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Warp — agentic threat model

9.9AIVSS 9.9 · Critical

Warp's integration of AI agents directly into a terminal environment presents a high-risk profile due to the agent's proximity to local system execution, sensitive environment variables, and developer credentials.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 9.8AARS uplift 0.13Factor sum 5.8/10Threat ×1.1Mitigation ×1.0
Autonomy of Action
0.50
Goal-Driven Planning
0.60
Self-Modification
0.20
Dynamic Tool Use
0.90
Persistent Memory
0.50
Contextual Awareness
0.80
Dynamic Identity
0.80
Multi-Agent Interactions
0.40
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.

L1 · Foundation Models⚠ not certain from listing

Not certain from the listing — Warp likely relies on external LLM APIs (e.g., OpenAI) for its agent capabilities. Threats include prompt injection that could manipulate the agent into generating or executing malicious shell commands.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — The agent likely accesses local terminal history, configurations, and codebase context to provide relevant suggestions. Threats include local data exfiltration and poisoning of the command history context.

L3 · Agent Frameworks⚠ not certain from listing

Not certain from the listing — Warp orchestrates natural language inputs into terminal actions. Insecure tool integration is a critical threat here, as the agent has direct access to the shell and could execute destructive commands.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — Warp runs as a local desktop application. If the application or its network connection to AI APIs is compromised, it could lead to local privilege escalation or host compromise.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — It is unclear what guardrails or observability tools are in place to intercept and block unsafe command generation before execution in the terminal.

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

Not certain from the listing — As a closed-source developer tool, Warp's telemetry policies, data privacy controls, and enterprise compliance features are not detailed in the public listing.

L7 · Agent Ecosystem⚠ not certain from listing

Not certain from the listing — While 'built-in AI agents' are mentioned, it is unclear if these agents interact with external agent registries, third-party plugins, or other developer ecosystems.

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.