CloneByMe — agentic threat model
CloneByMe presents a unique risk profile centered on hyper-realistic identity cloning (voice and avatar) and local execution. While local deployment mitigates cloud-based data exposure, the ingestion of sensitive business files and potential for identity impersonation pose significant security and social engineering risks.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.40 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.80 | |
| Multi-Agent Interactions | 0.20 | |
| Non-Determinism | 0.60 | |
| 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.
The agent integrates with external LLMs and utilizes specialized models for hyper-realistic voice and avatar cloning. This introduces risks of model reprogramming, adversarial inputs manipulating the avatar's output, and potential model stealing of the proprietary cloning weights.
Users upload photos, voice samples, and business-specific files to train the AI. This creates a high risk of data poisoning if malicious files are ingested, as well as data exfiltration risks if the local vector store or training pipeline is compromised.
Not certain from the listing — details on the orchestration framework, memory handling, or tool execution are not specified, but automated task execution poses risks of tool misuse or insecure integration with external LLMs.
The agent operates locally, which shifts the infrastructure threat landscape to the user's local host. Threats include local privilege escalation, unauthorized access to the local API, and lack of sandboxing for the executed tasks.
Not certain from the listing — no monitoring, logging, or guardrail mechanisms are mentioned for the local deployment or LLM integration to detect drift or malicious inputs.
Not certain from the listing — while local operation is cited for privacy, there is no mention of identity, authorization, or compliance frameworks (like GDPR or SOC2) for the cloned data.
Not certain from the listing — there is no explicit mention of multi-agent coordination or marketplace interactions, though integration with external LLMs is supported.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).