9 Top AI Tools for Red Teaming in 2026

A successful red team has never been measured by how many attacks it can launch. The real measure is whether it can discover something defenders did not already know. AI changes both sides of that equation.

At a Glance: 9 AI Red Teaming Tools

Tool	Primary Red Teaming Target	Approach
Novee	Applications, APIs, LLM applications, and agentic systems	Autonomous offensive AI and adaptive exploitation
XBOW	Web applications and network-accessible targets	Autonomous AI penetration testing
Mindgard	AI models and AI applications	Automated AI security testing
HiddenLayer	Models and AI systems	AI security assessment and adversarial testing
Promptfoo	LLM applications and agents	Configurable open-source adversarial testing
Microsoft PyRIT	Generative AI systems	Extensible red-team automation framework
NVIDIA garak	LLMs	Automated vulnerability probing
DeepTeam	LLM applications and agents	Open-source AI red teaming
Pentera	Enterprise IT environments	Automated security validation and adversary emulation

Start With the Target, Not the Tool

Before comparing platforms, define the system the red team is expected to challenge.

A conventional application

The objective may be to discover authentication weaknesses, injection vulnerabilities, access-control failures, business-logic flaws, privilege escalation, or exploitable chains across APIs and services.

An LLM

Testing shifts toward behaviors such as jailbreak susceptibility, harmful outputs, information disclosure, prompt manipulation, and other model-level weaknesses.

An AI application

The attack surface becomes larger than the model. Retrieval pipelines, application logic, APIs, databases, system prompts, external content, and integrations can influence what the application does.

An AI agent

Now the system can act. An agent may call tools, read files, query enterprise applications, use credentials, communicate with other agents, or execute workflows. Red teaming needs to determine whether an attacker can manipulate those capabilities into producing an unauthorized outcome.

Enterprise infrastructure

Here the red team may be validating whether security controls can detect and stop realistic attack techniques across endpoints, networks, identities, and other enterprise systems.

These are related disciplines, but they are not interchangeable. The nine tools below occupy different positions across this offensive-testing landscape.

9 AI Red Teaming Tools to Evaluate in 2026

1. Novee

Novee is an AI-powered offensive security platform designed to autonomously discover and validate exploitable weaknesses across modern applications.

Its approach goes beyond executing a predefined collection of security tests. Novee uses proprietary offensive AI and specialized offensive-security orchestration to interact with live targets, formulate hypotheses based on observed behavior, adapt when an attack path fails, and validate vulnerabilities through reproducible exploitation.

This makes the platform relevant to both conventional application security and the newer attack surface introduced by generative and agentic AI.

Novee’s AI Red Teaming capabilities target deployed LLM applications rather than limiting assessment to the underlying model. Testing can examine prompt injection, jailbreaks, sensitive data exfiltration, agent manipulation, and weaknesses arising from interactions between AI and connected systems.

This distinction matters because an AI application can use a well-protected model and still expose a vulnerable retrieval pipeline, insecure tool integration, excessive agent permissions, or exploitable business logic. Likewise, an agent may resist a direct malicious prompt but follow an injected instruction contained inside a retrieved document.

Novee tests this broader application environment. Its autonomous agents can explore targets, dynamically generate attacks according to observed behavior, adapt tactics, and chain weaknesses into exploitable paths. Findings are validated with evidence and reproducible steps rather than relying solely on theoretical vulnerability identification.

Key capabilities

• Autonomous AI penetration testing
• AI and LLM application red teaming
• Prompt injection and jailbreak testing
• Agent and tool manipulation testing
• Web, API, and mobile application testing
• Adaptive attack generation
• Vulnerability chaining and exploit validation
• Reproducible proof of exploitation
• Remediation guidance and retesting
• CI/CD security testing through Novee Pipeline

2. XBOW

XBOW uses autonomous AI agents to perform offensive security testing against applications and network-accessible targets. Its approach represents a shift from AI assisting penetration testers toward AI performing substantial portions of the offensive process itself.

The platform’s autonomous agents can explore targets, investigate potential vulnerabilities, attempt exploitation, and produce evidence supporting confirmed findings. This allows security teams to expand testing across applications that would otherwise require significant manual penetration-testing capacity.

That distinction is important because impactful application vulnerabilities are not always identifiable through software versions or known CVEs.

Key capabilities

• Autonomous penetration testing
• AI-driven target exploration
• Vulnerability discovery
• Exploit validation
• Web application testing
• Network-accessible target assessment
• Automated offensive workflows
• Evidence-backed security findings

3. Mindgard

Mindgard specializes in adversarial security testing for artificial intelligence systems. Instead of focusing primarily on conventional web vulnerabilities, its platform is designed to challenge models and AI applications with attacks specific to machine learning and generative AI.

The question is no longer simply whether an application contains a technical software flaw. Security teams also need to understand how an AI system behaves when an attacker deliberately manipulates its inputs, attempts to bypass safeguards, or exploits weaknesses in the surrounding AI architecture.

Key capabilities

• Automated AI red teaming
• Model security assessment
• Generative AI application testing
• Adversarial attack simulation
• Continuous AI security testing
• AI vulnerability discovery
• Security assessment across evolving AI deployments

4. HiddenLayer

HiddenLayer approaches red teaming as one component of a broader AI security lifecycle. Its platform provides capabilities for assessing models and AI applications against adversarial techniques while also supporting AI discovery, model scanning, supply-chain security, runtime protection, and attack simulation.

This creates a useful relationship between offensive testing and defensive controls. A red-team exercise may identify a way to manipulate an AI application. The organization can then use that finding to strengthen controls around the system, monitor for similar behavior in production, and subsequently retest the application.

Key capabilities

• AI security assessments
• Automated adversarial testing
• Model security testing
• AI application security testing
• AI attack simulation
• Model and supply-chain scanning
• Runtime AI protection
• AI asset discovery

5. Promptfoo

Promptfoo provides an open-source framework for testing LLM applications and agents against adversarial behavior.

Its role is different from an autonomous penetration-testing platform. Rather than independently exploring a conventional application and discovering attack paths, Promptfoo allows development and security teams to configure repeatable adversarial evaluations around the AI systems they build.

Teams can generate attack cases, execute them against target applications, and evaluate whether the resulting behavior violates expected security requirements.

Key capabilities

• Open-source LLM red teaming
• Automated adversarial test generation
• Prompt injection testing
• Jailbreak testing
• Agent security evaluation
• Custom policies and assertions
• Repeatable AI security evaluations
• CI/CD-friendly testing workflows

6. Microsoft PyRIT

Microsoft’s Python Risk Identification Toolkit for generative AI, or PyRIT, provides building blocks for security teams that want to construct their own generative AI red-team campaigns. It is better understood as a red-team framework than as a managed penetration-testing platform.

PyRIT allows practitioners to define targets, orchestrate adversarial interactions, transform prompts, score responses, and preserve the results of testing. These components can be combined into customized workflows suited to the AI system being evaluated.

Key capabilities

• Automated generative AI red teaming
• Multi-turn adversarial attacks
• Prompt transformations
• Target orchestration
• Response scoring
• Custom attack strategies
• Test result storage and analysis
• Extensible AI security research

7. NVIDIA garak

NVIDIA garak applies a vulnerability-scanning concept to large language models. Instead of searching conventional software for known vulnerabilities, the open-source tool probes models with adversarial inputs and evaluates their responses for undesirable or potentially exploitable behavior.

Its testing architecture is based around probes, generators, and detectors. These represent adversarial techniques. Generators connect garak to the model being evaluated. Detectors analyze the resulting responses for indications that the test succeeded.

Key capabilities

• Automated LLM vulnerability probing
• Adversarial model testing
• Prompt injection testing
• Jailbreak and safeguard evaluation
• Information leakage detection
• Extensible probes and detectors
• Support for multiple model interfaces
• Repeatable model security assessments
• Comparative testing across LLMs

8. DeepTeam

DeepTeam is an open-source red-teaming framework built for LLM applications and AI agents. It provides developers and security teams with a way to generate adversarial attacks, execute them against AI systems, and evaluate whether those systems remain within expected security boundaries.

Its focus on agents is particularly relevant as AI applications gain the ability to act rather than simply generate text. An agent may access data, call tools, interact with APIs, or trigger business processes. Red teaming therefore needs to investigate whether adversarial input can manipulate those capabilities.

Key capabilities

• LLM red teaming
• Agent security testing
• Automated attack generation
• Multiple adversarial attack methods
• AI vulnerability evaluation
• Custom testing workflows
• Repeatable security assessments
• Integration into AI development processes

9. Pentera

Pentera addresses a different layer of red teaming: the enterprise environment surrounding applications and AI systems. Its automated security validation platform emulates attacker techniques across enterprise infrastructure to determine whether existing weaknesses and defensive gaps can contribute to viable attack paths.

This differs substantially from model-level testing. A jailbreak assessment asks whether an attacker can manipulate an LLM. Pentera’s type of testing asks whether an attacker can move through enterprise systems, exploit weaknesses, evade or overcome controls, and reach valuable assets.

Key capabilities

• Automated security validation
• Adversary emulation
• Attack-path validation
• Enterprise infrastructure testing
• Security control validation
• Evidence-based exposure assessment
• Continuous security testing
• Remediation validation

Match the Red Teaming Tool to the Attack Surface

Putting every AI red teaming product into a single feature checklist can create a misleading comparison. The tools operate at different layers, and organizations may need more than one approach.

If You Need to Test…	Main Question
Live web applications and APIs	Can an attacker discover and exploit a real application weakness?
Complete AI applications	Can an adversary manipulate the deployed AI system?
LLM behavior	How does the model or application respond to adversarial interaction?
AI agents	Can adversarial input manipulate tools, permissions, or agent behavior?
Enterprise infrastructure	Can realistic attacker techniques overcome existing defenses?

That breadth becomes increasingly important as the boundary between a conventional application and an AI application disappears. A customer portal can contain ordinary authentication, APIs, databases, and business logic while also incorporating an AI assistant connected to retrieval systems and external tools. 

Testing only the model misses traditional vulnerabilities. Testing only the application code misses semantic attacks against the AI layer. Modern red teams increasingly need to understand both.

Frequently Asked Questions

Can AI red teaming be automated?

Yes. Automation ranges from repeatable adversarial test suites to autonomous offensive agents. garak can systematically probe models, while PyRIT can orchestrate generative AI attack campaigns. Novee uses autonomous offensive AI to explore live applications, adapt attacks based on observed responses, validate exploitation, and support continuous retesting.

Should security teams red team the LLM or the entire AI application?

Both provide useful information, but application-level testing is essential for understanding production risk. A deployed AI system can include retrieval, APIs, tools, permissions, system prompts, databases, and business logic. Exploitable attack paths may emerge from interactions between these components even when the underlying model behaves securely during isolated testing.

What makes an AI red team finding actionable?

An actionable finding should establish more than unusual model behavior. Security teams need to understand the affected system, the attack conditions, potential impact, and how the issue can be reproduced. Validated exploitability and reproducible attack paths help teams distinguish security-relevant weaknesses from interesting but lower-impact AI behavior.

Does AI red teaming replace human red teams?

AI can expand testing frequency, coverage, and speed, but human expertise remains important for threat modeling, creative attack design, interpretation of business impact, and sophisticated adversarial campaigns. Autonomous testing can handle substantial repeatable offensive work while allowing human red teams to concentrate on complex targets and strategic security questions.

Why is continuous AI red teaming important?

AI applications change frequently through model updates, new integrations, prompt modifications, retrieval sources, tools, permissions, and code deployments. Any of these changes can alter the attack surface. Continuous red teaming helps organizations reassess security as systems evolve instead of relying only on periodic assessments that represent a previous version of the application.