AI Red Teaming Tools: Nine Options Grouped by What They Actually Test
Nine AI red teaming tools grouped by target: model-level probing, application and agent chains, and continuous regression testing. Each entry covers what it generates, what it measures, and the failure class it misses. The closing sections cover the gap between a finding and a control, and why a scanner report on its own changes nothing about production.
Tools in this category split on a question their marketing rarely makes explicit: are they testing the model, or are they testing the system you deployed. A tool that fires 5,000 jailbreak variants at an endpoint and reports a refusal rate has measured the model. It has said nothing about whether your agent can be steered into emailing a customer list through two authorised tools.
I want to group nine options by that distinction, note what each one misses, and then cover the part that decides whether any of this was worth the budget: the distance between a finding and a control.
The three groups
A mature programme runs one from each group. Most teams buy one from Group A and assume they are covered.
Group A: model-level probing
1. Garak
NVIDIA's open-source LLM vulnerability scanner, and the closest thing the category has to a standard starting point. It ships a large probe library covering prompt injection, jailbreaks, toxicity, data leakage, and encoding attacks, runs against most provider APIs and local models, and produces a per-probe pass rate.
Free, scriptable, and reasonable in CI. It targets the model endpoint, so nothing about your retrieval layer or tool permissions is in scope.
2. PyRIT
Microsoft's Python Risk Identification Toolkit, built by its AI Red Team from internal engagement work. Its distinguishing feature is multi-turn orchestration: an attacker model drives a conversation against a target model across turns, with scorers deciding whether an objective was met.
That multi-turn design makes it the most useful Group A entry for goal-drift testing, and it needs more engineering effort to operate than a scanner.
3. Promptfoo
Positioned as evaluation with red teaming attached, which suits teams already running prompt evals. It generates adversarial test cases from a description of your application, runs them as part of the same suite as quality evals, and fails a build on regressions.
The developer workflow integration is its strength. Depth against a determined adversary is not.
4. Giskard
Open-source scanning across LLM and traditional ML, covering hallucination, harmfulness, prompt injection, and input perturbation resilience, with a report format built for handing to a governance function rather than an engineer.
Useful when the output needs to satisfy a risk committee. Lighter on agent-specific attack classes.
Group B: application and agent testing
5. Burp Suite with AI extensions
The reason this belongs on the list is that a deployed AI feature is a web application. Session handling, authorization on the endpoints around the model call, IDOR on conversation identifiers, and rate limiting are all conventional web problems, and they are consistently where the cheapest real findings come from.
Bringing a web application tester to an AI feature routinely finds issues that no LLM-specific scanner looks for.
6. Agent-focused test rigs
A growing set of purpose-built rigs drive multi-turn campaigns against deployed agents, planting instructions in retrievable content, chaining tool calls, and testing memory persistence across sessions. Coverage varies and the category is young.
The capability to insist on: driving the real agent with its real tools and real permissions, rather than a mock. A rig testing a mocked tool set finds nothing about composition. Method detail in AI agent red teaming.
7. Custom test rigs
Most of the findings that matter in a mature programme come from something a person wrote for that specific system. The reason is structural: composition attacks depend on which tools this agent holds and what data those tools reach, and no vendor ships knowledge of your CRM schema.
A custom rig is typically a few hundred lines driving the agent's API with scripted sequences and assertions about end state.
Group C: continuous regression
8. Your own chains as a test suite
The highest-value artifact from any exercise is the set of chains that worked, kept and re-run on every prompt edit, model upgrade, tool addition, and framework bump. Each of those events can reopen a closed finding, and without a suite nobody discovers that until the next engagement.
This requires no purchase and it is the step most often skipped.
9. Commercial red teaming services
Several vendors sell continuous adversarial testing as a managed service, combining automated campaigns with human analysts. The economics work when you lack an in-house offensive capability and need something defensible for an auditor or an enterprise customer.
Scope these carefully. A service testing only the model endpoint delivers a Group A result at a Group B price.
What every tool in the list misses
Two gaps sit outside the whole category.
Identity is the first of them. Almost every tool tests as a single anonymous caller. The interesting question in a deployed system is whether the agent's effective authority tracks the requesting user's entitlements or a static service credential shared by everyone. In most deployments it tracks the credential, which means an authorised low-privilege user can reach data their role never granted. Testing as one caller cannot surface that.
Production traffic is the second. Every tool here tests a system you pointed it at, in a window you chose. None of them see the request an employee makes on a Tuesday afternoon with a customer contract in the prompt. Cloud Radix found 77% of employees using unauthorized AI admit to pasting sensitive business data into unsanctioned models (Cloud Radix). No scanner covers that path, because the path never existed during the test.
From finding to control
A report is not a control, and this is where programmes quietly fail. The common response to a successful injection chain is a prompt-hardening sprint, which is the weakest available fix. Stanford Trustworthy AI research and the AIUC-1 Consortium briefing found model-level refusal behaviours degraded significantly under targeted fine-tuning and adversarial pressure (Help Net Security). Hardening a prompt against the attack you just saw leaves the class intact.
Durable fixes share a property: the control sits outside the component being attacked and evaluates every call rather than sampling. A policy layer on the request path classifies prompt content, checks the requesting identity against the target model and data class, and returns a deterministic decision. That control is unaffected by the phrasing that defeated the model's refusal training.
The honest framing for a security review is that testing and enforcement answer different questions. Testing tells you which attacks work today against the version you tested. Enforcement decides what happens on every call in production, including the attacks nobody has invented yet.
DeepInspect
This is the gap DeepInspect closes on the enforcement side. DeepInspect is a stateless proxy at the AI request boundary that evaluates identity, data classification, model authorization, and organizational policy for every request, then returns a pass or block decision before traffic reaches the model.
Where a red team tool tells you a chain worked in a test window, DeepInspect applies a deterministic rule on every call in production, keyed on the requesting identity rather than on a shared service credential. Enforcement runs inline with overhead under 50 ms in internal testing against inference times of 500 ms to several seconds. Every decision commits a structured record with the identity, role, data classification, policy version, and outcome, which is also the record that lets you reconstruct an exercise afterwards and prove to an auditor that a control operated on a given date. DeepInspect performs no red teaming itself, and it governs HTTP traffic to model endpoints rather than tool execution inside an agent process. Book a technical deep dive at deepinspect.ai.
Frequently asked questions
- Which AI red teaming tool should I start with?
Garak, because it is free, broad, and takes an afternoon to run against an endpoint. Treat the result as a baseline on model behaviour rather than as coverage of your application. The second step is a custom rig against your deployed agent, which is where findings specific to your system come from.
- Are open-source tools good enough?
For model-level probing they are. Garak and PyRIT are built by teams doing this work at scale, and neither is a weaker version of a commercial scanner. Commercial value shows up in managed campaigns, human analysts, and reports formatted for auditors rather than in the probes themselves.
- How often should red teaming run?
Regression suites belong in CI on every change to prompts, tools, models, or framework versions. Discovery exercises fit a quarterly cadence, or on any architectural change such as adding a tool with write access. Annual testing is common and it lags the rate at which agent systems actually change.
- Does a passing scan mean the deployment is secure?
It means the probes in that tool's library did not succeed against the configuration tested, in that window. Composition attacks, identity confusion, and anything involving your specific tools and data are generally outside a scanner's scope. A clean scan is a floor rather than a conclusion.
- What is the relationship between red teaming and runtime enforcement?
Testing identifies failure classes; enforcement applies controls to every request whether or not a test found the specific variant. Findings should convert into policy rather than into prompt edits, and the enforcement record then demonstrates to an auditor that the control was live. Related reading in AI red team methodology.