# AI Red Team Training for Federal Security Contractors

By Charles Givre · 2026-08-05

> AI red team training for federal security contractors: what to cover, MITRE ATLAS mapping, air-gapped lab delivery, and how contracting teams buy it.

If your company builds, integrates, or operates AI for a federal customer, someone is eventually going to ask how you tested it. Not whether the model is accurate. Whether it can be made to do something it should not.

Most defense contractors have people who can answer that question about a web application and nobody who can answer it about an LLM. The gap is narrower than it looks.

## What Is Actually Driving the Requirement

Be precise about this, because vendors are not. No regulation currently says federal contractors must red team their AI systems. What exists is a stack of guidance and contract language moving in one direction.

[NIST's AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) (AI 100-1) treats adversarial testing as part of the Measure function. [NIST SP 800-218A](https://csrc.nist.gov/pubs/sp/800/218/a/final) extends the Secure Software Development Framework to generative AI, which matters because SSDF attestation already appears in federal software procurement. For companies handling CUI, the DFARS 252.204-7012 and CMMC obligations do not mention AI specifically, but an AI system that leaks CUI through model output is still a CUI spill.

The practical trigger is usually simpler than any of that. A program office asks for evidence of adversarial testing, and the contractor discovers nobody on staff has done it.

## The Curriculum That Matters

Skip anything organized around "AI awareness." The training that produces capability is organized around attack classes and mapped to a taxonomy the government already recognizes: [MITRE ATLAS](https://atlas.mitre.org/).

- **Prompt injection, direct and indirect** (ATLAS [AML.T0051](https://atlas.mitre.org/techniques/AML.T0051), OWASP LLM01). The direct case is a chat box. The interesting case for contractors is indirect: an instruction hidden in a document that a retrieval pipeline pulls in. If your system ingests contractor-submitted files, vendor documentation, or open-source intelligence, that is your attack surface.
- **Jailbreaking** (ATLAS [AML.T0054](https://atlas.mitre.org/techniques/AML.T0054)). Pushing a model past its safety training and documenting which technique worked, so the finding survives the next model update.
- **Model evasion** (ATLAS [AML.T0015](https://atlas.mitre.org/techniques/AML.T0015)). Crafting input that a deployed classifier misses. For anyone shipping ML-based detection to a government customer, this is the finding that changes deployment decisions.
- **Training data poisoning.** Where the training or fine-tuning data comes from, who can influence it, and what a supply chain review of a model actually looks at.
- **Exfiltration through inference.** Whether the system surfaces its system prompt, its training data, or the contents of connected data sources under sustained multi-turn pressure.

The architecture half is what separates useful training from a payload list. Attacks land in the plumbing: how retrieved context gets concatenated into a prompt, what a tool-calling agent is permitted to invoke, whether the vector store enforces the same access controls as the system of record. A course that never leaves the chat interface teaches half the problem.

## Tooling, and Why Scanners Are Not Enough

A serious syllabus names its tools. [garak](https://github.com/NVIDIA/garak) from NVIDIA runs probe suites across known injection and jailbreak payloads and gives you a baseline. [PyRIT](https://github.com/Azure/PyRIT) orchestrates multi-turn attacks where the payload assembles across a conversation. [promptfoo](https://github.com/promptfoo/promptfoo) turns confirmed findings into a regression suite so a prompt change does not silently reopen a hole.

In our labs students run the scanner first, then script their own attacks for what it missed. The habit matters more than the tool. An assessment that is only a garak report is the AI equivalent of submitting raw Nessus output as a penetration test, and a government reviewer will read it that way.

## Delivery Constraints Are the Hard Part

For most contractors the curriculum is the easy question. Logistics is where training procurement dies.

Cleared personnel often cannot take a hosted course, cannot install a lab client on a government-furnished laptop, and cannot reach a commercial model API from the network where the work happens. The answer is a self-contained lab: everything inside a guest image, local models instead of hosted ones.

When we ran a three-day AI course for a military cyber unit, the entire engagement executed with no external network. Labs ran in the [Centaur VM](https://github.com/gtkcyber/centaur), adversarial exercises hit local models served by [Ollama](https://ollama.com/), and no request left the unit's environment. Twenty-five operators spent day two attacking models and day three building agents. The logistics detail that bites people: model weights have to be staged before the environment goes behind the air gap, because `ollama pull` needs a network.

Instructor eligibility is the other constraint worth asking about early. GTK Cyber instructors carry federal program experience including DARPA and ARPA-H AI support, and one holds an active TS clearance with full-scope polygraph. Ask any vendor this question before you scope a delivery, not after.

## Where This Training Does Not Help

Two honest limits.

It will not make your company an AI assurance shop. A two-day course produces staff who can run a competent assessment against an LLM application and write it up defensibly. It does not produce people who can evaluate a frontier model's alignment properties or do original adversarial ML research. If your contract scope is model evaluation at that depth, you are hiring, not training.

It also will not satisfy a compliance requirement by itself. Training builds the capability; the deliverable a program office wants is a tested system and a report. Contractors who buy training expecting an artifact are disappointed. Buy it because you need people who can produce the artifact.

GTK Cyber's [AI Red-Teaming course](/courses/ai-red-teaming) runs as a closed-cohort engagement for federal and defense teams, on-site or virtual, with the offline lab environment described above. Registration data, NAICS codes, and past performance are on the [government page](/government) if you are building a sources sought response or a subcontractor package.

## FAQ

### Is AI red teaming required for federal contractors?

There is no single mandate that says 'thou shalt red team thy model.' What exists is a set of converging pressures: NIST's AI Risk Management Framework (AI 100-1) names red teaming as a measurement practice, NIST SP 800-218A extends secure development practices to generative AI, and AI assurance language is appearing in solicitations and data rights clauses. If your company delivers or operates an AI-enabled capability for a federal customer, expect to be asked how you tested it adversarially. Treat it as a capability to build now rather than a box to check later.

### What should AI red team training for a defense contractor cover?

Four things, in order. First, the attack classes against LLM and ML systems: direct and indirect prompt injection, jailbreaking, training data poisoning, model evasion, and data exfiltration through inference. Second, the architecture those attacks target: embedding pipelines, vector stores, tool-calling agents, and the plumbing that passes retrieved context to the model. Third, tooling (garak, PyRIT, promptfoo) plus the Python skills to write attacks the scanners do not cover. Fourth, reporting: findings mapped to MITRE ATLAS and the OWASP Top 10 for LLM Applications in a form that survives a government security review.

### Can AI red team training be delivered on an air-gapped or classified network?

Yes, and for many defense customers it has to be. The requirement is that the lab environment carry its own tooling, datasets, and models so nothing calls a commercial API mid-exercise. GTK Cyber runs labs in the Centaur VM with local models served by Ollama, which means the exercises complete with no outbound network. Plan the model weights into your logistics: they have to be pulled and staged before the environment goes behind the air gap.

### Do students need a machine learning background for AI red team training?

No. The prerequisites that matter are security testing methodology and working Python. Contractor security staff who have run application assessments already have the adversarial instinct and the reporting discipline. What they lack is the AI-specific model: how a transformer resolves competing instructions, why safety training is a statistical guardrail rather than an access control, and how to evaluate model behavior across many trials instead of one. That layer is teachable in days, not months.

### How do federal contractors purchase AI red team training?

Three common paths. Direct commercial purchase for internal workforce development, which is the fastest route and often the right answer for a single team. A closed-cohort contract priced per student per day, which is how most agency and contractor deliveries are structured. Or as a training line item inside a larger prime or subcontract deliverable, which is how it usually shows up when the AI capability itself is contract scope. GTK Cyber, LLC holds an active SAM.gov registration (UEI FL7WRSQMR4S8, CAGE 8CWY8) and is a small business eligible for Total Small Business and Partial Small Business set-asides as well as full and open competition.


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Canonical: https://gtkcyber.com/blog/ai-red-team-training-federal-contractors/