Artificial intelligence (AI) holds a lot of promise for many industries, empowering workers to reduce manual processes while improving work speed and quality. However, AI and large language models (LLMs) are also prime targets for cyber attacks.
The vulnerabilities are as complex as the technologies themselves, from adversarial attacks that mislead AI systems to data poisoning that compromises their integrity. Traditional security measures, while valuable, often aren’t enough to address these sophisticated attacks.
This is why more organizations are pentesting generative AI. Pentesting AI models is a cutting-edge approach that uses the AI’s own features to prevent attacks.
In this guide, we'll explore the most common vulnerabilities in LLM systems, practical strategies to fix them, and why choosing the right security partner—like Mindgard—can make all the difference.
AI penetration testing evaluates the security of artificial intelligence systems by simulating attacks that exploit their vulnerabilities. Unlike traditional penetration testing, which focuses on networks, applications, and infrastructure, AI penetration testing uncovers weaknesses in the AI models' architecture, data handling, and decision-making processes.
AI penetration testing helps identify vulnerabilities that could allow hackers to exfiltrate sensitive data. Plus, this approach exposes AI models to many potential attacks, helping developers strengthen them before they’re released. With more regulations coming out regarding ethical AI, pentesting AI models also allows developers to ensure compliance.
Like any digital platform, generative AI has exploitable weaknesses. While designing a 100% hack-proof large language model is impossible, developers can prevent most attacks through thorough testing. Learn about the most common vulnerabilities and how AI penetration testing addresses them.
In this type of attack, a hacker injects malicious data into training datasets, corrupting the model’s learning process. As a result, the AI system adopts incorrect patterns or behaviors, compromising the reliability of its outputs. Mitigation strategies such as data validation and anomaly detection help safeguard training data by identifying and filtering out suspicious or manipulated inputs.
Cyber security teams don’t have to address data poisoning alone, either. Mindgard's platform includes comprehensive data integrity assessments, which identify and mitigate risks associated with data poisoning.
Prompt injections occur when attackers use manipulative prompts to generate harmful content, such as malicious code or discriminatory language. AI penetration testing will unearth these issues, which can be fixed through strict input validation and user authentication.
Mindgard’s continuous automated red teaming (CART) solution identifies vulnerabilities to prompt injection attacks through techniques including Input Fuzzing & Adversarial Testing, Pattern & Keyword Analysis, Context Manipulation Detection, Response Behavior Monitoring, Encoding & Escape Sequence Checks, and Context Boundary Testing, enabling organizations to fortify their AI systems against this common exploit.
Malicious actors execute model inversion attacks to reconstruct sensitive data or replicate your model. If successful, this exploit gives attackers access to proprietary data.
Mindgard provides tools for vulnerability scanning and implements defenses against model inversion and extraction attempts, ensuring the confidentiality of both models and their training data. From there, our team recommends mitigations so that organizations can enhance the security and reliability of their AI systems against attack.
More organizations are relying on AI models to speed up their workflows. While large language models and similar technologies are effective, they aren’t free from security concerns. From data poisoning to prompt injections and privacy risks, the threats to large language models are as diverse as they are dangerous.
Mindgard is a powerful ally in the fight against AI takeovers. By leveraging advanced adversarial testing, bias detection, and privacy-preserving techniques, Mindgard provides an all-in-one solution to AI security challenges. Not only that, but our team of human experts will guide you through the steps required to mitigate these issues after pentesting generative AI.
Don’t leave your AI unsecured. Schedule your Mindgard demo today to proactively plan for the threats of tomorrow.
Vulnerabilities change over time, but some of the most common vulnerabilities uncovered by AI penetration testing include:
Regular AI pentesting is the best way to uncover potential security risks in your large language model. Adversarial training exposes these models to various attack scenarios, helping you create a more secure model long before releasing it to users. Input sanitation, abnormality monitoring, data validation, and aggregation techniques also help.
Addressing bias and fairness is essential because AI models that propagate biases can lead to legal liabilities. Developers should train their AI models on diverse datasets that are more representative of the outputs they want. Bias detection tools are also helpful for pinpointing skewed patterns.
