KushoAI — agentic threat model
KushoAI presents a high-risk profile due to its ability to autonomously execute AI-generated test code within sensitive environments like CI/CD pipelines. A compromise of the agent could allow attackers to inject malicious code into the testing pipeline or launch unauthorized attacks against target APIs.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.10 | |
| 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.
Uses trained LLMs to plan test scenarios and write test code. Threats include prompt injection leading to malicious test generation, model poisoning, and misaligned outputs that fail to identify critical API vulnerabilities.
Ingests OpenAPI specs, Postman Collections, and cURL commands. Threats include data exfiltration of sensitive API endpoints, keys, or schemas, and data poisoning via malicious API specifications designed to exploit the parser.
Orchestrates scenario planning and test execution. Threats include insecure tool integration where the framework executes arbitrary code disguised as generated tests, or tool misuse during autonomous CI/CD execution.
Executes tests from a web-app or CI/CD pipeline. Threats include container escape, privilege escalation within the runner environment, and unauthorized lateral movement if the execution environment is not properly sandboxed.
Generates assertions to check accuracy, reliability, and performance. Threats include evaluation gaming where malicious API behavior is falsely marked as passed, and blind spots in AI-generated assertions that miss subtle security flaws.
Not certain from the listing — No explicit security compliance certifications (e.g., SOC2, ISO), access control mechanisms, or human-in-the-loop (HITL) guardrails are detailed in the public directory listing.
Not certain from the listing — There is no mention of multi-agent orchestration, agent-to-agent communication, or marketplace integrations in the provided features.
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.