Industries worldwide are seeing a revolution thanks to Artificial Intelligence (AI). AI systems are becoming increasingly common in sectors as diverse as Healthcare, Finance, Retail, Transportation, and Cybersecurity due to their ability to automate or streamline routine tasks, improve decision-making, and enhance efficiency and effectiveness. As organizations continue to utilize AI technologies in greater numbers, there is a corresponding rise in security-related concerns. Cybercriminals can target AI systems, since they present a brand-new type of cybersecurity vulnerability that traditional systems do not have.
Cybersecurity vulnerabilities concerning an AI system are weaknesses, flaws, or exploitable points within one (1) or more of an AI models’ Algorithms, Datasets, Infrastructure/s and/or its Deployment Environment. Cybercriminals can exploit these vulnerabilities to steal sensitive information, disrupt business operations, produce deceptive output from systems or entirely compromise the information systems of the organization. Therefore, because of this increase in the adoption of AI, it is critical for all businesses, developers, researchers, and Governments who develop/use/depend on Artificial Intelligence to understand Cybersecurity vulnerabilities in Artificial Intelligence technology.
This article will provide an overview of AI Security Vulnerabilities; Types of Vulnerabilities, Causes of Vulnerabilities, Real-World Examples of Vulnerabilities, Impacts of Vulnerabilities, and Best Practices for Preventing the Exploitation of Vulnerabilities.
AI Security Vulnerabilities Defined
AI systems depend on large volumes of Data, Large Scale Machine Learning Algorithms; Neural Networks; API (Application Programming Interface); Cloud Based Infrastructure and AI Applications. Each of the aforementioned components contains elements that can become attack surfaces. AI Systems have more potential attack surfaces than do traditional software systems, because, unlike traditional software vulnerabilities typically resulting from coding flaws, AI vulnerabilities may exist in Data; Model Behaviors; Trainability; and/or Logic Used to Make Decisions.
Typical vectors of an AI system consist primarily of the following five components: Training Data, Machine Learning Models, Feature Engineering Pipelines, API & Interfaces, Cloud Infrastructure, User Inputs, and 3rd Party Integrations. Attackers will target any of these components of an AI system to manipulate the results of the decision-making process or gain unauthorized access to an organisation’s network(s).
Advanced automated systems and generative AI tools are appearing rapidly and increasing security challenges to the AI community, and establishing many new risks. Organisations often choose innovative performance improvement over the security of their AI systems during development of those systems, providing attackers a greater opportunity to perpetrate an act against the organisation as a result of the gaps in security provided in the AI system.
Why are AI Systems Vulnerable?
AI systems do not rely purely on an algorithm that has a fixed logic for decision-making. They learn from data, and the learning through this data makes it very difficult to predict how the AI System will behave, establishing a unique vulnerability of an AI system.
There are some factors that contribute to the vulnerabilities of AI and AI security systems:
Heavy Data Dependence: AI models are found from large volumes of training data. If an attacker alters any volume of the training data, the model will not learn how to identify the appropriate characteristics of the pattern and will erroneously establish future patterns that will not be recognized as safe or legitimate.
Complexity of algorithms: Current AI models, in particular deep learning models, work on the Principal of a ‘black box.’ Developers may not fully understand the process by which decisions are made within a black box and therefore, it is not possible to anticipate and/or find vulnerabilities in the AI systems.
Many AI Systems are designed to Make Critical Decisions Automatically: An AI Model can, as a result of its design, cause significant damage or loss to an organisation without human intervention if the model is compromised.
Third Party Components: Numerous organizations utilize open-source frameworks, pre-trained models, APIs, and cloud services. Vulnerabilities in these external elements could potentially jeopardize the entire AI ecosystem.
No AI Security Standards: The security of AI continues to develop. Many businesses do not currently have adequate specifications for how to protect machine learning pipelines and infrastructure within their organization.
Common Types of AI Security Vulnerabilities
Data Poisoning
Data poisoning is a form of attack in which an adversary attempts to manipulate the training set of data used with a machine-learning model in order to manipulate its behavior. Machine-learning models learn directly from the data they use; therefore, using poisoned data will significantly affect the accuracy of the learning model.
For example, an attacker might insert bad images into a face recognition data set, which would lead to incorrect identification of the face based on poor-quality images.
Results of Data Poisoning:
- Reduced Accuracy
- Biases in Decision Making
- Hidden Backdoors
- Manipulated Predictions
Example:
In autonomous driving systems, if an attacker injects poisoned images of a stop sign into the training dataset for self-driving cars, the AI model may misinterpret a stop sign as a speed limit sign.
Adversarial Attacks
Adversarial attacks are a type of attack where the attacker has altered the input data very slightly to mislead the AI system, such that the alterations are typically not noticeable to a human but very effective in deceiving the machine-learning model.
For example, an attacker may change the color of a particular pixel in an image in order to confuse an AI-based image recognition system.
Types of Adversarial Attacks:
- Erasure Attacks
- Model Manipulation
- Adversarial Examples
- Physical Adversarial Attacks
Model Theft and Model Extraction: Attacks and Questions
Attacks on AI Models are often generated by an attacker querying a public API (whether through an application or an endpoint) and reconstructing the model behaviour, allowing for an attacker to take the AI Model and potentially use it for an unauthorised purpose. Therefore model theft is a substantial risk to organisations and individuals that produce and create works with AI.
Risks of model theft include the following:
- Loss of proprietary technologies
- Competitive disadvantage
- Financial loss
- Exposure of sensitive training data
Common methods used to steal an AI Model include:
- API abuse
- Reverse engineering
- Query-based extraction
Given the above risks to organisations using or providing AI-as-a-Service, it is reasonable that model theft continues to pose a growing risk.
Prompt Injection Attacks: Attacking Targeted Users/Chatbot Systems through Direct Manipulation
Prompt Injection Attacks are an additional attack vector that has only been a concern since AI and the ability to use a prompt to generate a response. An attacker’s primary goal with prompt injection is to either modify the systems prompt based instructions or retrieve confidential and sensitive information from the system.
Example: A malicious user can provide a directive to a chatbot to remove specific instructions, such as safety instructions, and provide confidential or sensitive information.
Risks associated with prompt injection attacks include:
- Periodic data leaks
- Harmful, abusive or detrimental outputs
- Erasing of security controls
- Unauthorised Actions
Prompt injection attacks have now become a significant risk due to the increase in the use of AI Assistants and Enterprise chatbots.
Data Leakage and Privacy Risk Associated with AI Applications
AI systems typically process an organisation or individual’s most highly retained forms of sensitive information, such as:
- Customer records
- Financial Data
- Medical information
- Personal conversation history
As a result of this, organisations or individuals with weak security controls are at high risk of extracting confidential information from AI models.
Causes: Examples of causes of issues with AI bias:
- APIs that are not secure
- Encryption that is weak
- Access controls that are poor
- Overfitting on sensitive data.
Membership Inference Attack (MIA): Attackers can actively identify whether or not the information of a particular individual has been used for training purposes. This creates serious implications for each individual’s privacy.
Bias Exploitation
AI systems could acquire biases through the training datasets that they were trained on. Attackers may purposely take advantage of those biases to influence results.
For example, manipulating various types of AI recruitment systems, influencing recommendation engines, and influencing discriminatory behavior.
All of these risk factors could cause negative brand reputation and create legal liabilities for organizations.
Supply Chain Vulnerabilities
AI development involves an extensive amount of open-source libraries and third-party tools.
Some examples of common supply chain vulnerabilities:
- Malicious dependencies
- Pre-Trained model(s) are tampered with
- Plug-in vulnerability
- Software updates that contain malware
Attackers may inject malicious code into AI-related frameworks and/or datasets.
Deepfake / Generative AI Abuse
One type of technology that is generating a lot of interest is generative AI. Generative AI is used to create images, video and audio recordings that appear to be authentic and real.
Potential security risks associated with generative AI;
- Identity Fraud
- Financial Scams
- Political misinformation
- Social engineering attacks
As generative AI technology continues to develop, keeping up with the sophistication of the generated content leads to increased difficulty in detecting.
Insider Threats to AI Systems
Employees with privileged access (employees, contractors, developers), either purposely or unintentionally, can pose threats to the AI system.
Examples of insider threats:
- Stealing training data
- Altering models
- Inserting malicious code
- Exposing API keys
Most organizations do not fully appreciate the risks associated with insider-related AI threats.
Best Practices for AI Security
Data Security for Training
Before training, ensure that your datset has been validated and monitored.
Recommended Actions
- Remove bad data
- Source data only from trusted sources
- Implement anomaly detection
Implementation of Access Controls
Only allow authorized personnel’s access to your AI systems, datasets, and APIs.
Security Controls
- Multi-Factor Authentication
- Role-Based Access Control
- API Security Gateways
Security Testing of AI is Required
AI systems must be tested continuously for adversarial attacks.
Testing Methods
- Penetration Testing
- Red Teaming
- Adversarial Testing
Data Encryption
Encryption protects your training data sets, models, and communications.
Where you need to encrypt
- At Rest Data
- In Transit Data
- Stored Model
Continuous Monitoring of AI Activity
Monitor for any unusual AI output or AI activity.
Monitoring Methods
- Real-Time Analytics
- Drift Detection
- Threat Intelligence Integration
Use of AI
Explainability is beneficial for developers to know how AI Models draw their conclusions.
Benefits
- Transparency
- Faster Vulnerability Find
- Better Compliance
API and Interface Protection
Most AI attacks occur through exposed APIs.
Measures for API Security
- Rate Limit
- Authentication Token
- Traffic Monitoring
- Input Validation
Supply Chain Security for AI
Ensure that any third-party libraries or models are secure.
Recommended Practices
- Dependency Scanning
- Software Bill of Materials (SBOM)
- Vendor Risk Assessment
Education of Employees on AI Security
Humans create the most risk associated with using AI systems.
Topics for Training
- Prompt Injection Awareness
- Social Engineering
Also Read:
The Dual-Edged Sword: Unveiling the Interplay Between Cybersecurity and AI
Lack Of API Visibility Undermines Basic Principle Of Security
