, and 200+ integrations. This introduces significant risks of tool misuse, prompt injection leading to unauthorized API calls, and execution of unintended browser actions.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — The execution environment for browser and API automations is not described, leaving sandboxing, container isolation, and secrets management practices unverified.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — Although 'self-healing workflows' imply some level of execution monitoring, the platform's security observability, guardrails, and logging capabilities are not specified.

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

Not certain from the listing — The platform claims to be 'enterprise-grade' but does not explicitly cite specific compliance certifications (e.g., SOC 2, ISO 27001) or identity governance controls.

L7 · Agent Ecosystem⚠ not certain from listing

Not certain from the listing — While users can build multiple AI agents and automations, the presence of a multi-agent ecosystem, agent-to-agent trust boundaries, or a marketplace is not detailed.

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.