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What It Is

Vern Labs positions itself as “Security infrastructure for AI systems.” It is not a traditional network-traffic or code-scanning tool; instead, it is built to protect AI systems that can reason, retrieve information, and take actions. Its product suite consists of three components: Intertrace, Ghostline, and Blackbox. They can be deployed independently or coordinated through a unified control plane, covering runtime detection, agent authorization, and adversarial testing.

Core Capabilities

Intertrace is a runtime security gateway that sits between the application and the LLM, embedding model, retrieval layer, or tool server. It performs inline inspection of user/system prompts, retrieved context, tool arguments, and model outputs, with actions such as block, redact, transform, escalate, and allow+log. The published latency figures are around 18ms p50 and 45ms p99, while preserving streaming responses. Ghostline focuses on authorization at the agent action layer, issuing scoped capability tokens for each tool, resource, and external call, and adding human approval and auditing for high-impact actions. Blackbox provides continuous red-team testing, covering OWASP LLM Top 10, injection, jailbreaks, privilege abuse, tool misuse, and more, with reports available in PDF, JSON, and SARIF formats.

Deployment, Compliance, and Integrations

Deployment options are fairly comprehensive, including cloud, VPC, hybrid, self-hosted, on-prem, and air-gapped environments. Under the hood, it can be deployed via Docker, Helm, and Terraform. The single container is under 300MB and stateless, making it suitable for enterprise private deployments and isolated environments. On compliance, the site states that SOC 2 Type II is still in progress, while also mentioning FedRAMP-align, CMMC align, zero default retention, immutable audit logs, SIEM export, OTEL, and S3 audit. For integrations, it supports any LLM, open-source models, MCP, Python, Node, Go, HTTP proxy, LangChain, LangGraph, and more, giving it broad coverage.

Pricing

Pricing follows a pilot-to-enterprise sales model. The Pilot plan is free for 30 days and includes 100k requests/month, a single product, cloud deployment, and email support. Production is custom annual pricing and includes all three products, self-hosted or cloud deployment, a Slack channel, and a 4-hour SLA. Enterprise requires contacting sales and is aimed at air-gapped, on-prem, defense, and highly regulated scenarios. Pricing transparency is only moderate, but this is in line with the typical procurement process for enterprise security products.

Pros, Cons, and Who It’s For

The main strengths are that its protection scope maps closely to real AI application risks, covering the full loop of runtime defense, authorization, testing, and auditing. It also offers strong deployment flexibility, making it suitable for regulated industries. The drawbacks are that public information mainly comes from vendor descriptions, SOC 2 is not yet complete, Production/Enterprise pricing is not transparent, and implementation requires capabilities around security policy, gateway integration, and Agent framework integration. It is best suited for enterprise teams that already run production AI applications and need to control prompt injection, data leakage, tool abuse, and audit risks.

Access from China

The site does not provide information on access from mainland China, RMB payment, invoices, or local support, so china_access can only be marked as unknown. For deployment in China, key items to verify include console connectivity, model-provider routing, SIEM integration, contract terms, and cross-border data requirements. Possible alternatives include localized AI security gateways, privately deployed LLM security platforms, or self-built policy gateways.