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AilaFlow — agentic threat model

9.2AIVSS 9.2 · Critical

AilaFlow is a closed-source, no-code AI agent platform, presenting a broad attack surface where vulnerabilities in the orchestration layer or tenant isolation could lead to widespread compromise of user-configured agents and connected data sources.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 8.5AARS uplift 0.74Factor sum 4.9/10Threat ×1.0Mitigation ×1.0
Autonomy of Action
0.50
Goal-Driven Planning
0.50
Self-Modification
0.20
Dynamic Tool Use
0.60
Persistent Memory
0.50
Contextual Awareness
0.60
Dynamic Identity
0.30
Multi-Agent Interactions
0.40
Non-Determinism
0.70
Opacity & Reflexivity
0.60

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 — as a no-code platform, AilaFlow likely supports multiple third-party foundation models, exposing it to model-specific threats like prompt injection, adversarial reprogramming, and misaligned outputs depending on the user's choice of LLM.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — the platform likely allows users to connect data sources or vector databases for RAG, introducing risks of data poisoning, unauthorized data exfiltration, and lack of lineage tracking for user-built agents.

L3 · Agent Frameworks⚠ not certain from listing

Not certain from the listing — the orchestration framework for these no-code agents could be vulnerable to insecure tool integration, memory poisoning, or logic flaws in the planning and execution phases.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — hosting and execution environments for the generated agents may lack robust sandboxing, potentially exposing the platform to container escape, privilege escalation, or lateral movement.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — it is unclear what guardrails, logging, or drift detection mechanisms are built into the platform to monitor the behavior and performance of deployed agents.

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

Not certain from the listing — there is no public information regarding identity management, role-based access control (RBAC), or compliance certifications (like SOC2 or GDPR) for the platform or its generated agents.

L7 · Agent Ecosystem⚠ not certain from listing

Not certain from the listing — if the platform supports multi-agent orchestration or a marketplace, it faces risks of cascading failures, agent-to-agent trust abuse, and compromised third-party agent templates.

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.