How should security teams red team frontier or custom AI models before deployment?

Why This Matters for Security Teams

Model red teaming is not a box-ticking exercise. Frontier and custom AI models can be prompted into revealing sensitive context, bypassing policy, or taking unsafe actions when they are connected to tools, files, or internal systems. That makes pre-deployment testing a control for both model behaviour and the surrounding NIST Cybersecurity Framework 2.0 functions that govern risk, response, and recovery.

The practical issue is that the highest-value failures are rarely obvious in normal QA. Teams often validate polished demo prompts and miss the long-tail behaviours that appear under pressure: prompt injection, indirect instruction following, cross-turn leakage, and unsafe tool use. NHIMG research on the State of Secrets in AppSec shows that 43% of security professionals are already concerned about AI systems learning and reproducing sensitive information patterns from codebases, which is exactly why model testing has to include data exposure scenarios, not just harmful content checks.

In practice, many security teams discover model failure modes only after users or attackers have already chained prompts, hidden instructions, and external tools into a working exploit path.

How It Works in Practice

Effective red teaming starts by defining what “unsafe” means for the specific model and deployment context. A model that powers customer support, code generation, or internal copilots should be tested against different abuse cases, because the risk is not just toxic output. It also includes data leakage, impersonation, policy bypass, and unauthorized action through connected tools.

Current guidance from NIST Cybersecurity Framework 2.0 and model assurance practice suggests using a mix of automated and human-led testing. Automated suites should cover single-turn prompts, multi-turn conversation chains, role-play attacks, encoding tricks, and prompt injection patterns. Human testers should then probe for contextual weaknesses that automated scripts often miss, such as hidden instruction conflicts, unsafe memory retention, and whether the model can be steered into acting outside its intended scope.

For deployed agents or tool-using models, red teaming must extend beyond text responses. Testers should simulate access to email, ticketing systems, databases, code repositories, and workflow tools, then observe whether the model can be coaxed into over-sharing, escalating privileges, or taking actions that were never explicitly approved. This is where NHI governance becomes operational: the model may be the interface, but the real control plane is its credentials, scopes, and action boundaries.

• Test for jailbreaks, data leakage, misdirection, harmful output, and unwanted actions.
• Include multi-turn scenarios that preserve attacker intent across context windows.
• Probe tool use separately from chat behaviour, especially where the model can call APIs.
• Record prompts, outputs, decisions, and mitigation status so fixes can be verified before release.

NHIMG’s DeepSeek breach coverage is a reminder that model risk is not theoretical when sensitive material is already present in the training, retrieval, or operational environment. These controls tend to break down when a model is granted live tool access without narrowly scoped credentials because prompt attacks then become action paths, not just bad text.

Common Variations and Edge Cases

Tighter red teaming often increases cost and release friction, requiring organisations to balance deeper assurance against delivery speed and model update frequency. That tradeoff becomes sharper for frontier models, frequent fine-tunes, and agentic systems that change behaviour after every prompt or tool call.

There is no universal standard for how much red teaming is enough. Best practice is evolving, but current guidance suggests risk-based coverage: larger testing budgets for models exposed to sensitive data, public users, regulated workflows, or autonomous actions. A lightweight internal chatbot may need prompt-injection and leakage testing, while a model that can write code, trigger workflows, or query internal systems needs control validation, tool-abuse simulation, and approval-path testing.

Edge cases also matter. Retrieval-augmented systems can leak source content even when the base model is safe. Fine-tuned models can inherit unsafe patterns from small but influential datasets. Multi-agent pipelines can produce failures that no single prompt reproduces because the weakness emerges only after several models pass messages and state between them. In those environments, red teaming should include memory, retrieval, and orchestration layers, not just the model itself. This is where State of Secrets in AppSec matters operationally: once secrets or sensitive patterns enter the system, model testing must prove they cannot be reproduced under stress.

Security teams should treat red teaming as a repeatable pre-launch gate, then rerun it after major prompt, policy, retrieval, or tool changes because the attack surface changes with the system around the model.

Related resources from NHI Mgmt Group

• How should security teams discover AI usage in source code before deployment?
• How should security teams red team AI agents that use tools and memory?
• How should security teams test AI guardrails before deployment?
• How should security teams validate AI model files before deployment?