Graphlit — agentic threat model
Graphlit presents a high data-centric risk profile as a serverless RAG-as-a-Service platform, where the primary threats involve data poisoning, embedding inversion, and unauthorized access to ingested unstructured enterprise data.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.20 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.80 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.30 | |
| 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.
Not certain from the listing — Graphlit integrates with external LLMs, making it susceptible to adversarial prompt injection, model misalignment, or data leakage if the underlying foundation models are compromised or manipulated.
As a RAG-as-a-Service platform handling automated ETL, multimodal ingestion, and vector embeddings, the primary threats are data poisoning of the vector store, embedding inversion, and unauthorized exfiltration of sensitive unstructured data.
Graphlit provides conversation history management and LLM integration tools. Vulnerabilities here include memory poisoning within the conversation history and insecure orchestration of the RAG pipeline.
Not certain from the listing — Being a serverless platform, infrastructure security depends on the cloud provider's isolation, but threats include container escape, API key exposure, and insecure serverless function execution.
Not certain from the listing — The description does not mention built-in guardrails or evaluation metrics, leaving potential blind spots in detecting drift, toxic outputs, or RAG retrieval anomalies.
Not certain from the listing — No specific compliance certifications (e.g., SOC2, ISO) or fine-grained access control mechanisms are detailed, posing risks to regulatory alignment when handling sensitive enterprise data.
Not certain from the listing — While designed to build AI-powered applications and agents, the platform's role in multi-agent orchestration or marketplace interactions is not specified, though cascading failures from compromised upstream data sources remain a risk.
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.