Should organisations prefer proprietary AI models for offensive testing?

Only if the vendor can show that the model, inference path, and audit controls are tightly governed. Proprietary ownership can reduce third-party dependency and improve consistency, but it does not automatically improve security. The deciding factor is whether the system can be bounded, validated, and monitored like any other privileged workload.

Why This Matters for Security Teams

For offensive testing, the model choice changes the risk boundary. A proprietary model may give a security team more control over hosting, logging, patching, and change management, but it also concentrates trust in a single vendor and can hide details that matter during red team work. The real question is not ownership alone, but whether the model can be governed as a privileged system with clear limits, reviewability, and rollback paths. That aligns closely with NIST SP 800-53 Rev 5 Security and Privacy Controls, especially where access control, auditability, and change monitoring are concerned.

Teams also need to separate model capability from test safety. A proprietary model can still produce unsafe outputs, leak sensitive prompts, or amplify flawed assumptions in a test plan. If the model is used to generate attack paths, payload variants, or adversary emulation steps, then prompt handling, output filtering, and approval workflows become part of the control surface. In practice, many security teams encounter model risk only after an offensive test has already produced an unreviewed action or an unusable result, rather than through intentional governance.

How It Works in Practice

In a well-run offensive testing workflow, a proprietary model is treated as a controlled component inside the testing stack. That means defining who can prompt it, what sources it can see, how outputs are logged, and when a human must approve the next action. The model should not be allowed to improvise access, trigger tools, or modify targets without explicit guardrails. This is where AI security guidance becomes practical: the model must be bounded against prompt injection, output manipulation, and tool abuse, not just measured for accuracy. NIST’s AI governance guidance in NIST AI Risk Management Framework is useful here, and adversarial testing concerns map naturally to MITRE ATLAS when the model is part of a larger attack simulation pipeline.

Operationally, teams usually compare proprietary and open models across a few controls:

• Can the model be hosted in a boundary the organisation controls?
• Can prompts, outputs, and tool calls be audited end to end?
• Can the model be version-pinned so test results are reproducible?
• Can sensitive data be excluded from training, retention, and evaluation paths?
• Can the system be disabled quickly if it begins generating unsafe or misleading content?

Current guidance suggests the same principle should apply whether the model is used for reconnaissance, exploit chaining, or report drafting: if the output can influence a security decision, it needs provenance, review, and traceability. That is especially important where the model is connected to detection engineering, SOAR playbooks, or vulnerability validation. These controls tend to break down when the offensive testing workflow is distributed across unmanaged notebooks, ad hoc API keys, and multiple vendors because chain-of-custody for prompts and outputs is lost.

Common Variations and Edge Cases

Tighter control over a proprietary model often increases cost, integration effort, and operational overhead, requiring organisations to balance repeatability against flexibility. There is no universal standard for preferring proprietary over open models in offensive testing; the right answer depends on the sensitivity of the targets, the maturity of the review process, and whether the team can independently validate the model’s behavior. For some teams, a proprietary model is safer because it is easier to restrict and monitor. For others, the closed nature of the system makes it harder to inspect failure modes or explain a bad test result.

Edge cases matter. In air-gapped environments, a proprietary model may be attractive if it can be fully hosted and logged internally. In regulated environments, especially where offensive testing touches production data or customer identities, model retention and prompt persistence may be unacceptable unless explicitly controlled. Where agentic workflows are involved, the key issue is not model brand but whether the AI agent has execution authority. That intersection is where OWASP’s guidance on OWASP Top 10 for LLM Applications becomes relevant, particularly for prompt injection and tool misuse.

Best practice is evolving, but the decision should always be based on governance quality, not perceived sophistication. Proprietary models are not inherently more secure for offensive testing, and open models are not inherently less safe. What matters is whether the organisation can bound the model like any other privileged workload and prove that the testing output is trustworthy enough to act on.

Related resources from NHI Mgmt Group

• Should organisations invest in AI offensive testing before adversaries do?
• What should organisations check before relying on a managed training platform for custom AI models?
• What should organisations monitor in AI workflows that use reasoning models?
• What do organisations get wrong about semantic models in AI governance?