Tergle — agentic threat model
Tergle presents a high-value target due to its access to sensitive financial and compliance data for auditing. While its integration of human auditors (HITL) mitigates autonomous execution risks, a compromise of its irregularity detection or data pipeline could lead to undetected fraud or severe data leaks.
OWASP AIVSS score rationale
| Autonomy of Action | 0.50 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.10 | |
| 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.
Not certain from the listing — likely utilizes proprietary or commercial LLMs optimized for document analysis. Threats include prompt injection designed to bypass irregularity detection or force false compliance reports.
Not certain from the listing — processes highly sensitive financial and compliance data. Threats include data exfiltration of proprietary financial records and poisoning of the reference data used to verify compliance.
Not certain from the listing — orchestrates specific audit workflows. Threats include insecure tool integration with financial databases or ERP systems, allowing unauthorized read/write access during automated checks.
Not certain from the listing — likely deployed as a closed-source SaaS platform. Threats include container compromise, unauthorized access to tenant data, and lack of network isolation for sensitive financial processing.
Not certain from the listing — requires highly robust logging for compliance. Threats include insufficient logging of AI decision-making paths, making it difficult to reconstruct how an irregularity was missed or flagged.
Tergle is explicitly designed for financial and compliance auditing and integrates human-in-the-loop (HITL) verification. Security controls must strictly align with financial regulations (e.g., SOX, GDPR); threats include unauthorized access to audit trails and compliance violations due to AI hallucination.
Not certain from the listing — no multi-agent or marketplace ecosystem is described. Threats are limited to standard API integrations with external financial systems rather than agent-to-agent trust abuse.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).