Qauntalogic — agentic threat model
Quantalogic presents a high-risk profile due to its core capabilities of code editing and execution, though this is partially mitigated by its built-in Docker-based sandboxing.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.40 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.20 | |
| Non-Determinism | 0.70 | |
| 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.
Supports universal LLMs (OpenAI, Anthropic, DeepSeek). Primary threats include prompt injection leading to malicious code generation or unauthorized tool execution.
Not certain from the listing — details on data operations, vector databases, or RAG pipelines are not specified, though code search implies some indexing mechanism.
The framework orchestrates code editing, code search, and web search. Insecure tool integration or tool misuse (e.g., executing destructive commands via code editing) represents a major threat vector.
Features Docker-based secure code execution. Threats include container escape, privilege escalation within the container, and exposure of the web interface services.
Provides real-time monitoring with a web interface. Threats include unauthorized access to the monitoring dashboard and potential blind spots in logging agent actions.
Not certain from the listing — while described as 'enterprise-ready', specific authentication, authorization, or compliance controls (like RBAC or NIST alignment) are not detailed.
Not certain from the listing — there is no explicit mention of multi-agent orchestration or marketplace integrations that could lead to cascading agent-to-agent trust issues.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).