Trent AI — agentic threat model

Not certain from the listing — The specific foundation models powering Trent AI's security agents are not disclosed. Potential risks include adversarial prompt injection bypassing the security agents' own reasoning or model-level evasion techniques.

Not certain from the listing — Details regarding how Trent AI stores, indexes, or processes environmental scan data and security logs are omitted. Risks include data poisoning of the security knowledge base or exfiltration of sensitive system configurations.

Trent AI uses a multi-agent framework to orchestrate scanning, risk judgment, and mitigation. Vulnerabilities in this orchestration layer could allow an attacker to manipulate the planning logic, leading to unauthorized tool execution or the suppression of security alerts.

Not certain from the listing — The deployment architecture and sandboxing mechanisms for Trent AI's scanning agents are not specified. If the agents run with high privileges to mitigate issues, a container escape or host compromise could lead to full infrastructure takeover.

As an evaluation and observability platform, Trent AI continuously monitors and evaluates security posture. A critical threat is 'evaluation gaming' or blind spots where sophisticated attackers bypass its detection heuristics, or cause denial of service via alert fatigue.

Trent AI acts as a security and compliance enforcement tool, addressing privilege escalation and data exfiltration. However, the platform itself must possess highly privileged access to target systems, making its own identity and access management (IAM) a high-value target.

The platform relies on a continuous multi-agent loop with specialized security agents. This introduces risks of agent-to-agent trust abuse, where a compromise of a single scanning agent could allow lateral movement and cascading failures across the entire security loop.