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Latest Grok 3 AI — agentic threat model

8.0AIVSS 8.0 · High

Grok 3 is a highly capable, closed-source multimodal LLM with advanced reasoning and web-search capabilities, presenting moderate-to-high risk primarily through indirect prompt injection via DeepSearch and potential execution of untrusted code.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 6.5AARS uplift 1.54Factor sum 4.4/10Threat ×1.0Mitigation ×1.0
Autonomy of Action
0.40
Goal-Driven Planning
0.60
Self-Modification
0.10
Dynamic Tool Use
0.50
Persistent Memory
0.30
Contextual Awareness
0.70
Dynamic Identity
0.10
Multi-Agent Interactions
0.20
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.

L1 · Foundation Models✓ mapped

Grok 3 is a closed-source foundation model. Primary threats include adversarial prompt injection, model extraction/stealing, and potential training data poisoning.

L2 · Data Operations✓ mapped

DeepSearch relies on real-time web data retrieval. This introduces severe risks of indirect prompt injection from poisoned web pages and data exfiltration of sensitive user queries.

L3 · Agent Frameworks⚠ not certain from listing

Not certain from the listing — the orchestration framework for DeepSearch and Big Brain Mode is proprietary. Potential threats include insecure tool integration with the search engine and prompt injection leading to unauthorized tool execution.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — hosted by xAI. Threats include infrastructure compromise, lack of sandboxing for code execution if the model runs generated code, and API exposure.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — no details on guardrails or monitoring are provided. Gaps in drift detection or evaluation gaming could lead to unsafe outputs.

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

Not certain from the listing — compliance standards (like SOC2 or ISO) are not mentioned. Lack of visible access controls or audit logs for user data.

L7 · Agent Ecosystem⚠ not certain from listing

Not certain from the listing — no multi-agent or marketplace interactions are described. Potential future threats include cascading failures if integrated with other xAI or third-party agents.

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.