The best AI red teaming tools in 2026 test LLM applications and AI agents the way attackers do: prompt injection, jailbreaks, data exfiltration and agent hijacking, run automatically and scored. Demand is measurable: Promptfoo, an open-source LLM red teaming tool, is used by teams at more than 25% of the Fortune 500 and OpenAI acquired it in March 2026.
This guide compares 41 AI red teaming tools across enterprise, open-source and research tiers, including Mindgard, Garak, PyRIT, Promptfoo, DeepTeam and Microsoft's AI Red Teaming Agent, with pricing, attack coverage and the use case each one fits.
Use the interactive table below to filter by tier, search by name or sort by category. Every tool on the list also gets its own section further down, and the 2026 additions (Promptfoo, DeepTeam, Giskard, Lakera Red, HiddenLayer AutoRT, Microsoft AI Red Teaming Agent and FuzzyAI) have their own group.
AI RED TEAMING TOOLS · UPDATED SEPTEMBER 2026
Filter by tier, search by name or strength, or click a column header to sort. Every tool has its own section below the table.
| Tool ↕ | Tier ↕ | Best for | Key strength | Open source ↕ | Pricing ↕ |
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Sources: vendor documentation and public repositories, September 2026; Promptfoo, March 2026; Confident AI, 2026; Palo Alto Networks, July 2025; Dealroom, August 2026. Compiled for mindgard.ai.
AI red teaming tools are specialized frameworks and platforms that simulate adversarial attacks, such as prompt injection, jailbreaking, data exfiltration, tool misuse and safety bypasses, against large language models, RAG pipelines and AI agents, then score how the system responded. Security teams and researchers use AI red teaming tools to find failure modes before an attacker does and to re-test after every model, prompt or tool change.
In other words, rather than waiting for bad behavior to occur with an AI system in production, you can use AI red teaming tools to proactively discover the failure modes, vulnerabilities and unsafe behaviors that are most likely to be exploited by adversaries. Organizations use the findings from these red teaming tools to improve their overall security posture, before those issues become a real problem for AI systems.
It's easy to confuse AI red teaming with other forms of model evaluation or standard software penetration testing, but the difference is that AI red teaming tools are built to mimic the sophisticated, creative ways adversaries actually try to subvert AI systems. Most tools in this space share a few common capabilities:
There are nuances to each AI red teaming tool, but they all aim to do the same thing: allow your team to see how your AI actually behaves when put under pressure by someone trying to break it.
Already have safety mechanisms built into your models and applications? An AI red teaming tool can help you determine if those safety features actually work, or if there’s a way for a motivated attacker to bypass them.
These terms are often used interchangeably. They mean different things, attack different layers, and help solve different problems. However, many folks confuse them, and that’s where your security program begins to fall short.
AI security scanners have broad reach, while AI pentesting can tell you where you're vulnerable and may need to shore up your security. AI red teaming can demonstrate how an attacker could leverage vulnerabilities for maximum impact.
To choose an AI red teaming tool, match it to your threat model, your AI architecture (standalone LLM, RAG pipeline or autonomous agent), your compliance evidence needs and your budget. AI red teaming tools add rigor and repeatability to a process that is otherwise ad hoc, but not every tool fits every use case.
Six questions settle most decisions before you buy or install an AI red teaming platform.
Run through this AI red teaming tool evaluation grid as a framework for comparing AI red teaming tools based on maturity and fit.
The AI Red Teaming Tool Matrix tells you what a mature tool looks like. It does not tell you which tier you need, and that depends on what you are testing, who runs the tests and how often the system changes.
Answer five questions below and the selector names the tier that fits, three tools from this guide to shortlist and the reasoning behind the pick, so you walk into a vendor call or a GitHub repo already knowing what to ask for.
AI RED TEAMING TOOL SELECTOR · 2026
Five questions. The result names the tier that fits and three tools from this guide to shortlist, with the reasoning.
How this works: each answer adds points to an open-source, hybrid or enterprise recommendation. Agents and MCP, continuous change, audit evidence and enterprise budget push toward a platform; engineering capacity and zero license spend push toward open source. Tool facts: Promptfoo, March 2026; Confident AI, 2026; Microsoft Learn; Dealroom, August 2026. Scores are computed from your answers, not from external data. Built for mindgard.ai.
Enterprise AI red teaming tools cost a custom annual contract; open-source AI red teaming tools cost nothing to license. The three price bands: open-source frameworks (Garak, PyRIT, Promptfoo, DeepTeam, Giskard) are free, and your cost is engineering time plus API spend on attacker and judge models.
Vendor community tiers sit in the middle: Lakera Red's Community tier includes 10,000 API requests a month at no charge and Microsoft's AI Red Teaming Agent runs inside Azure AI Foundry with no separate license. Enterprise platforms (Mindgard, HiddenLayer, Prisma AIRS and Confident AI's red teaming tier) quote annual contracts on request; Confident AI publishes its evals plans from free to $2,000 a month and prices red teaming as an enterprise add-on. Budget for upkeep of the attack library, not the license: a scanner run once at launch is cheap, and it leaves every later model update untested.
AI red teaming tools are quickly becoming a standard part of responsible AI development. There are numerous tools to choose from so evaluate a few before deciding. Here are some of the best AI red teaming tools to help you get started.
We’ve identified examples of the best tools to red team your AI systems for various use cases, including:
Before we get into the details of each platform, a few notes on our methodology here. While there are lots of useful prompt testing tools out there (and we’ll likely see more of those as time goes on), this list is biased towards tools that help with real-world AI red teaming exercises.
The platforms below should help teams test realistic attack scenarios like prompt injection, data leak retrieval, logical failure cases, jailbreak tests, etc.
We first compiled a list of tools that fulfilled as many of our criteria as possible. We prioritized tools that allowed teams to run continuous, automated tests and that could be plugged into CI/CD pipelines.
We also favored tools that can scale (handle large volumes of tests), have access to API/access to plugins for different LLM providers, and can fit into your existing security workflow/applications.
For the September 2026 refresh we added the seven tools that Google's AI Overview for "ai red teaming tools" and the top-ranking roundups from Promptfoo, Confident AI and Synack name, re-verified every repository link and retired the unmaintained LLMFuzzer as the fuzzing pick in favor of FuzzyAI.
Microsoft's AI Red Team, which had tested more than 100 generative AI products by October 2024, set the limit of automation plainly:
"While automation tools are useful for creating prompts, orchestrating cyberattacks, and scoring responses, red teaming can't be automated entirely."
- Blake Bullwinkel and Ram Shankar Siva Kumar, Microsoft AI Red Team. Microsoft Security Blog, January 2025
That is the case for pairing an automated platform with a services engagement or an in-house team, rather than treating either as complete on its own.
Governments and standards bodies have made red teaming AI systems a central part of compliance. With evolving AI safety standards, there's a clear expectation that organizations will demonstrate that their systems have been tested against real-world adversarial attack simulations.
Your AI red teaming efforts can help validate your compliance by documenting that your models were tested for potential risks, misuse, and failure modes prior to deployment. Red teaming your AI models maps perfectly to these new regulations and governance initiatives focused on transparency, accountability, and managing risk throughout the AI lifecycle.
Standards and regulations focused on governing AI typically mandate extensive risk identification, adversarial validation, and testing exercises that AI red teaming makes possible.
These frameworks provide structure, but AI red teaming is an essential step to validate the effectiveness of your AI controls.
AI red teaming tools map to the OWASP Top 10 for LLM Applications by the risk each attack module targets, and to the NIST AI RMF by the function the evidence supports (Map, Measure, Manage and Govern). Prompt injection (LLM01), sensitive information disclosure (LLM02) and excessive agency (LLM06) are the three OWASP risks with the widest tool coverage on this list; data and model poisoning (LLM04) and supply chain (LLM03) need artifact scanners rather than prompt-based tools.
The table below shows which AI red teaming tools from this guide produce evidence for each risk, so an auditor can trace a NIST AI RMF Measure activity to a specific test run. For a walkthrough of the testing methodology behind the OWASP list, see our guide to the OWASP AI testing guide.
Jumpstart your search by checking out these examples of some of the best tools for red teaming AI systems.
Open source AI red teaming solutions can be great if you value flexibility and don’t need to make a large investment upfront. However, open source tools require a lot of time and resources from your team. Because of this, open source often proves to be a deceptive bargain when you consider total cost of ownership (TCO).
With open source AI red teaming tools, “free” comes as the cost of time spent building out your stack. You’ll need to maintain your own infrastructure, run attack generation yourself, and format your own standard reports. This isn’t an issue if you have staff with bandwidth and aren’t under pressure to demonstrate compliance, but it will almost certainly lead to longer response times.
Enterprise AI red teaming solutions solve these problems by offering managed services. These tools have a higher price point but save you time on overhead and provide you with managed infrastructure, built-in attack generation, and standardized reporting. Enterprise solutions also offer support and continuous product improvements. When you run with an open source tool, you’re on your own. Vendors that offer enterprise plans include SLAs and product roadmaps. These are important if your red teaming workflows have any association with production risk or regulatory compliance.
When it comes to features, enterprise AI red teaming offerings generally deliver more value. Open source tools will generally only support one or two types of tests. Enterprise AI red teaming tools enable you to run full-spectrum tests against your application. That means coverage that spans beyond injection attacks to agent-based attacks and everything in between, including integrations with LangChain, Hugging Face, and more. If your organization values quick turnarounds, scalability, and auditable processes, an enterprise solution is the way to go.
Four open-source AI red teaming tools cover most practitioner use cases in 2026: Promptfoo, Garak, PyRIT and DeepTeam. Promptfoo is an MIT-licensed CLI and library that runs red team plugins against any LLM provider; more than 350,000 developers have used it and OpenAI acquired the company in March 2026.
Garak, NVIDIA's LLM vulnerability scanner, probes around 100 attack vectors with up to 20,000 prompts per run. PyRIT, Microsoft's Python Risk Identification Tool, orchestrates multi-turn adaptive attacks and is the engine behind the Azure AI Foundry AI Red Teaming Agent. DeepTeam, from Confident AI, ships 50+ vulnerability types and 20+ attack methods under an Apache 2.0 license.
Pick Garak for a fast baseline scan, Promptfoo for CI/CD gating, PyRIT for custom multi-turn campaigns and DeepTeam if you already run DeepEval. None of the four ships managed infrastructure, a maintained attack library with an SLA or audit-ready reporting, which is where enterprise platforms such as Mindgard earn their fee.
Mindgard is an automated AI red teaming platform that runs continuous adversarial testing against LLM applications, AI agents and multimodal models, from development through production. Its attack library comes from Mindgard's own research team, which has disclosed more than 150 vulnerabilities in production AI products, including a zero-day code execution flaw in the Cursor IDE, and the company raised a $30 million Series A led by Album VC in August 2026 to scale that work.
Continuous, automated testing that re-runs on every model, prompt or tool change is what puts Mindgard at the top of this list. For hands-on assistance, Mindgard also offers AI red teaming services and artifact scanning.
James Brear, Mindgard's CEO, framed the design goal at the Series A announcement:
"We don't just automate attacks. We operationalize expertise, turning the knowledge of leading AI security researchers into capabilities every enterprise needs to secure their AI."
James Brear, CEO, Mindgard. Dealroom News, August 2026
That is the practical difference between an attack library that a vendor's researchers maintain and one your team has to keep current on its own.
Schedule your Mindgard demo now to automatically build a more resilient cyber infrastructure.
Key features:
Garak is an open-source LLM vulnerability scanner maintained by NVIDIA. It can be used by red teams to scan for common vulnerabilities in AI models such as data leakage and misinformation. Google's AI Overview describes it as the "Nmap for LLMs".
It also automatically generates attacks against AI models to test how well they perform in different threat scenarios.
Key features:
The Python Risk Identification Toolkit is part of Microsoft's AI Red Team exercise toolkit. As the name implies, PyRIT is a Python toolkit for assessing AI security, and it can be used to stress test machine learning models or manage adversarial inputs.
It is a well-maintained framework: Microsoft uses it to test its generative AI systems, such as Copilot, and it powers the AI Red Teaming Agent in Azure AI Foundry.
Key features:
IBM's open-source toolkit for testing machine learning models is called AIF360. It allows you to detect vulnerabilities and mitigate discrimination and bias in machine learning models.
This red teaming tool can be used in any industry where fairness and equity are critical, such as finance or health care. Outside of testing for bias, AIF360 comes with dataset metrics, bias testing models, and bias-mitigation algorithms.
Key features:
Foolbox attempts to deceive neural networks by generating adversarial examples. This lets programmers know where their model falls short so they can build better defenses in the future.
Foolbox includes a library of decision-based attacks that can attack state-of-the-art neural networks.
Key features:
Datasets power AI and ML models. Visualize your data using Meerkat's open-source interactive datasets. Meerkat is a data tool rather than an attack tool; it earns its place here because slice-based evaluation is how teams find the inputs a red team should target.
Written in Python, this library can assist with preprocessing unstructured data for use in ML models. Easily preprocess images, text, audio, and more forms of unstructured data to enhance performance and security.
Key features:
Protect your NLP data and models with Granica. Scan cloud data lake files for PII and confidential information that can be exploited maliciously and receive recommendations to lock them down. Granica makes data AI-ready at scale.
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Agentic AI systems present a different risk surface because they act instead of answer: agents call APIs, query databases, invoke workflows and use third-party tools through protocols such as the Model Context Protocol (MCP) to finish a goal. NIST's January 2025 agent hijacking evaluation found that novel attacks hijacked agents in 81% of attempts, against an 11% baseline, and that success climbed from 57% to 80% when the attacker got 25 tries per task.
That requires a different approach to testing. Instead of asking how a model will respond to a single prompt, you need to think about how it will convert goals into actions over time.
Misuse of tools and prompt injection are the two primary dangers. Tool misuse involves either using an inappropriate tool or supplying unsafe inputs to a tool (sending personal information to an API, for instance). Prompt injection deceives agents into performing actions they weren't instructed to do through the use of directives or vague language. These risks are amplified when decisions need to be made throughout a complex, multi-step workflow.
NIST's evaluation team described the underlying problem in plain terms:
"AI agent hijacking is the latest incarnation of an age-old computer security problem that arises when a system lacks a clear separation between trusted internal instructions and untrusted external data."
- NIST AI Safety Institute (now the Center for AI Standards and Innovation), Technical Blog: Strengthening AI Agent Hijacking Evaluations, January 2025
That separation is exactly what an agent red teaming tool has to attack: every tool result, retrieved document and MCP response is untrusted external data until proven otherwise.
AI red teaming tools that support agentic AI and MCP testing on this list are Mindgard, PyRIT, Promptfoo, DeepTeam and Microsoft's AI Red Teaming Agent. Each runs multi-turn attacks, injects payloads through tool outputs and documents (indirect prompt injection) and traces the tool calls an agent makes. Single-shot scanners such as Garak and Plexiglass find model-level weaknesses but miss tool misuse.
If your agents connect to MCP servers, put each server's tools in scope: a poisoned tool description is an injection vector the model reads on every turn. Tools that integrate with frameworks like LangChain and model hubs like Hugging Face can mimic tool use and trace decisions across a conversation.
The above red teaming tools are great examples of some of the best software solutions available with various features and capabilities, but there are plenty of reputable solutions on the market to consider.
Check out this alphabetical list of some of the top red teaming tools, complete with a list of their standout features.
Malicious actors want access to AI models and their data. This AI red teaming tool by Borealis AI, which is backed by the Royal Bank of Canada, specializes in adversarial robustness.
AdverTorch generates adversarial attacks and teaches AI how to defend against these examples through training scripts.
Key features:
The Adversarial Robustness Toolbox (ART) is a toolkit red teams can use to assess machine learning security. Created by IBM, ART assists businesses in benchmarking their models' threat-mitigation preparedness.
The toolkit also contains an open-source library specifically for adversarial testing. This provides red teams with out-of-the-box tools to help create attacks and test models.
Key features:
Automate attacks against your LLM with BrokenHill, a program that creates jailbreak attacks. It focuses on greedy coordinate gradient (GCG) attacks and includes many of the algorithms found in nanoGCG.
Key features:
BurpGPT is a valuable tool you can use to test the security of your web applications. BurpGPT integrates with OpenAI's LLMs to automatically scan for vulnerabilities and analyze traffic. As a paid AI red teaming tool, BurpGPT can rapidly identify higher level security risks that other scanners miss.
Key features:
AI tools perform best when they have thorough training on adversarial attacks. CleverHans is a helpful red teaming tool that does just that.
It's an open source Python library that allows your team to use attack examples, defenses, and benchmarking. Google Brain originally supported it, but it's now maintained by the University of Toronto.
Key features:
Counterfit is a command-line interface (CLI) that automatically assesses machine learning security. Maintained by Microsoft's AI Security team, Counterfit simulates attacks to identify vulnerabilities.
While it works with open-source models, this AI red teaming software tool can even work with proprietary models.
Key features:
Dreadnode’s Crucible red teaming software helps developers practice and learn about common AI and ML vulnerabilities. It also helps red teams test these models in hostile environments and pinpoint issues that need addressing.
Key features:
DeepTeam is Confident AI's open-source red teaming framework for LLMs and AI agents, licensed under Apache 2.0. It ships 50+ vulnerability types and 20+ attack methods, including multi-turn and agent-specific attacks.
It pairs naturally with DeepEval, the same company's evaluation library, so teams can run safety and security tests in the same pipeline as quality evals.
Key features:
FuzzyAI is CyberArk's open-source fuzzing framework for LLMs. It runs jailbreak and prompt injection attack strategies against hosted and local models and reports which ones landed.
Promptfoo's 2025 comparison of open-source AI red teaming tools lists FuzzyAI in its top five, which is why it replaces the unmaintained LLMFuzzer as the fuzzing pick on this list.
Key features:
Galah is a web honeypot framework that works with any LLM including OpenAI, GoogleAI, Anthropic, and others. Since it's backed by LLMs, this honeypot can dynamically generate responses to any HTTP request made to it.
This honeypot will also cache responses so you won't pay the API for duplicate requests.
Key features:
Ever wanted to quickly figure out what a function and its variables do? Gepetto allows you to accelerate the reverse engineering process by automatically annotating functions and renaming their variables.
However, this Python plugin uses GPT models to generate explanations and variables, so take its suggestions with a grain of salt.
Key features:
Giskard is an open-source evaluation and testing library for LLM agents and RAG systems. Its scan runs probes for prompt injection, sensitive data leakage, hallucination, harmful content and bias, then generates a report of detected vulnerabilities.
Google's AI Overview for this query lists Giskard alongside Garak and PyRIT as an open-source framework, and its RAG-specific tests fill a gap that model-only scanners leave.
Key features:
GPT-WPRE is another red teaming tool perfect for reverse engineering entire programs, and using Ghidra’s code decompilation tool allows you to summarize a whole binary.
While this tool has limitations, many developers find its natural language summaries helpful for understanding the context behind different functions.
Key features:
Guardrails adds safeguards to LLMs that bolster them against the latest threats. This Python framework runs application guards to detect, quantify, and mitigate risks. It also generates structured data from LLMs.
Key features:
HiddenLayer's Automated Red Teaming for AI (AutoRT) tests models and pipelines without agents or instrumentation; the vendor describes it as model-agnostic and agentless.
HiddenLayer's broader platform covers the AI supply chain and runtime, so AutoRT is the entry point for teams that want red teaming inside a wider AI security program. Pricing is custom.
Key features:
Reverse engineer models with IATelligence’s Python script. This tool uses OpenAI to understand scripts and look for potential vulnerabilities, making it invaluable for quickly understanding API vulnerabilities in existing malware.
Key features:
Inspect is a red teaming tool for evaluating LLMs. Created by the UK AI Security Institute, it includes features for everything from benchmark evaluations to scalable assessments.
Key features:
LLMs produce malicious outputs when they get jailbroken. Jailbreak-evaluation measures how susceptible an AI model is to jailbreak attacks.
Key features:
Lakera Red is an adversarial testing platform for enterprise LLM applications and chatbots that covers safety, security and responsible AI assessments. Its Community tier includes 10,000 API requests a month at no charge, with custom Enterprise plans above that.
Lakera is best known for Lakera Guard, its runtime firewall, so Red suits teams that want testing and runtime protection from one vendor; our list of the best AI security tools for LLM and GenAI covers that runtime layer.
Key features:
Fuzzing is the process of providing invalid, unexpected, or random data to a computer program. LLMFuzzer is the first open-source fuzzing framework created exclusively for conducting AI fuzzing tests.
Note: LLMFuzzer is no longer actively maintained as of 2024. However, internal development teams can still use this free tool to assess LLM APIs. For an actively maintained fuzzer, see FuzzyAI above.
Key features:
LM Evaluation Harness tests model performance across 60+ standard benchmarks with hundreds of subtasks, including natural language processing, reasoning, and safety evaluations.
While it's designed for academics and researchers, the LM Evaluation Harness is also helpful for comparing your model's performance against other datasets.
Key features:
Mend AI Red Teaming identifies risks unique to your conversational AI with prebuilt, customizable tests. It verifies your AI powered application’s security against threats like prompt injection, context leakage, data exfiltration, biases, and hallucinations that can lead to unintended consequences.
Key features:
Microsoft's AI Red Teaming Agent, part of Azure AI Foundry, is an automated red teaming agent built on PyRIT that generates adversarial probes, runs them against a target model or application and scores the results as an attack success rate.
It is the clearest example of the "AI red teaming agent" pattern searchers ask about: an LLM-driven attacker that plans and adapts rather than replaying a fixed prompt list. It runs inside Azure AI Foundry with no separate license.
Key features:
Detect and mitigate vulnerabilities in your LLM with Plexiglass. This simple red teaming tool has a CLI that quickly tests LLMs against adversarial attacks.
Plexiglass gives complete visibility into how well LLMs fend off these attacks and benchmarks their performance for bias and toxicity.
Key features:
Organizations using Microsoft 365 will appreciate this AI red teaming tool from Zenity, as Power Pwn is designed specifically for Azure-based cloud services, including Copilot.
Key features:
Promptfoo is an open-source CLI and library for LLM evals and red teaming, released under the MIT license. More than 350,000 developers have used it, 130,000 are active each month and teams at more than 25% of the Fortune 500 rely on it. OpenAI announced its acquisition of Promptfoo on March 9, 2026 and said the open-source project will continue.
Its red team mode generates attacks from plugins that map to OWASP Top 10 for LLM Applications categories, runs them against any provider or a custom HTTP target and produces a report you can fail a CI build on.
Key features:
Promptfoo's founders explained why the category grew so fast when they announced the OpenAI deal:
"adversarial tests for security, safety, and other behavioral risks were the biggest blockers to shipping AI, especially at large enterprises."
Ian Webster, Co-founder and CEO, Promptfoo. Promptfoo blog, March 2026
Red teaming stopped being a research exercise once it became the gate between a prototype and production.
Meta developed the popular Purple Llama tool, which provides benchmark evaluations for LLMs. This set of AI red teaming tools includes multiple applications for building safe, ethical AI models and prevents malicious prompts.
Key features:
SecML is developed and maintained by the University of Cagliari in Italy and cybersecurity company Pluribus One. This open-source Python library performs security evaluations for machine learning algorithms.
It supports many algorithms, including neural networks, and can even wrap models and attacks from other frameworks.
Key features:
Tenable developed a set of scripts for Ghidra, the NSA's open-source reverse engineering suite, that analyze and annotate decompiled code.
Its extract.py Python script extracts decompiled functions, while the g3po.py script uses OpenAI's LLM to explain decompiled functions. In practice, these tools help automate the reverse engineering process.
Key features:
Red teams train with tools like TextAttack, a Python framework for testing natural language processing (NLP) models. This platform improves security and function by training both your NLP models and red team.
It also gives users access to a library for text attacks, allowing red teams to test NLPs against the latest text-based threats.
Key features:
ThreatModeler’s platform specializes in threat modeling for commercial purposes. It isn’t open-source, but this paid solution specifically supports threat modeling and red teaming for AI models.
You can rely on this tool to simulate attacks and evaluate your AI’s response.
Key features:
Prompt injections, jailbreaks and other exploits can have disastrous effects on both your AI/ML model and organization. Vigil is a security scanner designed to evaluate prompts and responses for these issues.
The library is written in Python and includes several scan modules, along with the ability to use custom detections via YARA signatures. However, please note that this red teaming tool is still in development, so use it only for experimental and research purposes.
Key features:
Robust Intelligence offers an end-to-end security and safety platform for AI. It tests models during development and continues monitoring them in production. Databricks acquired Robust Intelligence in 2024, and the product now ships inside Databricks.
It runs algorithmic red teaming, feeding many test inputs into a model, hunting for weaknesses like prompt injection, data poisoning, privacy leaks, or other safety and security issues. Then it recommends guardrails tailored to that model.
Key features:
Protect AI offers pre-deployment and continuous testing via Recon. Recon simulates adversarial attacks against generative AI pipelines. It helps catch vulnerabilities like prompt injection, data leakage, or model misuse before they hit production. Palo Alto Networks completed its acquisition of Protect AI on July 22, 2025 and folded it into Prisma AIRS, which Google's AI Overview for this query lists as an enterprise AI red teaming platform mapped to OWASP Top 10 and NIST AI RMF.
Protect AI integrates with existing security workflows. The platform supports multiple model formats and deployment environments.
Key features:
If you're looking for a comprehensive AI security platform, Mindgard is a leading solution that offers extensive model coverage for LLMs as well as audio, image, and multi-modal models.
Mindgard helps organizations detect and remediate AI vulnerabilities that only emerge at run time. It integrates into CI/CD pipelines and all stages of the software development lifecycle (SDLC), enabling teams to identify risks that static code analysis and manual testing miss.
Mindgard is designed not just for point-in-time red teaming but as part of a posture management approach. It supports AI Security Posture Management (AI-SPM) by continuously monitoring model behavior, tracking red teaming findings over time, supporting policy enforcement, and integrating into CI/CD pipelines. This enables organizations to not only detect issues but also ensure they stay remediated, measured, and resilient.
By reducing testing times from months to minutes, Mindgard provides AI security coverage with accurate, actionable insights. Book a demo today to learn how Mindgard can help you ship AI you can defend.
AI red teaming tools are used to attack AI systems on purpose: they run prompt injection, jailbreak, data extraction and tool misuse attacks against LLM applications, RAG pipelines and AI agents, then score how the system responded. Security teams use the results to fix failure modes before release, to prove testing for NIST AI RMF, ISO/IEC 42001 and EU AI Act obligations and to re-test after every model or prompt change.
Free AI red teaming tools include Garak, PyRIT, Promptfoo, DeepTeam, Giskard, FuzzyAI, ART, Counterfit and TextAttack, all open source. Lakera Red offers a free Community tier with 10,000 API requests a month, and Microsoft's AI Red Teaming Agent runs inside Azure AI Foundry with no separate license. Free covers the software only; attacker and judge model API calls plus engineering time are the real spend.
AI red teaming tools work on RAG pipelines and AI agents when they support multi-turn attacks and indirect prompt injection through documents and tool outputs. Promptfoo, PyRIT, DeepTeam, Giskard, Mindgard and Microsoft's AI Red Teaming Agent do. Single-shot scanners such as Garak, Plexiglass and Vigil test a model's responses to prompts and never exercise tool calls, so they miss excessive agency and task hijacking in agents.
An AI red teaming agent is an autonomous attacker: an LLM-driven system that plans, generates, adapts and scores attacks against a target without a human writing each prompt. Microsoft's AI Red Teaming Agent in Azure AI Foundry, built on PyRIT, is the reference example. Conventional AI red teaming tools replay fixed probe libraries; an agent adapts its strategy across turns based on the target's responses.
Yes, when they are used against systems you own or have written permission to test. Ethical hackers and internal security teams use these tools to fix vulnerabilities before real attackers can exploit them. Testing a third party's AI system without authorization is still unauthorized access, and the tools still need to comply with data protection and regulatory requirements.
AI red teaming tools simulate an adaptive adversary across the whole AI system (model, prompts, integrations, agents and users) to find unknown failure modes. AI penetration testing tools validate specific, known vulnerabilities inside a scoped endpoint such as a model API and deliver proof of exploit. Red teaming is broader and more adversarial; pentesting is narrower and more reproducible.
Mindgard helps organizations discover, assess and defend their AI systems. Spun out of more than a decade of AI security research at Lancaster University in the UK and headquartered in Boston and London, Mindgard operationalizes the expertise of AI researchers and offensive security practitioners through a Security Platform that performs Shadow AI discovery, AI red teaming, and run-time AI protection to assess and mitigate risk across models, agents, and applications.
The expert-level checklist for operationalizing NIST AI RMF, ISO/IEC 42001 and the EU AI Act. 190+ interactive items and a board-ready maturity scorecard. Built for CISOs, AI governance leads and ML engineering teams.
