Top AI Security Vulnerabilities and How to Prevent Them

Artificial intelligence is on the verge of entering the mainstream era, as it has been aggressively adopted almost everywhere. With this, it also introduces both the advantages and severe data security risks. AI models depend on input data, algorithms, parameters and output, which invites potential vulnerabilities. According to a recent survey by Darktrace, around 74% of respondents believe AI threats and vulnerabilities pose a significant challenge to their organizations.

With big software companies integrating AI into their core products, risks have significantly soared in the past few years. Improperly designed or poorly trained models could expose sensitive data through prompt attacks or inference. Attackers are now capable of employing sophisticated attack techniques that enable them to manipulate training data, leading to biased predictions. These vulnerabilities can prove to be significantly expensive to security teams. Thus, implementing proper security measures and utilizing a reliable AI security platform are crucial to safeguard AI systems against emerging AI vulnerabilities.

This blog explores what AI security vulnerabilities are and offers insights on how to mitigate them effectively.

What are AI Security Vulnerabilities?

AI security vulnerabilities are flaws or weaknesses in artificial intelligence systems that attackers exploit to steal data, compromise operations, or expose intellectual property.

These vulnerabilities affect all types of AI systems which include

• Generative AI (ChatGPT, Gemini) - vulnerable to data leaks and injection attacks.

• Predictive models (Forecasting and fraud detection tools) - prone to poisoned training data.

• AI-powered software tools (GitHub Copilot) - vulnerable to exposing business intellectual property or proprietary code.

• Enterprise AI applications (AI assistant bots, analytics) - prone to sensitive data access or confidential corporate data.

How Does AI Create Security Vulnerabilities

Here’s a breakdown of how AI creates security vulnerabilities.

Exposure of Data

When organizations train AI models on exclusive datasets, the system can gain access to sensitive or confidential data, including business strategies, operational schedules, customer details, trade secrets, and intellectual property (IP). If attackers get control, they can directly exploit the artificial intelligence by leading prompts and extract confidential corporate data slowly without breaching conventional security systems.

Manipulating Training Data

Attackers can add false or incomplete datasets into an Artificial intelligence system. This manipulation causes the model to generate misleading output or predictions. These inaccuracies result in costly errors and damage the organization's reputation.

Attack Surface in AI-Powered tools

Popular mainstream AI-powered software tools such as GitHub Copilot or Gemini Code Assist are capable of scanning software codebases. It supports developers and also creates pathways to critical IP exposure. This vulnerability presents a significant AI attack surface, particularly for organizations heavily reliant on intellectual property.

What are the Attack Vectors Caused by AI

AI attack vectors are pathways cyber attackers utilize to exploit systems, disrupt operations, steal sensitive data, IP or credentials. They employ a combination of traditional methods, such as malware, phishing, and harmful links, along with AI-powered technologies like machine learning, deep learning, and natural language processing. Additionally, these methods have also evolved and automated existing attacks to make them a more effective and the more complex to detect. The common attack vectors created by AI are.

AI-Powered Social Engineering

Artificial intelligence utilizes AI algorithms to expedite the analysis of large datasets and identify behavioral patterns. Attackers utilize this method for phishing, impersonation, and manipulation of trust to extract data from targeted organizations.

AI-Powered Proprietary Risk

Proprietary AI systems carry unique risks even if they seem as a safer choice than open-source alternatives. AI-powered features embedded in business applications attract both external insiders and attackers. Insiders with access privileges and system knowledge may know how to bypass weak audit controls or reverse engineer the model for exploitation. Proprietary systems usually depend on custom monitoring and are often less secure than standardized solutions, which creates blind spots for businesses.

What are the Top 7 AI Security Vulnerabilities?

AI has several vulnerabilities that cause significant damage, some of the common vulnerabilities are.

Privacy and Copyright Breaches

When sensitive data is included in AI model training sets, it exposes individuals to risks such as surveillance or identity theft. These data breaches are violate privacy rights and also damage user trust in the AI technologies. The another concern is copyright breach, where AI systems can unintentionally use copyrighted data without a authorization during training or output generation, raising legal concerns.

Data Poisoning

Data poisoning is an attack in which a cyber attackers inject false, biased or corrupted information into the training data of AI models. Attackers who gain the access to training repositories can contaminate the data, thereby attaining a significant control over the model’s behavior. Such a vulnerabilities underscore the crucial need for a robust data governance, effective access control, and thorough the integrity checks in AI development.

Compromised Model Components

AI development relies on third-party tools, frameworks, and pre-trained models, which improves innovation but also increases the probability of an attack surface. If these components are compromised, it becomes easy for attackers to integrate malware directly into the machine learning models, exposing organizations to unknown threats.

Subpar Model Performance

Many machine learning models fail to reach the production stage, and even those that successfully make it to that stage often underperform in real-world environments. Models trained on controlled datasets may fail when faced with new data types. This hindrance causes them to degrade quickly. In other cases, teams may perform rushed deployments by avoiding the testing process, which further weakens the models. Thus, it not only disables security but also introduces technical debt, resulting in increased operational costs as the model scales.

Data Leakage and Disclosures

AI models can accidentally expose sensitive data if proper security practices are not implemented. Some of the vulnerabilities include disclosing user inputs, outputs or data stored in the model’s memory, which attackers can exploit with prompt manipulation. Even without direct attacks, accidental leaks often occur due to technical misconfigurations, which cause confidential data disclosures.

Direct Adversarial Attacks

Adversarial attacks contaminate AI systems through deceptive inputs that are designed to modify behavior or degrade performance. These attacks often include the exploitation of API weaknesses and access privileges. Not only do the attackers disrupt operations, but they also attempt to steal the information from entire AI models by conducting brute-force attacks or phishing campaigns that target Machine Learning as a Service (MLaaS) platforms.

Model Drift

Model drift happens when an MLM’s prediction becomes inaccurate over time because real-world conditions change, but the model has not adapted to them. Drift typically appears as either data drift (shift in input data distribution) or concept drift (Change in nature or patterns). Most often, drift can cause bias in results, minimize accuracy, and even render models unusable, leading to costly business consequences.

What are the Best Practices to Mitigate AI Security Vulnerabilities

Here’s a breakdown of the best practices to mitigate AI security vulnerabilities.

Data Minimization and Data Anonymization

If sensitive data is not effectively anonymized, it gets leaked into training datasets and expose organizations to both compliance violations and security threats. To mitigate this, security teams must enforce encryption, secure multi-party computation, and differential privacy methods. Specialized privacy tools that automatically secure sensitive data, add secure training algorithms, and test resilience against emerging threats should be implemented.

Strong Data Management

Strong and reliable data management is essential to prevent both accidental and malicious data leaks. Security teams must implement stringent access and identity management controls, where only verified or authorized users or applications can use the model data. Additionally, using version control models helps track variants and eliminate those trained on sensitive information. Apart from this, encrypting data in transit and at rest with input sanitization for generative AI systems minimizes the risks of breaches and prompt injection attacks.

Implement Stringent Security Policies

Strong security policies begin with proper asset management, ensuring that all AI model assets are tracked, authenticated, version-controlled, and restorable in the event of severe compromise. Enforcing the principle of least privilege helps a restrict access to the sensitive training data. A different security layers should be enforced based on the data type. Organizations can also implement cryptographic model signing, which provides the transparency and enables the downstream users to verify the trustworthiness and a integrity of models.

Model Monitoring and Automatic Retraining

AI models need constant monitoring to remain secure and the accurate. Automatic drift detection techniques help to identify shifts in the input data, whereas performance metrics like recall and a precision flag concept drift. This monitoring also reveals the likelihood of malicious interference. To maintain effectiveness, organizations should apply automatic retraining when performance gradually decreases below an acceptable level. Incremental accuracy improvements can result in operational and business advantages.

Real World Example of AI Vulnerabilities

Researchers at North Carolina State University discovered that widely used deep neural networks in AI were extremely vulnerable to adversarial attacks. These attacks involve making imperceptible tweaks to input data, causing AI to misinterpret the output.

To expose these vulnerabilities in a structured way, the team developed a tool that could analyze trained AI’s decision-making and identify an accurate, minimal input arrangement that is capable of manipulating the model. They use this tool across all the four available of AI models and discovered that they were all easily manipulated.

This example highlights a crucial lesson: even reliable and a robust AI models are vulnerable to manipulation with a small modifications, rendering them unsafe for a certain applications. It also highlights the need for them.

• Enforce stringent adversarial testing in CI/CD.

• Continuous monitoring and defense measures to build resilience against such AI vulnerabilities.

• Utilize open-source security testing tools to a get complete visibility into weaknesses.

Image Source: R Street Institute

Final Thoughts

Overall, AI security is critical for any security team that deploys AI in the modern technology sphere. By integrating AI-powered Security solutions, security teams can enhance their security resilience through threat detection, lower response times and stay ahead of a emerging cybersecurity and API security threats. Akto has integrated the next-generation Agentic AI Suite to offer a comprehensive range of advanced AI-powered API security solutions.

AktoGPT utilizes OpenAI’s GPT to enhance API security and provide unmatched API protection. This integration enables automated response strategies, predictive threat analysis and optimization of a API security protocols, making them more then resilient and adaptive.

Apart from this, it has also introduced “GenAI Security Testing” to improve the security of AI and LLM’s Large Language Models. This feature makes use of AI-based testing methodologies and algorithms to effectively detect and mitigate vulnerabilities in a AI and LLM API’s. It helps address risks such as insecure output handling and prompt injection.

To discover more such AI-Powered API security features, connect with the security experts at Akto and Book a free demo right away!