AI Security / AI Red Teaming

AI Security · Offensive Practice

AI Red Teaming

The Enterprise Practice Guide for 2026

AI red teaming is how you find the vulnerabilities in your AI systems before someone with worse intentions does. It is not an extension of penetration testing; it is a related discipline with its own attack categories, tools, and skill set. This guide covers the six attack categories every program should cover, the open-source and commercial tools that work, the vendor shortlist for outside engagements, and the 10-step checklist for standing up an AI red team this quarter.

30-SECOND EXECUTIVE TAKEAWAY

OWASP LLM Top 10 is the baseline, not the program. Real red teaming goes deeper into your specific architecture, tool permissions, and data classes.
Indirect prompt injection is where most findings come from. Test every pathway untrusted text reaches the model, including RAG, tool outputs, and web browsing.
Re-test after every model upgrade. Foundation model behavior changes between versions; mitigations that worked yesterday may not today.

Why AI red teaming is its own thing

A penetration test against a web application targets a known set of bug classes (injection, auth bypass, broken access control, etc.) using known techniques. A bug either exists or it doesn\u2019t. AI red teaming targets a probabilistic system where the same prompt produces different outputs across runs, where success is defined by what the model will do rather than what was coded, and where new attack categories emerge between every model release.

The result is a discipline with overlap into traditional offensive security but its own learning curve. The most successful AI red teams in 2026 pair a senior penetration tester with someone who has spent serious time prompt-engineering production LLMs. The pair finds things neither would alone. Hiring only the security background or only the ML background tends to produce reports that miss half the surface.

The cheapest time to do this work is before launch. The most expensive time is after an incident. Most organizations are shipping faster than their security and risk programs can review, which is why the OWASP LLM Top 10, the AI risk register, and a structured red team need to be in place before the architecture decisions are locked in.

ATTACK CATEGORIES

The six attack categories every program covers

Mapped roughly to the OWASP LLM Top 10 with practical groupings. Coverage of all six is the floor, not the ceiling. Gaps in any category leave the corresponding production risk untested.

Prompt injection (direct & indirect)

Override the developer’s system prompt or hijack agent behavior. The OWASP LLM #1 risk and the most-used attack in real engagements.

Tools: PyRIT, garak, promptfoo, Lakera Guard

Jailbreaks

Bypass the model’s safety alignment to produce content it was trained to refuse. Useful for testing both safety and brand-risk exposure.

Tools: garak, JailbreakBench, custom prompt libraries

Sensitive information disclosure

Extract training data, system prompts, or context-window contents. Includes RAG context-leak attacks and PII memorization probes.

Tools: PyRIT extraction modules, custom probes per app

Tool / function-call abuse

Get an agent to invoke tools in unauthorized ways. The dominant attack against agentic AI; blast radius scales with tool permissions.

Tools: Custom test harnesses; the OWASP Agent Security Initiative checklists

Model extraction & inversion

Reverse-engineer the model’s weights or extract memorized training data. Mostly relevant for proprietary fine-tuned models.

Tools: Foolbox, art (IBM Adversarial Robustness Toolbox)

Data poisoning probes

Test whether the system properly handles adversarial content in retrieval pipelines, fine-tuning data, or feedback loops.

Tools: Custom test corpora, garak data-poisoning modules

VENDOR SHORTLIST

When to bring in outside help

Five vendors with material enterprise traction in 2026, with the use case each one fits best. This is an opinionated shortlist, not a directory. Other vendors exist; these are the ones that show up most often in CAIO and CISO conversations.

Vendor	What they\u2019re known for	Best fit
Lakera	Strongest open-source presence (Lakera Guard OSS); platform combines red teaming with runtime defense	Organizations starting from zero who want one vendor for both testing and runtime guardrails
HiddenLayer	Strong on model-layer attacks (extraction, supply chain); good for organizations training custom models	ML teams with proprietary models or complex fine-tuning pipelines
Robust Intelligence	Enterprise-grade testing and continuous validation; deep enterprise sales motion and governance integrations	Large regulated enterprises that want vendor-led assessments and audit-grade reports
Mindgard	Continuous testing platform with strong CI/CD integration; UK-headquartered	Organizations integrating AI testing into existing application security pipelines
Giskard / promptfoo	Open-source testing frameworks with growing adoption; lighter-weight than commercial platforms	Smaller teams or proof-of-concept programs before committing to a commercial platform

Want the wider market of AI security tools beyond red teaming? See the AI security stack guide.

DOWNLOADABLE CHECKLIST

The 10-step AI red team checklist

Use this as the standing checklist for every LLM-facing application, plus a quarterly cross-system review. Adapt the depth to the system\u2019s risk tier, but cover every step at least at a screening level.

Define what "in scope" means: which models, which surfaces, which user roles, which data
Document each system’s blast radius before testing (what data, what actions, what reputation risk)
Run the OWASP LLM Top 10 as the baseline checklist for every LLM-facing app
Test indirect prompt injection through every untrusted-content pathway: RAG, tool output, web browsing, email reading
For agentic systems, enumerate every tool the agent can call and test for unauthorized invocation paths
Probe sensitive-data extraction with realistic adversary queries, not toy ones
Document every finding with reproduction steps, severity, exploitability, and recommended mitigation
Feed findings into the AI risk register with a named owner and a remediation deadline
Re-test after every model upgrade; new model versions invalidate prior assumptions
Run a tabletop incident response exercise at least once a year using a real red-team finding

Subscribers to the CTAIO newsletter get the executive PDF pack with the full red team SOP.

AI Red Teaming: Frequently Asked Questions

What is AI red teaming?

AI red teaming is the structured practice of attacking your own AI systems before adversaries do. It combines traditional offensive security techniques (think penetration testing) with AI-specific attack patterns: prompt injection, jailbreaks, training data poisoning, model extraction, and adversarial inputs. The goal is to find vulnerabilities in production AI systems while you can still fix them, and to build organizational muscle for the post-incident response when one inevitably gets through.

How is AI red teaming different from regular penetration testing?

A penetration test targets a known surface (network, app, identity) with a known set of techniques. AI red teaming targets a probabilistic surface where the same attack works one day and fails the next, and where success often means getting the model to do something the developer didn’t imagine rather than exploiting a coded bug. The skill set overlap is partial. The best AI red teams pair an experienced penetration tester with someone who has hands-on experience prompt-engineering production LLMs.

When should an organization start AI red teaming?

Before any LLM feature reaches authenticated users or external content. The cheapest moment is during the pre-launch security review; the second-cheapest is the first quarter post-launch; the most expensive is after an incident. The OWASP LLM Top 10 makes a reasonable starting checklist, but the real value comes from reading every claim of "we’ve mitigated this" with the assumption that the mitigation has a bypass and your job is to find it.

Should we build an internal AI red team or hire a vendor?

Both, sequenced. Start with an internal capability so the team learns your specific attack surface, then bring in a vendor periodically for structured external assessments. Pure vendor reliance leaves no internal muscle for the day-to-day. Pure internal reliance misses adversary techniques the team hasn’t seen. The mix that works for most enterprises in 2026: a 1–2 person internal AI red team, supplemented by a vendor engagement once or twice a year.

What tools do AI red teams use?

Open-source: PyRIT (Microsoft), garak (NVIDIA), promptfoo, Giskard. These give you scriptable adversarial test suites and report generation. Commercial: Lakera, Robust Intelligence, HiddenLayer, Mindgard. The commercial offerings add managed adversarial libraries, continuous testing, and integration with the security stack. Tools matter less than the testing program; a good red teamer with promptfoo and a notebook will outperform a poor one with the most expensive platform on the market.

How much does AI red teaming cost?

A vendor-led structured assessment of a single LLM application runs $40K–150K in 2026, depending on scope and depth. A continuous testing platform license runs $50K–250K/year. An internal capability is roughly the loaded cost of one to two senior security engineers with AI specialization, which in the US is $300K–600K/year fully loaded. Most enterprises with significant AI investment are running $500K–1.5M/year in total AI red teaming spend, and the ones that aren’t are usually the ones that haven’t had the incident yet.

How does AI red teaming connect to AI risk management?

Red team findings feed the AI risk register. Each finding becomes a documented risk with an owner, a remediation plan, and a residual-risk score after fix. The cadence of red teaming is set by the AI risk management program; high-risk systems need continuous testing, medium-risk get quarterly reviews, low-risk get annual. See our AI risk management guide for the full structure.

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Thomas Prommer Technology Executive — CTO/CIO/CTAIO

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