AI Red Teaming: Testing LLMs and Generative AI Features

What "AI red teaming" actually means

The term has accumulated multiple meanings. In an enterprise context — the one most teams shipping LLM features care about — AI red teaming is a structured adversarial assessment of an LLM-backed feature: prompt resilience, retrieval-boundary integrity, tool-use safety, output handling, and the application layer that wraps the model. It is not an evaluation of the underlying model's capabilities or alignment in isolation. That is the model provider's responsibility.

The engagement looks similar to a focused web/API pentest in shape, with adversarial corpora and structured probing of the model surface added on top. The deliverable is a pentest report plus a reusable corpus your team can use as regression tests in CI.

How AI red teaming differs from traditional pentest

Dimension	Traditional pentest	AI red teaming
Primary surface	Application and API endpoints, business logic, infrastructure	Model input/output, retrieval, tool calls, plus the surrounding service
Adversarial input	Crafted requests, fuzzed parameters, role manipulation	Adversarial prompts, poisoned documents, tool-parameter tampering, plus traditional inputs
Output evaluation	Response parsing, status codes, evidence in headers/body	Semantic evaluation of model output: did it leak, did it act, did it produce harmful content
Reproducibility	Deterministic given the same input	Probabilistic; same prompt may yield different outputs across runs
Deliverable	Findings with PoC, severity, remediation	Same plus a reusable adversarial corpus for CI regression

What an engagement covers

Eight categories, mapped roughly to the OWASP Top 10 for LLM Applications:

1. Direct prompt injection

Adversarial corpora across instruction override, role assumption, system-prompt elicitation, output coercion, and policy circumvention.

2. Indirect prompt injection

Crafted documents, web pages, and emails that the feature retrieves or processes, designed to override system behavior. Tested through the realistic ingestion path.

3. Sensitive information disclosure

Elicitation of system prompts, training-data fragments, other users' context, embedding-store content. Both via direct query and via downstream rendering paths.

4. Improper output handling

Whether downstream consumers (HTML rendering, code execution, SQL queries, API calls) treat model output as untrusted. XSS, code injection, and data corruption via the model.

5. Excessive agency

Tool-use safety. What can the model be coerced into calling, with what parameters, in what chains. Whether consequential actions require user confirmation.

6. Vector and embedding weaknesses

Retrieval-boundary correctness across tenants. Source poisoning resilience. Embedding-inversion concerns where embeddings are stored without the same protections as source content.

7. Misinformation and harmful content

Whether the feature can be coerced into producing harmful or policy-violating content, and whether output filters catch it. This is more about brand and regulatory risk than security in the traditional sense.

8. Unbounded consumption

Cost amplification, denial of service via expensive prompts, abuse-resistance gaps in feature throttling.

How to scope an engagement

Three inputs determine scope:

• The feature shape. Chat, document RAG, agent with tools, embeddings-only feature. Each has different surface emphasis.
• The integration depth. Hosted model with simple prompts vs custom-fine-tuned model with multi-step agent loops. More integration depth means more surface.
• The blast radius. What can the model cause? Read-only output, internal tool calls, customer-facing actions, money movement. The blast radius determines testing depth on agency-related categories.

What good looks like in deliverable

Five deliverables in a serious AI red team engagement:

1. Adversarial corpus. The exact prompts, documents, and tool inputs used during testing — labeled by category and outcome. Reusable in your CI as regression tests.
2. OWASP LLM Top 10 mapping. Each finding tied to the category it represents.
3. Evidence per finding. Reproduction prompts, observed outputs, and the threshold at which the issue manifests.
4. Mitigation playbook. For each class of finding, paste-ready mitigations: prompt structure, tool gating, retrieval limits, output validation.
5. Retest evidence. Post-fix testing of the affected items.

Cadence and integration with CI

For an LLM feature in production, two cadence patterns work:

• Annual deep engagement plus quarterly refresh. A full red team annually, with a smaller scoped engagement quarterly to cover prompt or tool changes.
• Pre-launch engagement plus continuous corpus testing in CI. A pre-launch deep engagement, followed by ongoing CI runs of the adversarial corpus on every prompt or model change.

The adversarial corpus from the engagement is the multiplier — it lets your team detect regressions in prompt or tool changes without re-running a full engagement every time.