Qualligence — agentic threat model
Qualligence acts as a builder of custom AI agents and LLM applications for data science, presenting a broad attack surface that depends heavily on the security of the bespoke implementations and deployment environments.
OWASP AIVSS score rationale
| Autonomy of Action | 0.50 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.40 | |
| Non-Determinism | 0.50 | |
| 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.
Not certain from the listing — as a custom AI agent creator, Qualligence likely utilizes various commercial or open-source foundation models depending on client needs, exposing them to standard LLM risks like prompt injection, data poisoning, and misaligned outputs.
Not certain from the listing — data science applications typically require extensive data pipelines, vector databases, and RAG architectures, which are vulnerable to training data poisoning, embedding inversion, and unauthorized data exfiltration.
Not certain from the listing — the orchestration frameworks used to build these custom agents could be susceptible to tool misuse, insecure tool integration, and memory poisoning if state is maintained across sessions.
Not certain from the listing — deployment likely occurs via APIs or cloud hosting, presenting risks of container compromise, insecure API endpoints, and credential exposure if secrets are not properly managed.
Not certain from the listing — monitoring and guardrails would depend on the specific implementation, with potential blind spots in drift detection and insufficient logging of agent decisions.
Not certain from the listing — compliance frameworks (like SOC2, GDPR, or ISO) and identity/access management controls are not specified in the high-level directory listing.
Not certain from the listing — while they craft 'intelligent applications', it is unclear if these agents interact in a multi-agent ecosystem or marketplace, which would introduce risks of cascading failures and A2A trust abuse.
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.