AI application vulnerability testing

For an AI application red-teaming exercise, my recommendation is to shortlist Mindgard, Promptfoo, Microsoft PyRIT, and Palo Alto Networks Prisma AIRS.

The best choice depends on whether you want an automated vulnerability assessment, a full adversarial security engagement, or continuous testing integrated into your development pipeline.

One important distinction: test the entire AI application, not just the underlying LLM. Your application may introduce vulnerabilities through system prompts, RAG pipelines, document ingestion, APIs, agents, tool permissions, and data access controls.

My top recommendations

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1. Mindgard

(Best for expert-led red teaming)

• Combines automated testing with human security researchers.
• Covers prompt injection, jailbreaks, data extraction, RAG, agent hijacking and tool abuse.
• Provides severity-ranked findings, remediation recommendations and retesting.
• Offers a managed red-teaming engagement.

My take: Best starting point if you want an actual security assessment rather than just running automated prompts.

Website:Mindgard AI Red Teaming Services+1

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2. Promptfoo

(Best overall developer tool)

• Automated testing across 50+ vulnerability types.
• Tests application APIs, RAG and AI agents.
• Supports custom security policies.
• Integrates with CI/CD pipelines.
• Open-source foundation.

My take: Excellent for an engineering team that wants to conduct its own assessment and continuously retest fixes.

Website:Promptfoo Red Teaming+2

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3. Prisma AIRS

(Best enterprise platform)

• Automated adversarial testing.
• More than 50 attack techniques and 500 scenarios documented.
• Supports AI models, applications and agents.
• Multi-turn and multi-agent testing.
• Integrates with broader enterprise AI security capabilities.

My take: Strong candidate for larger organizations already using Palo Alto Networks security products.

Website:Prisma AIRS AI Red Teaming+2

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4. Microsoft PyRIT

(Best open-source framework)

• Automated and human-led red teaming.
• Multi-turn adversarial testing.
• Customizable testing scenarios.
• Supports multiple AI providers and custom endpoints.

My take: Great for an internal security team with Python and AI security expertise.

Website:Microsoft PyRIT Documentation+2

What I would test in your AI application

I would structure the exercise around these eight security areas:

Use the OWASP GenAI Security Project as a reference for structuring coverage.

My recommended approach

If I were organizing this exercise, I'd use:

1. Mindgard for an expert-led initial security assessment.
2. Promptfoo for repeatable automated testing and regression testing.
3. PyRIT for additional custom adversarial assessments.

This combines human expertise, automated vulnerability discovery and ongoing validation. No platform alone can certify an AI application as secure.

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Three questions to narrow down the best platform

I can make a much more precise recommendation if I know a little about your application.

1. What type of AI application are you testing?

Customer-facing AI chatbot

Internal enterprise AI assistant

RAG / enterprise knowledge base

AI agent with tools and API access

Custom AI application / multiple models

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2. What is the underlying architecture?

OpenAI API / Azure OpenAI

AWS Bedrock

Google Vertex AI

RAG / enterprise knowledge base

Self-hosted or open-source LLM

Multiple providers

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3. What is your primary objective?

Comprehensive security assessment

Find jailbreaks and prompt injection vulnerabilities

Test data leakage and access control

Test AI agents and tool execution

Compliance and security audit

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Budget

Free / open source only

Under $10,000

Enterprise budget available

Prefer managed professional services

[Get my platform recommendation]

With those answers, I can narrow this down to the best platform, explain why it fits, and propose a practical red-teaming assessment plan with deliverables and success criteria.

Choosing the "best" AI red teaming platform depends on whether you are looking for an open-source framework (to run tests yourself programmatically), a commercial automated platform (for continuous CI/CD scanning and production visibility), or a managed service (expert-led consulting).

The top options across these categories are organized below to help you select the right fit:

1. Top Commercial AI Red Teaming Platforms (Automated & Continuous)

• Mindgard: Widely recognized as a leading automated AI red teaming and security platform. It stands out for deep coverage of LLMs, RAG pipelines, and agentic workflows, offering continuous automated testing that plugs into your existing development and security pipelines.
• HiddenLayer: Excellent for enterprise model discovery, security monitoring, and adversarial machine learning simulation across production environments.
• Check Point (Lakera): Offers robust prompt injection defense and AI red teaming capabilities, highly suited for organizations looking to govern LLM interactions in real time.
• Enkrypt AI: Strong focus on governance, multi-modal applications, and RAG vulnerability coverage with built-in runtime protection.

2. Top Open-Source AI Red Teaming Frameworks (Developer-Led)

• Microsoft PyRIT (Python Risk Identification Tool): An open-source orchestrator designed to automate risk identification in generative AI systems. It acts like a "Metasploit for AI," orchestrating multi-turn attacks like jailbreaks and prompt injection.
• Garak (LLM Vulnerability Scanner): Often called the "Nmap for LLMs," garak scans conversational models for vulnerabilities, data leakage, and toxic outputs through a modular probe-and-detector architecture.
• Promptfoo: Great for lightweight, local testing of prompts, security guardrails, and model outputs, often integrated directly into CI/CD build steps.generalanalysis.com

3. Top Managed Services & Consultancies (Human-Led / Advisory)

• Mandiant (Google Cloud): Offers world-class, human-led red teaming and specialized AI security assessments built on decades of frontline breach intelligence.
• Bishop Fox: Features targeted offensive security services, including advanced AI and application-layer mapping (AIMap).
• Big Four / Global Consultancies (Deloitte, Accenture, EY): Best if your red teaming exercise requires a comprehensive risk, regulatory compliance (e.g., EU AI Act, ISO 42001), and governance audit alongside technical exploitation.neuraltrust.ai

To help narrow down the ideal approach, could you tell me:

• Is your AI application a custom LLM wrapper, a multi-agent system, or a traditional ML model?
• Are you looking for an automated software tool to integrate into CI/CD, or an external security firm to run a formal assessment?

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.owasp.github.io+2

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

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1. Promptfoo

(Best overall starting point)

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:Promptfoo

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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:Mindgard

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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:PyRIT on GitHub

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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:Garak on GitHub

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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:Lakera

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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:HiddenLayer

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.owasp.github.io+2

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.

Explore AI penetration testing

Source: provider's published service scope.www.blazeinfosec.com+1

CrowdStrike

Best for organizations with an existing enterprise security program

Offers AI red-team services and penetration testing for AI applications, including assessments aligned with OWASP's LLM security risks.

Explore CrowdStrike AI red teaming

Source: provider's published service description.www.crowdstrike.com

RedSecLabs

Best for a human-led, security-focused assessment

Advertises AI and LLM penetration testing by security engineers, covering RAG poisoning, prompt injection, agent abuse, and the surrounding application and infrastructure.

Explore RedSecLabs AI testing

Source: provider's published service description.www.redseclabs.com+2

Redbot Security

Best for testing complex enterprise AI attack paths

Focuses on manual security testing across models, APIs, retrieval pipelines, agent workflows, MCP integrations, and connected enterprise systems.

Explore Redbot AI security testing

Source: provider's published service description.redbotsecurity.com

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.www.pillar.security+1

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 formal evaluation framework, see the OWASP Vendor Evaluation Criteria for AI Red Teaming Providers and Tooling.www.pillar.security+1

My recommendation

• 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

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2. Preferred engagement

Hire an external provider

Use a self-service testing platform

Combine both

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3. Approximate budget (if known)

Under $5,000

$5,000–$20,000

$20,000–$50,000

Over $50,000

Not sure yet

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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

[Find the best-fit services]

When investigating and remediating prompt injection and agentic exploits, several specialized AI security firms, major cloud providers, and global professional services offer dedicated AI Red Teaming assessments.

The primary providers in the AI red teaming ecosystem are categorized below by their service focus:

1. Specialized AI Security & Pure-Play Firms

• Mindgard: Specializes in Mindgard AI Red Teaming Services covering LLM applications, RAG pipelines, multi-turn vector tests, and indirect/direct prompt injection vulnerabilities.mindgard.ai
• Protect AI: Offers security frameworks and red teaming focused on the entire machine learning and AI pipeline security.
• HiddenLayer: Provides security solutions and adversarial machine learning protection, targeting prompt injection and model manipulation.
• Robust Intelligence (part of Cisco): Focuses heavily on automated stress testing, continuous validation, and vulnerability assessments for AI models.

2. Enterprise & Cloud Security Giants

• Microsoft: A market leader leveraging its dedicated Azure AI Red Team and proprietary tools like PyRIT (Python Risk Identification Tool) to map out jailbreaks and prompt-based data leakage.
• Palo Alto Networks: Offers Palo Alto Networks Prisma AIRS featuring automated vulnerability hunting and AI red teaming for autonomous agents.
• Check Point Software: Provides robust testing frameworks and Check Point Software AI Security designed to stress-test LLM applications and agentic pathways against prompt injections.
• CrowdStrike & SentinelOne: Both offer tailored AI red teaming services (such as SentinelOne's Singularity AI testing) to audit LLM and agentic workflow safety pre-production.straiker.ai

3. Global Professional Services & Consultancies

• Big Four & Consultancies: Deloitte, Accenture, PwC, EY, and KPMG all host dedicated Responsible AI and adversarial security practices that perform hands-on red teaming engagements for enterprise client deployments.
• Booz Allen Hamilton: Frequently engaged for high-stakes federal, defense, and critical infrastructure AI adversarial testing.
• MITRE Corporation: Known for pioneering evaluation frameworks like MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems).straiker.ai

If you would like, I can set up a task to monitor new threat advisories regarding prompt injection techniques or notify you of updates in AI red teaming frameworks.

For an investigation into a security breach involving prompt injection, you’ll want an AI Red Teaming provider with experience testing large language models (LLMs), retrieval-augmented generation (RAG), AI agents, and the systems and tools they can access.

The key distinction is between general penetration testing and AI-specific adversarial testing: the latter examines whether malicious instructions hidden in user input, documents, retrieved content, or tool outputs can override intended safeguards, expose sensitive data, or trigger unauthorized actions.

Here are providers and specialist organizations worth evaluating for an incident investigation and follow-up security assessment.

AI Red Teaming providers to consider

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1. Bishop Fox

(Incident-focused assessment)

Offers hands-on AI/LLM penetration testing and red teaming. Its published scope explicitly includes direct and indirect prompt injection, sensitive-data disclosure, guardrail bypasses, agent and tool misuse, and incident-response readiness. A strong candidate when you need to investigate a real exploit path, not just run automated scans.bishopfox.com+1

Website:AI/LLM Security Assessment

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2. HiddenLayer

Specializes in AI security assessments, red teaming, and automated attack simulation. Its capabilities include prompt injection, jailbreaks, model-data leakage, system-prompt extraction, and unsafe tool use. Consider it if you want repeatable testing after remediation as well as an initial assessment.www.hiddenlayer.com+2

Website:AI Red Teaming

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3. DarkRange

Provides AI security consulting, architecture reviews, and adversarial testing. Its stated services cover prompt injection, jailbreaks, data extraction, RAG pipelines, guardrails, and AI agents. Relevant when the investigation needs to examine the entire AI application architecture.darkrange.ai

Website:DarkRange AI Security

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4. ReinforcedX

Offers LLM and agent security consulting covering prompt injection, jailbreaks, data leakage, and tool abuse. Its published engagement model includes turning failed attacks into repeatable evaluation tests and continuous-integration security gates. Useful for teams seeking a remediation and regression-testing workflow.www.reinforcedx.com+1

Website:LLM Security & Red Teaming

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5. TrustEdge.ai

Offers adversarial testing of LLM and agent deployments, including prompt injection and risks associated with agentic applications. Its published approach emphasizes testing the actual deployed system and its permissions rather than simply verifying that security controls exist.trustedge.ai

Website:AI Red Teaming & Prompt Injection Testing

What to request for your breach investigation

For an actual security incident, I would prioritize a scoped forensic investigation combined with an adversarial AI security assessment, rather than purchasing a red-team scan alone.

Ask prospective providers whether they can deliver these six workstreams:

Preserve relevant logs, prompts, model and application versions, retrieved documents, tool-call records, and identity/access logs before making changes. If data may have been exfiltrated, involve your incident-response team and restrict potentially compromised credentials as appropriate.

How I would narrow the shortlist

Based on the published services above:

• For a live breach and hands-on attack reconstruction: start by discussing scope with Bishop Fox.
• For repeatable adversarial testing across multiple AI systems: evaluate HiddenLayer.
• For RAG, agent architecture, and remediation work: also evaluate DarkRange and ReinforcedX.

These are scope-based suggestions, not a ranking of independently verified performance. Confirm availability, forensic capabilities, relevant references, and engagement terms directly with each provider.

One question that would help me refine the shortlist: What kind of AI system was breached?

AI system

Enterprise LLM or RAG chatbot

AI agent with tools or API access

Public-facing AI product

Foundation model or training pipeline

Other / not yet determined

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Primary engagement goal

Investigate a confirmed incident

Independent adversarial penetration test

Ongoing automated red teaming

Forensics, remediation, and retesting

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Provider preference

US-based provider preferred

Global providers acceptable

Specialist boutique consultancy preferred

[Refine provider shortlist]