Coval (YC S24) — agentic threat model

Not certain from the listing — Coval likely utilizes foundation models to generate simulated user personas and evaluate agent responses. Threats include adversarial prompt injection during simulation and model misalignment leading to false validation results.

Not certain from the listing — The platform manages test datasets, interaction logs, and evaluation gold standards. Risks include test data poisoning, which could mask agent vulnerabilities, and the exfiltration of proprietary interaction logs.

Coval orchestrates automated simulations across chat and voice modalities. Vulnerabilities in its orchestration framework could allow simulated agents to break out of the test harness or execute unauthorized actions via insecure tool integrations.

Not certain from the listing — As a SaaS platform integrating with continuous integration (CI) pipelines, infrastructure threats include the exposure of API keys, insecure webhook endpoints, and lateral movement from compromised test environments into production systems.

As an evaluation and observability platform, Coval's core risk is evaluation gaming, where target agents are optimized to pass specific statistical validations while remaining unsafe in production, alongside potential blind spots in edge case detection.

Not certain from the listing — No specific compliance frameworks (e.g., SOC2, ISO 27001) or fine-grained access control mechanisms are detailed in the public directory listing.

Coval operates directly within an agent ecosystem by simulating user-to-agent and agent-to-agent interactions. Threats include cascading failures during multi-agent testing and trust abuse between the simulation harness and the target agents.