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xiaofei li — agentic threat model

7.1AIVSS 7.1 · High

xiaofei li is a closed-source generative AI video model with low agentic autonomy but high potential for misuse in generating realistic synthetic media (deepfakes). Its primary risks stem from model opacity, non-determinism, and the generation of highly convincing multi-shot video and synchronized audio without explicit safety controls detailed in the listing.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 6.0AARS uplift 1.12Factor sum 2.8/10Threat ×1.0Mitigation ×1.0
Autonomy of Action
0.20
Goal-Driven Planning
0.40
Self-Modification
0.00
Dynamic Tool Use
0.10
Persistent Memory
0.10
Contextual Awareness
0.30
Dynamic Identity
0.00
Multi-Agent Interactions
0.00
Non-Determinism
0.80
Opacity & Reflexivity
0.90

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.

L1 · Foundation Models✓ mapped

The foundation layer consists of ByteDance's closed-source generative video and audio models. Key threats include adversarial prompt injection to bypass safety filters, model extraction/stealing, and the generation of harmful or copyright-infringing synthetic media.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — details regarding the training dataset, video/audio corpus, or any retrieval-augmented generation (RAG) data sources are not provided. Potential threats include data poisoning and copyright infringement claims.

L3 · Agent Frameworks⚠ not certain from listing

Not certain from the listing — the orchestration framework managing the multi-shot narration and audio-video synchronization is proprietary. Threats include logic flaws in shot-splitting or prompt parsing that could be exploited to generate unintended content.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — the hosting infrastructure (presumably ByteDance cloud) is undisclosed. Threats include resource exhaustion (denial of service) due to the high computational cost of 2K video generation, and potential server-side request forgery (SSRF) if the model accepts external URL inputs.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — there is no mention of built-in guardrails, output filtering, or deepfake detection mechanisms. Gaps in observability could allow users to generate policy-violating content undetected.

L6 · Security & Compliance (cross-cutting)⚠ not certain from listing

Not certain from the listing — compliance with synthetic media regulations (such as watermarking requirements under the EU AI Act or China's CAC provisions) is not detailed. The paid, closed-source nature suggests basic access controls, but specific compliance frameworks are unverified.

L7 · Agent Ecosystem⚠ not certain from listing

Not certain from the listing — the agent operates as a standalone video generator with no indicated multi-agent or marketplace integrations. Ecosystem threats are currently minimal.

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.