AI is reshaping industries, but security teams treat it like traditional software. Unfortunately, the real problem is AI models don’t just have bugs—they have systemic vulnerabilities. Adversarial manipulation, data poisoning, and model inversion aren’t edge cases; they’re real threats attackers are already exploiting. Yet, most security programs lack a structured approach to testing AI risks.
Conventional pentesting isn’t enough. AI systems introduce dynamic attack surfaces where inputs can be weaponized, models can be tricked, and sensitive training data can leak. Security leaders who assume their existing tools will catch these risks are missing the point—AI security requires a fundamental shift in how we test and defend our systems.
Not all AI pentesting tools are built for this reality as some focus on model robustness, others on data integrity, but few provide a truly holistic approach. The key is choosing tools that don’t just automate security but help you understand how your AI can be manipulated because, in AI security, the biggest risk isn’t what you can see but what you haven’t tested.
What Makes Astra the Best VAPT Solution?
- We’re the only company that combines automated & manual pentest to create a one-of-a-kind pentest platform.
- Vetted scans ensure zero false positives.
- Our intelligent vulnerability scanner emulates hacker behavior & evolves with every pentest.
- Astra’s scanner helps you shift left by integrating with your CI/CD.
- Our platform helps you uncover, manage & fix vulnerabilities in one place.
- Trusted by the brands you trust like Agora, Spicejet, Muthoot, Dream11, etc.
Importance of AI Penetration Testing
Attackers can manipulate AI models, steal training data, or introduce biased outputs by performing attacks like model inversion or prompt injections. These attacks can cause data leakages and security breaches, leading to financial and reputational harm to the organizations.
That is where AI Penetration testing comes into play to protect the AI models from such attacks. AI Penetration testing focuses on assessing the security of the models used by AI applications and automated making systems by performing specialized tests.
Benefits of AI Penetration Testing
- AI Pentesting helps detect vulnerabilities that can manipulate models and their outputs.
- It provides data security by preventing poisoning of the training data that could compromise model training.
- It helps ensure that the model outputs are not biased or manipulated.
- It allows the AI models and tools to comply with various regulatory standards like GDPR, HIPAA, or ISO27001.
Top 7 AI Pentesting Tools
1. Adversa AI
It performs testing on AI models to detect a wide range of vulnerabilities by ensuring the right security measures are applied to them. It provides end-to-end risk and security management for the AI models and applications being developed.
Features:
- Platform: Cloud-based
- Capability: Performs checks for adversarial attacks and integrity threats.
- Accuracy: False positives possible
- Price: Custom pricing was discussed upon contact
- Best Suited For: AI model security and adversarial attack prevention
Pros:
- Specializes in securing AI models.
- Focuses on adversarial attacks
Cons:
- High cost compared to other tools
2. Robust Intelligence
It provides automated AI risk assessment to detect vulnerabilities like adversarial inputs or data poisoning and mitigate them. It also offers automated threat intelligence to protect AI applications from integrity and privacy issues.
Features:
- Platform: Cloud-based
- Capability: Performs checks for output bias, adversarial attacks, and unauthorized data access.
- Accuracy: False positives possible
- Price: Custom pricing was discussed upon contact
- Best Suited For: AI risk assessment
Pros:
- Continuous AI security monitoring
- Provides automated risk mitigation
Cons:
- Needs strong technical knowledge to use
3. Microsoft Counterfeit
It is an open-source AI security testing tool that helps simulate adversarial attacks on AI models. It is a useful tool for security teams as it helps understand model responses to attacks and provides recommendations and mitigations to improve the models.
Features:
- Platform: Command-line tool
- Capability: Simulates adversarial attacks on AI models.
- Accuracy: False positives possible, customizable.
- Price: Free
- Best Suited For: Automation layer testing
Pros:
- It allows customizing attacks on AI models
- Strong community support helps simulate attacks better
Cons:
- Needs strong technical knowledge to use
4. IBM Adversarial Robustness Toolbox (ART)
It is an open-source AI security testing tool that allows users to test their AI models for robustness against adversarial attacks. It helps security teams provide training to AI models against threats and implement detection and evasion techniques.
Features:
- Platform: Command-line; python library
- Capability: Test defense and robustness of AI models
- Accuracy: No False-positives
- Price: Free
- Best Suited For: AI model robustness testing
Pros:
- Protects against evasion, poisoning, extraction, and inference threats
- Provides advanced adversarial testing
Cons:
- Needs strong technical knowledge to use
5. Protect AI
It focuses on protecting AI Applications and MLOps workflows by allowing you to detect, mitigate, and manage threats in the pipeline. It provides end-to-end security for AI models, LLM applications, or SPM platforms by implementing a security-first approach to AI.
Features:
- Platform: Cloud-based
- Capability: End-to-end protection in the AI lifecycle
- Accuracy: False positives possible
- Price: Custom for enterprise, Free version available
- Best Suited For: MLOps and AI Application security
Pros:
- Comprehensive testing in the AI development lifecycle
- Strong compliance and integration support
Cons:
- Higher cost than most tools
6. Hidden Layer
It provides security solutions for AI applications and models by detecting and mitigating various attacks in pre-trained models. It actively detects various injection attacks and monitors the inputs and outputs of the model for potential threats.
Features:
- Platform: On-premise, Saas, and hybrid
- Capability: Performs checks for output bias, adversarial attacks, and unauthorized data access.
- Accuracy: False positives possible
- Price: Custom pricing was discussed upon contact
- Best Suite For: AI model security
Pros:
- Offers real-time monitoring on deployed applications
- Strong detection for model extraction attacks
Cons:
- Needs AI-specific expertise to use
7. Garak
An LLM vulnerability scanner probes the models for a wide array of attacks. Just like Nmap or Metasploit for web security or network security, it is an open-source tool that allows static and dynamic testing of the models.
Features:
- Platform: Command-line tool
- Capability: Tests for prompt injection, data manipulation and bias exploits.
- Accuracy: Low False-positives
- Price: Free
- Best Suited For: Testing chatbots and LLMs
Pros:
- Comprehensive testing for prompt injection
- Helps maintain regulatory compliance by enforcing AI guidelines
Cons:
- Limited to language model security
Final Thoughts
AI penetration testing has become crucial to protect ML models against advanced threats like prompt injections, adversarial attacks, data poisoning, or model theft. Organizations or individuals can use advanced AI Penetration testing tools to ensure that their models, AI-drive applications, and chatbots are secure and protected against such attacks.
Choosing the right tool based on your needs is essential to get efficient results and mitigations to secure your asset and comply with regulatory standards. Regular AI penetration testing, with a mix of open-source and commercial tools, can help enhance AI security and maintain user trust in these applications.
FAQs
1. How do AI pentesting tools differ from traditional pentesting tools?
Traditional tools focus on networks and applications, while AI pentesting tools precisely assess vulnerabilities in machine learning models and AI-driven systems.
2. Who should use AI pentesting tools?
Any organization using AI in critical applications, such as finance, healthcare, and autonomous systems, should implement AI pentesting tools on their assets to look for security vulnerabilties.
3. Can AI pentesting tools fully secure an AI system?
While they significantly reduce risks, AI pentesting tools should be used alongside other security measures to create a comprehensive defense strategy, and in-depth manual testing should also be performed with it.
