Latta AI — agentic threat model
Latta AI focuses on automated bug detection without direct code access, presenting a moderate risk profile primarily associated with the potential exposure of staging environments, APIs, or application telemetry during testing.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.30 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.20 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.30 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.40 | |
| 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 — The underlying foundation model is unspecified, leaving potential vulnerabilities to adversarial inputs or model-reprogramming during black-box testing unaddressed.
Not certain from the listing — It is unclear how application telemetry, logs, or execution data are ingested and stored without direct code access, risking data exfiltration if sensitive runtime data is captured.
Not certain from the listing — The orchestration framework for executing test cases and identifying anomalies is unknown, posing risks of insecure tool execution if the agent interacts with live APIs.
Not certain from the listing — The execution environment (sandbox vs. local) for running tests against target applications is unspecified, raising concerns about potential lateral movement or container escape.
Not certain from the listing — No details are provided regarding guardrails or observability mechanisms to prevent the agent from generating harmful test payloads or flooding systems with false positives.
Not certain from the listing — Compliance certifications, access controls, and data privacy policies for handling proprietary application behavior data are not documented.
Not certain from the listing — There is no indication of multi-agent orchestration or ecosystem integration, suggesting a standalone operation with minimal A2A risk.
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.