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Vibe Kanban — agentic threat model

7.1AIVSS 7.1 · High

Vibe Kanban acts as an orchestrator driving powerful coding agents in isolated workspaces, presenting a high-risk profile due to its ability to execute code and modify repositories, though mitigated by isolated workspaces and human-in-the-loop diff reviews.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 8.5AARS uplift 0.96Factor sum 6.1/10Threat ×1.05Mitigation ×0.75
Autonomy of Action
0.70
Goal-Driven Planning
0.80
Self-Modification
0.20
Dynamic Tool Use
0.90
Persistent Memory
0.40
Contextual Awareness
0.70
Dynamic Identity
0.50
Multi-Agent Interactions
0.80
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 — relies on external foundation models (Claude, Gemini, Codex) driven via their respective CLIs; vulnerable to prompt injection or model reprogramming that could cause the underlying models to generate malicious code or bypass safety guardrails.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — operates on codebase files and kanban issue descriptions rather than a dedicated vector database; risk involves unauthorized data exfiltration of proprietary source code or poisoning of the codebase context provided to the agents.

L3 · Agent Frameworks✓ mapped

The orchestration framework wraps and drives external agent CLIs (Claude Code, Gemini CLI, etc.) to execute tasks. Vulnerabilities include insecure tool integration, command injection via issue descriptions, and the risk of agents executing unintended shell commands or modifying files outside the intended scope.

L4 · Deployment & Infrastructure✓ mapped

Runs coding agents in isolated workspaces to execute tasks. This sandboxing is a critical control to prevent container escape, host compromise, and lateral movement, though the security depends heavily on the robustness of the isolation mechanism used for these workspaces.

L5 · Evaluation & Observability✓ mapped

Provides diff reviews with inline comments, enabling human-in-the-loop verification of agent outputs before merging. However, there is a risk of blind spots if malicious code changes are subtle or if reviewers suffer from automation bias and approve malicious diffs.

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

Not certain from the listing — open-source tool coordinating local or cloud CLI runs; lacks explicit mention of enterprise identity management, role-based access control (RBAC), or compliance auditing for who can dispatch agents or approve diffs.

L7 · Agent Ecosystem✓ mapped

Coordinates multiple concurrent agent runs (Claude Code, Codex, Gemini CLI, Amp) against a single kanban board. This multi-agent orchestration introduces risks of cascading failures, race conditions on shared codebases, and cross-agent trust abuse where one compromised agent influences another.

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