Bilic — agentic threat model
Bilic poses a moderate-to-high risk due to its positioning in financial security and compliance; a compromise could lead to undetected financial fraud, regulatory evasion, or exposure of highly sensitive financial data.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.50 | |
| Non-Determinism | 0.40 | |
| Opacity & Reflexivity | 0.50 |
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 relies on standard LLMs fine-tuned or prompted for financial compliance, which are vulnerable to prompt injection that could bypass compliance checks.
Not certain from the listing — requires access to sensitive financial records and regulatory databases, making it a high-value target for data exfiltration or knowledge-base poisoning.
Not certain from the listing — orchestrates compliance checks using tools; insecure tool integration could allow an attacker to manipulate audit results or execute unauthorized queries.
Not certain from the listing — must be hosted securely given the financial nature; vulnerabilities in deployment could expose sensitive financial APIs or credentials.
Not certain from the listing — requires strict audit logging and drift detection to ensure compliance evaluations remain accurate and untampered.
Not certain from the listing — although the agent itself is designed for compliance, its own access controls, authentication, and alignment with financial regulations (e.g., SOC2, GDPR) are unverified.
Not certain from the listing — 'agents' plural suggests a multi-agent setup where compromised sub-agents could feed malicious compliance data to the main orchestrator.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).