Top 12 AI Pentesting Tools

AI-specific penetration testing tools are critical for your security program as traditional security best practices fall short of protecting your organization against adversarial attacks to LLMs and generative AI models. We compare the top 12 AI pentesting tools by capabilities, AI focus, and use case below to help you select the right tool for your organization.

The table below compares all 12 tools by AI focus, licensing, and core capability so you can quickly narrow down the right fit.

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What to Look for in an AI Pentesting Tool

When choosing pentesting tools for AI initiatives, businesses should prioritize capabilities for assessing large language models.

For instance, businesses must ensure that their AI pentesting tools include adversarial testing capabilities to create adversarial examples for attacking AI-based applications.

Tools should also help businesses conduct testing for data integrity and poisoning attacks. Since data integrity ensures that the data AI systems use has not been altered or tampered with, testing for integrity ensures that decision-making is not affected by bad data.

Tools should also have model extraction features since model theft, such as model leeching, is increasingly common. Model leeching allows attackers to recreate your proprietary model by sending queries to it. To prevent this from happening, AI security testing tools should have detection and prevention countermeasures for extraction such as rate limiting, query monitoring, and testing to make your model more robust against adversarial attacks.

Tools should also have query detection and prevention countermeasures built-in. API security testing is also important, so make sure AI tools have this functionality to detect authentication and input validation issues.

Tools should have runtime and behavioral analysis capabilities. This will allow you to monitor your AI systems while they’re under attack and detect anomalies. Lastly, pentesting tools should have logging, reporting, and help with compliance.

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LLM-Specific Attack Vectors

LLM applications open up a completely new attack surface. Rather than attacking infrastructure, malicious actors will attack inputs, context, and data. The big three risks are prompt injection, jailbreaking, and data poisoning.

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What This Means for Choosing AI Pentesting Tools

• Chatbots/copilots: Requires prompt injection + jailbreak detection
• Agentic systems / RAG: Needs multi-step + poisoning defenses
• Model integrity: Calls for training data validation

Teams will typically use some combination of the following: 

• LLM-native testing solutions (Garak, PyRIT)
• Evaluation frameworks (promptfoo)
• Guardrails (LLM Guard, NeMo Guardrails)
• Platforms like Mindgard that provide full lifecycle testing, as well as runtime detection

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AI Pentesting vs. Traditional Pentesting

AI pentesting and traditional pentesting have one common goal: identify weaknesses before attackers do. They differ in what is being tested, how attacks are performed, and how the testing can be automated.

Traditional pentesting is focused on systems and infrastructure: networks, APIs, applications. The attack surface is well-defined and mostly revolves around code, configurations, or exposed services that have vulnerabilities.

AI pentesting revolves around model behaviors and how data flows through them. The attack surface becomes the inputs (prompts), context (RAG), and training data where bad actors can manipulate output vs attacking the system itself.

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Standard pentesting tools won't catch model-specific weaknesses like prompt injection and jailbreaking. But AI pentesting won't prove your infrastructure is secure.

You need both infrastructure testing for servers and APIs, and AI pentesting for your model’s behavior and data integrity.

Only then can you be sure you’ve protected your full attack surface, from infrastructure to AI.

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Methodology: How We Selected These Tools

Before we get into the list, we want to share a bit about how we researched and compiled these tools.

This list isn’t meant to be comprehensive of all security testing tools. Instead, we wanted to provide a list of tools that specifically help teams validate and test their AI applications under realistic attack scenarios.

With that in mind, we researched tools that met certain criteria around testing for model risks (prompt injection, jailbreaking, data-poisoning) that generic pentesting tools wouldn’t catch. Here are some things we considered: 

• Relevant to AI attack surfaces: Tools had to pentest or attack the LLM itself (behaviors/model), prompts, or data pipelines. We didn’t include tools focused on infrastructure/host scanning, dependency scanning, APIs, etc.
• Depth of red teaming capabilities: We prioritized tools that allowed you to do more than provide static test cases. Did it support multi-turn attacks? Could you generate adversarial prompts? Free-form scenario testing?
• Coverage across the AI lifecycle: We looked for tools that enabled testing across a model’s inputs, surrounding context (RAG) and data it was trained on rather than a single attack surface.
• Automated testing: Automated and repeatable testing processes were prioritized over manual tooling or tools that provided a point-in-time view.
• Practical adoption: We wanted this to be a list of tools teams are actually using to build and scale AI security programs. This includes open-source projects like Garak, PyRIT, promptfoo, but also commercial platforms like Mindgard.

What This List Does (and Doesn’t) Cover

This list contains tools you can use to test and validate AI under realistic attack conditions. The tools presented here are not ranked according to preference or efficacy.

We did want to include some tools that can be leveraged as part of an AI testing workflow (e.g. network scanners, protocol analysis tools) but in isolation are not enough to pentest your AI components. You’ll notice many of these tools focus on helping teams move from point-in-time testing of AI components to continuous validation against realistic attacks.

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The Best AI Pentesting Tools

Not all penetration testing tools are built for AI security. Some were designed for testing networks and web apps long before LLMs existed.

Each of these tools takes a different approach to penetration testing AI and LLM systems, from purpose-built AI red teaming frameworks to traditional scanners adapted for modern attack surfaces. Below we break down what each tool does, where it excels and which use cases it fits best.

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Burp Suite: Web App Security Testing

Burp Suite gives you hands-on web security testing, automated DAST scanning, and CI-driven DAST scanning in one AI-powered tool. Get the bleeding edge information on pentesting from PortSwigger Research.

Map your attack surfaces, take advantage of automation features to spot vulnerabilities, and aggregate logs from all your tools into one data source.

Cost: $499 per user per year

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Mindgard: AI Pentesting, Red Teaming & Runtime Protection

Mindgard’s AI pentesting solutions can also be leveraged for red teaming on autopilot. With Mindgard, organizations can fight back against advanced attacks. Mindgard’s Offensive Security solution will identify AI security threats humans are likely to miss.

Mindgard’s MITRE ATLAS™ Adviser enables structured AI security testing based on the MITRE ATLAS framework. Organizations can use it to discover AI vulnerabilities and bolster AI security through standardized adversarial AI testing.

Mindgard’s AI security testing solution also performs continuous vulnerability testing to maintain AI model security. Schedule a demo today to learn how Mindgard’s Offensive Security solution works.

Cost: Contact for tailored pricing

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Metasploit: Exploit Testing Framework

Metasploit Metasploit is yet another tool used for penetration testing. The penetration testing framework can be downloaded for free, though they also offer a commercial version of the framework made specifically for penetration testers.

However, the free version of the framework can come in quite handy for pentesting. There are very detailed checklists for pentesting attacks like basic attack payloads and the Meterpreter advanced payload.

Cost: Contact for pricing

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NetSPI: Managed Pentesting Services

NetSPI provides cloud pentesting, SaaS pentesting, and application pentesting. This is a paid AI pentesting tool that works with traditional AI and custom LLMs.

Cost: Contact for pricing

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Nessus: Vulnerability Scanning Tool

Nessus is a Tenable solution that secures not only AI models but your entire infrastructure. Nessus is a paid solution that allows you to scan web applications, cloud, and external attack surfaces.

Nessus also uses AI to discover potential paths to exploit based on historical data and machine learning.

Cost: $4,790 (Professional) or $6,790 (Expert) for a one-year license

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XBOW: Autonomous Pentesting Platform

XBOW is an AI-powered, automated pentesting platform that launches agents to pentest applications aggressively, like a real-world attacker would, at scale. XBOW identifies vulnerabilities, exploits them, validates them, learns and re-tests with evolving payloads at machine speed. Valid findings are reported with proof-of-concept exploits and remediation recommendations.

XBOW is designed to operate like a human pentester would. It systematically maps out attack surfaces, navigates through potential penetration routes, and adjusts its strategies on the fly according to how the application reacts, handling the whole process from testing to reporting without human intervention.

Cost: Starts at $4,000 per test

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Penligent: Agent-Based Pentesting Platform

Penligent is an AI-powered agent-based pentesting platform that enables pentesting teams to automate the complete pentesting lifecycle, from reconnaissance to reporting, using natural language instructions and self-adaptive attack automation. 

Penligent connects pentesting tools like Nmap, Metasploit, Burp Suite, and more into one AI-driven workflow. Generate repeatable findings complete with proof-of-concept exploits, scan summaries and compliance-ready reporting with human-in-the-loop decisioning. 

‍Cost: Pro plans start at $49.90/month, with scaled pricing based on monthly credits. Free, Enterprise, and Team plans are also available.

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Open-Source AI Pentesting Tools

Want more flexibility? Want to kick the tires before investing in a commercial product? There are also Open-source AI pentesting tools. These range from AI-native red teaming frameworks to traditional network security tools that remain useful in testing AI applications.

Some teams find themselves using both: specialty AI tools for red teaming models, and traditional tools to poke around the underlying infrastructure and validate at the API-layer.

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Garak: LLM Vulnerability Scanner

Garak is a vulnerability scanner that’s specific to LLMs. It’s an open-source AI pentesting tool that identifies security vulnerabilities using plugins and hundreds of probes. After it finishes scanning, the AI Pentesting Tool will report back everything it discovered as well as how to remediate it.

Similar to PyRIT, which we'll discuss below, Garak is an upstream primary red teaming tool designed for LLMs. Running realistic-world attacks using a large library of probes/plugins against model behaviour (instead of system endpoints), comprehensive reports are generated identifying vulnerabilities found along with potential mitigations.

Cost: Free (Open Source)

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PyRIT: AI Red Teaming Framework

PyRIT is an open-source adversarial artificial intelligence red teaming toolkit developed by Microsoft Azure. PyRIT was developed with the goal of assisting security teams in finding potential vulnerabilities with their LLM deployments. PyRIT does this by simulating more realistic multi-turn attacks that an actual attacker is likely to perform over the course of an extended conversation with a target model. Other tools, such as Garak, rely on users being able to directly run model-breaking prompts against their deployed models.

PyRIT takes a novel approach to attacking LLMs by scoring models on behavior throughout conversation. The scenarios tested by PyRIT are meant to evaluate a model's jailbreak resistance, as well as its resistance to prompt injection attacks and producing unsafe content. PyRIT has been proven effective at red teaming chatbots and agent-style applications before they enter production.

Cost: Free (Open Source)

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PentestGPT: AI Pentesting Assistant

As the name suggests, PentestGPT is a pentesting chatbot that has a user interface similar to that of ChatGPT. Think of it as an AI-based assistant you can utilize during pentesting. By simply putting in commands, it can use NLP to conduct automated vulnerability scans and then provide you recommendations on potential exploitation paths.

Cost: Free (Open Source), users pay OpenAI directly for token usage

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Nmap: Network Scanning Tool

Nmap is a widely-used open-source tool for network scanning and security auditing that’s free and open source and can be used for network scanning and security auditing. It’s not as in-depth in its AI model attacks as other paid pentesting tools, such as Mindgard, but it does allow you to prioritize vulnerabilities according to the level of risk.

Cost: Free (Open Source)

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Wireshark: Network Traffic Analysis

Wireshark is technically a network protocol analysis application. No matter which platform you install it on, Wireshark offers live data streams in various formats tailored to your operating system. The tool also regularly refreshes its protocol database, so you're always equipped with the latest best practices for your tests.

You can also use it to test AI applications that use APIs for communication with cloud-based services. For instance, by capturing network traffic and analyzing it, you can use Wireshark to find vulnerabilities like data leaks, unencrypted sensitive data, API abuse, and other network-centric adversarial threats.

However, Wireshark can only analyze network traffic. So while you won’t be able to use it to directly test your AI model (to check for bias or run adversarial attacks against it, analyze it offline, etc.) it can still be used to assess your AI security in a broader sense.

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Cost: Free (Open Source)

Quickly compare the open-source AI pentesting tools below: 

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Redefining Pentesting for AI Platforms

LLMs, chatbots, and other ML models are the future of business, but that also brings the risk of more cyber attacks in the future. The top 12 AI-based pentesting tools are the future of cybersecurity, providing much-needed protection for your business through the efficiency of AI, ML, and intelligent cybersecurity systems. 

However, despite the many options available in the market, Mindgard remains the gold standard in AI pentesting services. Our ability to detect zero-day attacks makes us the most reliable option for AI-based cybersecurity services. Book your demo with Mindgard today to protect your AI-based systems from cyber attacks.

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Frequently Asked Questions

Can AI tools replace human penetration testers?

AI pentesting tools can’t fully replace human penetration testers. While AI-powered systems can complete penetration testing much quicker than a human due to automation, it’s best to use AI-based systems along with human penetration testers to complete penetration tests.

Will AI-based pentesting tools always be 100% accurate?

AI-based tools can be very precise in identifying vulnerabilities, as AI has the advantage of feeding on large quantities of data and learning from it. But the precision of these AI-based tools also comes down to the algorithm it uses to complete penetration tests. One way to make AI more precise is to verify their results with human assistance.

Are AI-powered penetration testing tools only for big enterprises? What about small and medium businesses?

AI-powered pentesting tools can be used by small businesses too. Many AI-based penetration testing providers provide small business packages, allowing these smaller companies to have the protection they need. AI systems can help automate the process of detecting vulnerabilities and can prioritize the risks that your business might face.