DeepSeek V3 — agentic threat model
DeepSeek V3 is a highly capable, open-source foundation model with significant reasoning and coding capabilities, but as a raw model, it lacks built-in agentic guardrails, sandboxing, or orchestration, presenting risks primarily related to model alignment, output non-determinism, and opaque MoE routing.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.30 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.80 |
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.
As a 671B parameter MoE foundation model, L1 is the primary risk surface. Key threats include adversarial prompt injection, jailbreaking, model reprogramming, and the generation of malicious code or misaligned outputs. Being open-source, the model weights are publicly accessible, mitigating model-stealing threats but increasing the risk of malicious fine-tuning.
Not certain from the listing — while the model was trained on 14.8 trillion tokens (introducing potential data poisoning or copyright/provenance risks during pre-training), the listing does not detail any native RAG, vector database integrations, or runtime data operations.
Not certain from the listing — DeepSeek V3 is a raw foundation model and does not natively include an agentic framework, planning loops, memory management, or tool-calling orchestration in this directory listing.
Not certain from the listing — hosting and serving a 671B MoE model requires massive infrastructure and specialized FP8 execution environments, but specific deployment sandboxing, network isolation, or secrets management are not detailed.
Not certain from the listing — while benchmark performance is heavily documented, real-time observability, output guardrails, logging, and drift detection mechanisms for production deployments are not specified.
Not certain from the listing — no specific compliance certifications (such as SOC2, ISO 27001, or EU AI Act alignment) or built-in policy enforcement mechanisms are mentioned in the public directory.
Not certain from the listing — the model itself does not natively operate within a multi-agent ecosystem or marketplace without external orchestration frameworks.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).