Qevlar AI — agentic threat model
Qevlar AI presents a high-risk profile due to its autonomous remediation capabilities and deep integration into critical security infrastructure like SIEM, EDR, and cloud environments. A compromise or successful prompt injection could allow adversaries to suppress alerts, manipulate incident response workflows, or abuse powerful security tools to disrupt operations.
OWASP AIVSS score rationale
| Autonomy of Action | 0.85 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.90 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.20 | |
| 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 — Qevlar AI is closed-source and does not specify the underlying foundation models used. Potential threats include adversarial prompt injection designed to bypass triage logic or model reprogramming to ignore specific malicious alerts.
Not certain from the listing — details on vector databases, RAG pipelines, or threat intelligence data operations are not specified. Threats include poisoning the threat intelligence feed or RAG data to white-list malicious IPs, hashes, or domains.
The agent orchestrates multi-step investigations, formulates hypotheses, and executes autonomous remediation. Threats include tool misuse (e.g., executing destructive commands or isolating legitimate hosts via EDR/SOAR integrations) and insecure tool integration if input sanitization on alert data is weak.
Qevlar offers SaaS or private-cloud deployment with headless integrations. Threats include container compromise, credential theft of highly sensitive SIEM/EDR/Cloud API keys, and lateral movement into the enterprise network from the private-cloud deployment.
Not certain from the listing — while the agent documents conclusions directly into tickets, specific evaluation guardrails, drift detection, or continuous monitoring of the agent's decision-making are not detailed. Threats include blind spots where the agent silently fails to detect or report an ongoing attack.
Not certain from the listing — no specific compliance certifications (such as SOC2, ISO 27001) or fine-grained RBAC policies are detailed in the description, despite targeting enterprise SOCs and MSSPs. Threats include unauthorized access to the agent's orchestration plane.
Not certain from the listing — there is no mention of multi-agent collaboration or marketplace integrations, focusing instead on direct integrations with SIEM/EDR/SOAR. Threats include cascading failures if integrated SOAR/EDR APIs change or fail.
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.