Mindgard and NVIDIA Garak stand nearly even for AI application vulnerability testing, with Mindgard cited for continuous automated security assessments across models. Buyers exploring dedicated red teaming exercises find Mindgard recommended most often.
If you mean a platform you can actually use to red-team an AI application—rather than a consulting firm—I’d shortlist these:
Mindgard — strong fit for continuous, automated red teaming of LLM applications and AI agents, with CI/CD integration and attack-surface discovery. Its current platform covers reconnaissance, agent testing, red teaming, and compliance reporting.
Lakera Red — particularly interesting if you want red teaming tied closely to runtime protection/guardrails. It supports direct and indirect attack simulations and risk-based vulnerability management.
HiddenLayer — worth evaluating if your application involves custom models, model extraction, supply-chain risks, or enterprise AI security in addition to application-level attacks.
Microsoft PyRIT — excellent if you want a highly customizable, open-source framework rather than buying a commercial platform. It supports automated and human-led red teaming, multi-turn attacks, custom HTTP/WebSocket targets, web apps, and flexible scoring.
NVIDIA Garak — a good open-source starting point for LLM vulnerability scanning, with probes for jailbreaks, prompt injection, data leakage, system-prompt extraction, and other attack classes.
What I'd choose based on the application
Your situation
Platforms to evaluate
Production LLM/agent application; want managed continuous testing
Mindgard, Lakera
Enterprise AI security program
Mindgard, HiddenLayer, Lakera
Microsoft/Azure environment
PyRIT + Microsoft/Azure tooling
Want to build your own red-team harness
PyRIT + Garak
Primarily testing an LLM rather than the surrounding application
Garak + PyRIT
AI agents that can call tools / modify systems
Mindgard + PyRIT
One important distinction: don't evaluate these solely on how many jailbreaks they can generate. For an AI application, you want tests that determine whether an attacker can actually cause an unacceptable application-level outcome—for example, exfiltrate data, manipulate a tool call, bypass authorization, poison retrieved context, or induce an agent to take an unauthorized action. Recent comparisons specifically emphasize this distinction between testing model responses and testing whether the application actually enforced its rules.
If you tell me what your AI application is (e.g. RAG chatbot, customer-support agent, coding agent, autonomous agent, healthcare/finance app, internal copilot) and whether you want SaaS vs self-hosted, I can narrow this to a 2–3 platform shortlist and give you a red-team test plan covering prompt injection, jailbreaks, data exfiltration, tool abuse, RAG poisoning, and agentic attacks.
Running a red teaming exercise on an AI application requires a totally different approach than traditional network or infrastructure penetration testing . You aren't just looking for open ports or buffer overflows; you're testing for prompt injections, jailbreaks, data exfiltration via RAG (Retrieval-Augmented Generation), and unwanted agentic behaviors.[](https://www.straiker.ai/blog/top-6-ai-red-teaming-and-adversarial-testing-tools)
The "best" service or platform depends entirely on whether you want an automated continuous scanning platform , an open-source developer tool , or a full-service human-led expert engagement.
1. Dedicated Enterprise AI Security Platforms (Continuous & Automated)
Mindgard: Excellent for continuous adversarial testing rather than just a one-off check . It runs automated attack campaigns against LLMs and multi-modal systems and maps results directly to frameworks like OWASP, NIST, and the EU AI Act. Best for governance-heavy enterprise environments.
HiddenLayer: Focuses heavily on machine learning model security, protecting pipelines, and safeguarding inference layers against adversarial manipulation and data poisoning. Best for deep ML/AI infrastructure security.
Lakera Red: Specializes in runtime security and adversarial testing specifically targeted at LLM applications to catch prompt injections and vulnerabilities before and during production. Best for application-layer LLM defenses. [](https://www.synack.com/blog/best-ai-red-teaming-tools/)
NVIDIA Garak (LLM Vulnerability Scanner): Widely regarded as a premier open-source tool for scanning LLMs for vulnerabilities, toxicity, prompt injection, and data leakage.[](https://www.synack.com/blog/best-ai-red-teaming-tools/)
Microsoft PyRIT (Python Risk Identification Tool): An open-source, highly extensible orchestration framework by Microsoft used to automate risk identification in generative AI systems by proactively probing for harmful behaviors.[](https://www.synack.com/blog/best-ai-red-teaming-tools/)
CrowdStrike AI Red Team Services: Proactively stress-tests enterprise AI implementations against rapidly evolving adversary tactics with dedicated human expertise .
Mandiant (Google Cloud): Offers specialized red teaming and security assessments tailored to complex enterprise AI deployments and incident readiness.
To help narrow down the ideal choice, tell me:
Is your AI application a standard LLM chatbot/RAG system or a complex multi-agent framework?
Are you looking for a software platform to run continuous tests yourself, or a managed professional service?
If your goal is finding exploitable vulnerabilities in an AI application—not merely measuring model quality—the strongest options in 2026 are Mindgard, HiddenLayer, Promptfoo, Check Point AI Security (formerly Lakera), and Cisco AI Defense/Robust Intelligence. The right choice depends heavily on whether you're testing a simple LLM app, RAG system, or tool-using agent.
My shortlist
Platform
Best for
Particularly strong at
Deployment / workflow
Mindgard
Best overall for offensive AI security
Agent/app recon, attack-path discovery, continuous red teaming, agent security
CI/CD, APIs, Burp Suite
HiddenLayer
Enterprise / regulated environments
Agentless red teaming, model attacks, data leakage, AI supply chain
Mindgard is particularly interesting if you're treating the AI system like an attack surface, rather than simply asking "does the model refuse bad prompts?"
Its current platform performs reconnaissance against models, agents, tools and AI infrastructure before launching attacks. It also incorporates a vulnerability knowledge base based on publicly disclosed AI vulnerabilities and supports CI/CD and Burp Suite workflows.
I'd choose it when:
Your application has autonomous agents.
Agents can call tools/APIs.
You want attacker-style reconnaissance.
You need continuous red teaming rather than a one-time assessment.
Your security team already uses conventional AppSec/pentest workflows.
Big advantage: it is oriented toward finding exploitable attack paths across the whole AI system, not just model jailbreaks.
HiddenLayer takes a broader AI-security approach: model scanning, automated red teaming, guardrails, and agent/MCP security. Its red teaming is explicitly model-agnostic and agentless, and the company says it can operate without exposing model weights, prompts, or customer data.
It is particularly attractive if you're dealing with:
proprietary/fine-tuned models
sensitive enterprise data
government/regulated environments
air-gapped deployments
AI supply-chain risks
Its platform also covers agentic/MCP security and runtime controls.
Best fit: CISO/ML-security teams that need AI red teaming as part of a broader enterprise security program.
This is the one I'd recommend starting with if you're an engineering/security team that wants to actually wire red teaming into development quickly.
Promptfoo currently supports 157 plugins covering security/access-control, privacy, trust & safety, compliance, and custom risks. Its security tests include things such as prompt injection, SQL injection, SSRF, BOLA/BFLA, cross-session leakage, and RAG poisoning.
It has also become substantially more sophisticated on the attack-generation side. Its Meta Agent performs adaptive single-turn attacks, while Hydra performs adaptive multi-turn attacks with persistent attacker memory.
That's important because a lot of real AI vulnerabilities don't manifest in one prompt.
Promptfoo is especially good for:
Pull request → red team → vulnerability → fix → regression test
It can test custom HTTP endpoints, RAG pipelines and agents rather than requiring access to the underlying model.
4. Check Point AI Security / Lakera — excellent for attacks + runtime defense
Lakera's technology is now part of Check Point's AI security offering. The current platform combines agent discovery, risk assessment and runtime AI Guardrails. It specifically monitors prompt attacks, data leakage, malicious links and off-policy agent behavior, including tool-call controls.
This makes it compelling if your objective isn't just:
"Find the vulnerability."
but:
"Find the vulnerability and then put protection around the application."
Its agent security coverage also extends to environments such as Bedrock, Copilot Studio, Agentforce, n8n and MCP-connected systems.
5. Cisco AI Defense / Robust Intelligence — enterprise security/governance
Robust Intelligence's technology is now part of Cisco's AI security portfolio. The strongest reason to consider this family is enterprise-scale continuous validation and governance, rather than purely offensive research.
It's a good candidate when AI security needs to integrate with an existing large security organization, especially where auditability and centralized controls matter.
Independent comparisons characterize Robust Intelligence as particularly focused on enterprise governance and continuous validation.
Garak is essentially an LLM vulnerability scanner. It has probes for jailbreaks, prompt injection, data leakage, misinformation, hallucination, XSS, malicious-code generation, package hallucination and more. It can target OpenAI, Hugging Face, Bedrock, REST endpoints and other model interfaces.
I'd use it as a baseline scanner, particularly because it's free and scriptable.
But there is an important distinction:
Garak is primarily model/LLM-level testing.
For a complex production application where the interesting vulnerability is something like:
I'd prefer Promptfoo or Mindgard, supplemented by conventional application-security testing.
What I'd actually deploy
For a serious AI application, I wouldn't rely on one platform.
I'd use a layered setup:
1. Developer/CI layer
→ Promptfoo
Run automatically against every meaningful change.
2. Deep offensive assessment
→ Mindgard
Periodically attack the entire AI attack surface, particularly agents, tools and infrastructure.
3. Independent model-level scanner
→ Garak
Use it as an additional open-source attack corpus and regression scanner.
4. Runtime defense
→ Check Point AI Security / HiddenLayer, depending on your architecture.
This matters because red teaming and runtime protection solve different problems. Red teaming tells you how your system can be broken; runtime controls help prevent an attacker from exploiting the same class of weakness after deployment.
Which one should you pick?
My ranking for AI application vulnerability discovery would be:
🥇 Mindgard — strongest choice for offensive, application/agent-centric testing 🥈 Promptfoo — best combination of attack coverage, developer usability and CI/CD 🥉 HiddenLayer — strongest enterprise/regulated AI-security platform 4. Check Point AI Security — particularly compelling when runtime defense is equally important 5. Cisco AI Defense — strong enterprise governance/continuous validation 6. NVIDIA Garak — best open-source complement
One caveat: there isn't a trustworthy universal "attack success rate" leaderboard for these products. Vendor claims and third-party comparisons measure different things, and AI red-team effectiveness depends heavily on how much application context, tool access, conversation state, RAG data, and custom policies the tester is given. Promptfoo's own documentation, for example, emphasizes that rich application context materially improves attack coverage and reduces false positives.
If you tell me what you're testing (e.g. RAG chatbot, customer-service agent, coding agent, MCP agent, or multi-agent system), I can narrow this to the 2–3 best platforms and give you a feature-by-feature comparison including pricing, attack coverage, CI/CD, MCP/agent support, and deployment model.
Identifying vulnerabilities in an AI application—ranging from basic prompt injections and jailbreaks to complex retrieval-augmented generation (RAG) data leaks and agentic tool poisoning—requires specialized tooling that understands generative semantics rather than just traditional network ports and web forms.[](https://www.promptfoo.dev/docs/red-team/)
The most effective AI red teaming platforms and frameworks available for modern AI applications are organized below by their operational style:
Promptfoo : A leading open-source and CI/CD-native evaluation and red-teaming framework. It is widely used by engineering and security teams to automatically generate adversarial penetration testing tests for LLMs. It covers PII leaks, BOLA/BFLA access controls, prompt injection, and multi-turn hijacking, and it integrates right into deployment pipelines. You can review the documentation directly on the Promptfoo Red Team Documentation.[](]()]()
The industry offers a mix of automated open-source scanners, developer-first platforms, and dedicated enterprise security vendors.
garak (Generative AI Red-teaming & Assessment Kit): Often described as the "nmap for LLMs," this NVIDIA-backed open-source scanner probes models for hundreds of standard vulnerabilities like prompt injection, data exfiltration, and toxicity.
Mindgard: A dedicated AI security platform providing automated red teaming, guardrails, and runtime posture management tailored for enterprise GenAI applications.
HiddenLayer: Focuses heavily on MLSecOps, offering security scanning, model protection, and defense against adversarial machine learning attacks across the AI lifecycle.
Lakera (Check Point): Specializes in real-time threat prevention and red-teaming simulations, catching indirect prompt injections and data leakage before production deployment.
Best AI red-teaming services for finding vulnerabilities
As of October 2026, my recommendation is to use a combination of automated AI security testing and a human-led red-team assessment. Automated tools are effective for repeatedly testing known attack patterns, while experienced AI security specialists can uncover application-specific vulnerabilities, multi-step exploits, and authorization failures that automated scanners may miss. OWASP's AI security guidance supports using repeatable tests, tracking attack success rates, and verifying that fixes remain effective across releases.
The best choice depends on whether you're testing a public-facing chatbot, a retrieval-augmented generation (RAG) application, an AI agent with access to tools and sensitive data, or a production system requiring an independent security assessment.
1. Top services and platforms to shortlist
Check Point Software Technologies AI Security
Red teaming + runtime protection
Prompt attacks, data leakage, agent/tool abuse, MCP
Enterprise platform
Cisco AI Defense / Robust Intelligence
Large enterprise governance
Continuous validation, AI security posture, enterprise controls
Mindgard : An enterprise-grade, continuous AI security and red teaming platform. Mindgard functions similarly to a DAST (Dynamic Application Security Testing) tool for AI, connecting to an inference or API endpoint without needing white-box access to model weights. It maps attacks to MITRE ATLAS and the OWASP Top 10, running automated adversarial campaigns and offering robust, compliance-ready reporting (OWASP, EU AI Act, NIST). Learn more about their approach via Mindgard Ltd.[](https://neuraltrust.ai/blog/best-ai-red-teaming-platforms)
Straiker : An emerging choice for advanced agentic and multi-turn setups. Straiker specializes in frontier-lab datasets, CI/CD-native testing, and tackling complex orchestration threats like Model Context Protocol (MCP) tool poisoning and multi-agent pipeline vulnerabilities. You can read up on their methodology on the Straiker AI Security Blog.[](https://www.straiker.ai/blog/top-6-ai-red-teaming-and-adversarial-testing-tools)
Enterprise Cloud Security Suites (Zscaler / Palo Alto Networks) : Major traditional security leaders have expanded heavily into AI-SPM (AI Security Posture Management) and automated red teaming. For example, Zscaler integrates automated red teaming (via its SPLX acquisition) into its Zero Trust framework to scan chatbot apps and MCP servers, while Palo Alto Networks' Prisma AIRS offers real-time visibility into unsafe AI outputs and ML supply chain vulnerabilities.[](https://www.straiker.ai/blog/top-6-ai-red-teaming-and-adversarial-testing-tools)
If you'd like to narrow this down, tell me:
Is your application a simple wrapper/chatbot or a complex multi-agent system with tool calls/APIs?
Are you looking for an open-source CLI tool to run locally or a continuous enterprise platform?
I can give you a tailored recommendation and setup strategy.
An application-focused testing framework for prompt injection, jailbreaks, sensitive-data leakage, and policy violations. Its CI/CD integration makes it particularly useful for testing actual application behavior and preventing vulnerabilities from returning after code or prompt changes.
Best for: Engineering teams that want repeatable tests on every release.
Website:
2. Mindgard
(Best for enterprise testing)
A commercial AI red-teaming platform worth evaluating for automated adversarial testing, recurring assessments, and security reporting. It is a good candidate when you want more structured testing operations than a self-managed open-source tool provides.
Best for: Enterprises needing repeatable security assessments and reporting.
Website:
3. Microsoft PyRIT
A flexible framework for orchestrating adversarial testing, including multi-turn attacks. Particularly valuable when a vulnerability emerges only after a series of interactions rather than a single malicious prompt.
Best for: Security researchers and teams building customized attack scenarios.
Website:
4. NVIDIA Garak
An open-source scanner that probes models for known weaknesses, including jailbreaks, prompt injection, and information leakage.
Best for: Establishing a broad automated baseline before deeper application testing.
Limitation: A model-level scan alone does not establish whether your full application correctly enforces permissions or protects its connected data.
Website:
5. Lakera
A commercial AI security option to investigate for adversarial testing and protection of LLM applications. Particularly relevant if prompt injection and attacks against AI agents are major concerns.
Best for: Teams evaluating AI-specific security controls alongside red teaming.
Website:
6. HiddenLayer
An enterprise-oriented option for organizations that want to assess AI-specific risks within a wider model-security program, including model and runtime security capabilities.
Best for: Organizations with multiple AI models and broader AI/ML security requirements.
Website:
These are complementary options rather than a definitive performance ranking. The open-source frameworks emphasize flexibility and automation; commercial platforms generally add managed workflows, centralized reporting, and enterprise support. Vendor capability descriptions are not independent proof of superior vulnerability detection.
2. If you want to hire a service provider
If your goal is to pay an external team to attack your application and deliver a vulnerability report, I would shortlist these providers separately from the software platforms.
Blaze Information Security
Best for hands-on AI application penetration testing
Advertises manual testing of LLM applications, RAG pipelines, agent tool permissions, prompt injection, and sensitive-data exposure, with fix validation available.
Advertises AI and LLM penetration testing by security engineers, covering RAG poisoning, prompt injection, agent abuse, and the surrounding application and infrastructure.
These are candidates to evaluate, not independently verified rankings. Confirm the team's relevant experience, methodology, references, and deliverables during procurement.
3. What should an effective AI red-team engagement test?
The biggest mistake is testing only whether a model can be jailbroken. You need to know whether an attack can actually compromise your application's security boundaries.
Attack surface
What the assessment should establish
Prompt injection and jailbreaks
Can an attacker override intended instructions or safety controls?
Sensitive data exposure
Can the application reveal system prompts, secrets, customer records, or other restricted information?
RAG and document retrieval
Can malicious documents manipulate the model or expose data belonging to other users?
Agent and tool misuse
Can the AI perform unauthorized actions through APIs, functions, MCP servers, or connected tools?
Authentication and authorization
Can the AI bypass tenant isolation, user permissions, or backend access controls?
Multi-turn attacks
Can an attacker gradually manipulate the system through conversation history, memory, or chained actions?
Output and downstream execution
Can model output trigger unsafe SQL, code execution, browser actions, or other unintended operations?
4. How to choose the most effective service
I would evaluate providers against these five requirements:
Real application testing: They test your actual application, integrations, and permissions, not just an isolated model endpoint.
Human-validated findings: Each important vulnerability includes reproducible evidence, demonstrated impact, and clear remediation steps.
Custom attack scenarios: They test your business logic and threat model, not just a standard jailbreak checklist.
Retesting: They verify that identified vulnerabilities are fixed and provide regression tests where appropriate.
Safe handling: They agree on authorized scope, test data, production safeguards, confidentiality, and data retention.
For a startup or small engineering team: Start with Promptfoo for automated application testing, then hire a specialist for a targeted manual assessment.
For an AI agent with access to customer data or production systems: Prioritize a human-led assessment that explicitly tests tool permissions, indirect prompt injection, data exfiltration, and multi-step attack chains.
For a regulated or large enterprise: Evaluate CrowdStrike or specialist consultancies alongside a continuous testing platform such as Mindgard, with formal reporting and remediation verification.
A strong program combines all three layers: automated scans, human-led adversarial testing, and regression testing after fixes. No single scanner or service can guarantee that every vulnerability has been found.
Let me narrow this down
What kind of AI application are you looking to red-team?
1. Application type
Customer-facing LLM chatbot
RAG application using internal documents
AI agent with tool/API access
AI-powered SaaS product or copilot
Custom model or multimodal AI system
2. Preferred engagement
Hire an external provider
Use a self-service testing platform
Combine both
3. Approximate budget (if known)
Under $5,000
$5,000–$20,000
$20,000–$50,000
Over $50,000
Not sure yet
4. Main objective
Discover serious, exploitable vulnerabilities
Continuous automated testing in CI/CD
Compliance, audit evidence, and formal reporting
AI testing plus conventional application penetration testing
