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kling 3.5 — agentic threat model

6.1AIVSS 6.1 · Medium

Kling 3.5 is a specialized video generation model with low agentic autonomy, meaning its primary risks center on model-level vulnerabilities (e.g., deepfakes, jailbreaks, and intellectual property theft) rather than systemic orchestration or tool-use exploits.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 5.3AARS uplift 0.85Factor sum 1.8/10Threat ×1.0Mitigation ×1.0
Autonomy of Action
0.10
Goal-Driven Planning
0.00
Self-Modification
0.00
Dynamic Tool Use
0.00
Persistent Memory
0.00
Contextual Awareness
0.10
Dynamic Identity
0.00
Multi-Agent Interactions
0.00
Non-Determinism
0.80
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.

L1 · Foundation Models✓ mapped

Kling 3.5 is a foundation video generation model. Primary threats include adversarial prompt injection (jailbreaking to generate NSFW, copyrighted, or deepfake content), model stealing/IP theft, and output misalignment.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — likely relies on massive video-image-text datasets for training and handles user-uploaded reference images. Threats include training data poisoning, copyright/provenance issues, and potential exfiltration of user-uploaded assets.

L3 · Agent Frameworks⚠ not certain from listing

Not certain from the listing — Kling 3.5 appears to be a direct model inference service rather than a complex agentic framework with planning, memory, or tool-use capabilities.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — likely deployed on high-performance GPU cloud infrastructure. Threats include container escape, resource exhaustion (GPU denial of service), and insecure API endpoints.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — likely relies on automated input/output content moderation filters to block unsafe prompts and images, but specific logging, observability, and drift detection are unverified.

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

Not certain from the listing — closed-source commercial service, but no compliance certifications (like SOC2 or ISO 27001) or explicit data privacy guarantees are detailed in the listing.

L7 · Agent Ecosystem⚠ not certain from listing

Not certain from the listing — operates primarily as a standalone vertical video generation tool with no described multi-agent or marketplace integrations.

MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).