Machine learning has become an important part of modern cybersecurity. Security teams use machine learning to analyze network traffic, detect unusual behavior, identify suspicious files, and process enormous amounts of security data much faster than traditional methods can manage.
But there is a catch.
The technology designed to make cybersecurity smarter can also become a target.
Attackers are increasingly interested in the data, models, applications, and infrastructure that support machine learning systems. Instead of attacking only traditional software, criminals can look for ways to manipulate the information a model learns from, trick its predictions, extract sensitive information, or exploit weaknesses in the systems surrounding it.
These risks are generally discussed under the broader field of adversarial machine learning. The National Institute of Standards and Technology (NIST) describes adversarial machine learning as an area concerned with attacks that exploit the data-driven nature of machine learning systems and identifies major categories such as poisoning, evasion, privacy, and misuse attacks.
Understanding these threats is becoming increasingly important as organizations use machine learning in security operations, fraud detection, customer applications, healthcare, finance, and other business-critical environments.
What Are Machine Learning Cyber Threats?
Machine learning cyber threats are attacks or security risks that target machine learning systems, their supporting infrastructure, or the data they depend on.
Traditional cybersecurity focuses heavily on protecting networks, applications, devices, accounts, and data. Machine learning adds another layer to that environment because the system’s behavior can depend on training data, model architecture, model parameters, inputs, and outputs.
A simple example can help explain the difference.
Imagine a company uses a machine learning model to identify potentially malicious network activity. The model has learned from thousands of examples of normal and suspicious behavior.
An attacker who understands how the detection system works may attempt to make malicious activity appear normal.
The attacker does not necessarily need to break into the security software directly. Instead, they may try to influence what the model sees or how it interprets the available information.
That is what makes machine learning security different from many traditional security problems.
Why Are Machine Learning Systems Becoming a Target?
Machine learning systems depend heavily on data.
That dependence creates opportunities for attackers.
A typical machine learning environment can involve:
- Training datasets
- Data collection systems
- Data pipelines
- Machine learning models
- APIs
- Cloud infrastructure
- Applications
- User inputs
- Model outputs
- Access credentials
- Third-party services
Each component can introduce security considerations.
NIST’s current adversarial machine learning taxonomy specifically examines attacks across different stages of the AI lifecycle and considers attacker goals, capabilities, and knowledge of the learning process.
As organizations connect machine learning models to real-world applications, protecting only the model itself is therefore not enough.
The surrounding ecosystem also needs attention.
1. Data Poisoning Attacks
One of the most important machine learning cyber threats is data poisoning.
Data poisoning occurs when an attacker attempts to manipulate the information used to train a machine learning model.
The idea is relatively simple: if a model learns from unreliable or deliberately manipulated information, its future decisions may also become unreliable.
For example, suppose a cybersecurity company trains a model using historical examples of malicious and legitimate network activity. If an attacker manages to introduce misleading examples into the training process, the resulting model could learn patterns that make certain threats harder to identify.
The challenge is that the attack can happen before the model is deployed.
Security teams may believe they are working with a trusted model while the underlying training data has already been compromised.
How Organizations Can Reduce Data Poisoning Risk
Organizations can reduce exposure by:
- Controlling who can modify training datasets
- Validating data before it enters training pipelines
- Monitoring unexpected changes in datasets
- Maintaining version histories
- Keeping reliable audit records
- Separating trusted and untrusted data sources
- Testing models against manipulated datasets
Training data should be treated as a security-sensitive asset rather than simply another business dataset.
2. Adversarial Evasion Attacks
Another major threat involves evasion.
In an evasion attack, an attacker modifies an input so that a machine learning model produces an incorrect classification or fails to detect something it should recognize.
The important point is that the attacker may not need to change the model itself.
Instead, they attempt to change what the model sees.
In cybersecurity, this could involve modifying malicious activity so that an automated detection system considers it less suspicious.
NIST identifies evasion as one of the major attack categories in adversarial machine learning.
This creates a difficult problem for security teams because a model that performs well during normal testing may behave differently when deliberately targeted by an attacker.
3. Model Theft and Extraction
Machine learning models can represent significant investments.
Organizations may spend substantial amounts of time and computing resources building, training, testing, and deploying their models.
Attackers may therefore attempt to reproduce or extract information about a model through repeated interaction with a system.
For example, if an organization exposes a machine learning model through an API, an attacker could potentially send many carefully selected inputs and study the resulting outputs.
Over time, those interactions may reveal information about how the model behaves.
Model extraction can create both intellectual-property and cybersecurity concerns.
It can also give attackers a better understanding of the system they are attempting to bypass.
4. Privacy and Information Leakage
Machine learning systems can process sensitive information, including customer records, financial information, employee data, medical information, and confidential business material.
That makes privacy another important concern.
An attacker may attempt to learn sensitive information from model behavior or from weaknesses in the system surrounding the model.
The risk becomes more significant when machine learning systems are connected to internal databases, enterprise documents, or other confidential resources.
NIST’s 2025 taxonomy includes privacy attacks among the recognized categories of adversarial machine learning threats.
Organizations therefore need to consider not only whether a model produces accurate results, but also whether its outputs could unintentionally expose information.
5. Backdoor and Trojan Attacks
Backdoor attacks introduce hidden behavior into a machine learning system.
A compromised model may appear to work normally under ordinary conditions while behaving differently when a particular trigger or pattern appears.
This makes backdoor attacks especially challenging.
Traditional testing may show that the model performs well because the malicious behavior remains hidden until a specific condition is met.
NIST includes backdoor and trojan-related threats within its broader adversarial machine learning research and taxonomy.
Organizations should therefore consider the origin of models, datasets, libraries, and other components used throughout the machine learning supply chain.
6. Attacks Through Machine Learning APIs
Many modern machine learning systems are not isolated.
They are connected to websites, mobile applications, internal platforms, APIs, cloud services, and other software.
This connectivity can create additional attack surfaces.
An insecure API could expose information about the model, allow excessive requests, or provide an attacker with opportunities to study how the system responds to different inputs.
For this reason, machine learning security should include normal application-security practices such as:
- Strong authentication
- Authorization controls
- Rate limiting
- Secure API design
- Logging
- Monitoring
- Input validation
- Regular security testing
The model may be sophisticated, but the application around it still needs basic cybersecurity protection.
7. Supply Chain Risks
Machine learning systems rarely depend on a single component.
Organizations may use open-source libraries, pretrained models, external datasets, cloud platforms, third-party APIs, and other external resources.
Every dependency introduces another point that needs to be evaluated.
A compromised third-party component could potentially introduce vulnerabilities before an organization even deploys its machine learning application.
This is why machine learning security should include supply chain visibility.
Organizations should know:
- Where models came from
- Where training data originated
- Which libraries are being used
- Which external services are connected
- Who maintains important dependencies
- How updates are reviewed
How Machine Learning Threats Can Affect Cybersecurity
The consequences of a successful attack can vary depending on the role of the machine learning system.
For a cybersecurity detection model, an attack could result in missed alerts or incorrect classifications.
For a fraud detection system, manipulated inputs could potentially affect how suspicious transactions are evaluated.
For a recommendation or decision-making system, manipulated data could influence outputs.
For systems handling sensitive information, privacy attacks could expose information that should remain protected.
The impact therefore depends heavily on what the machine learning system does and how closely it is connected to important business processes.
How Organizations Can Protect Machine Learning Systems
There is no single tool that can eliminate every machine learning security risk.
Instead, organizations need a layered approach.
Protect Training Data
Control access to training datasets and monitor changes carefully.
Data validation should happen before information enters important training pipelines.
Test Models Against Adversarial Inputs
Security testing should not focus only on normal inputs.
Teams should also evaluate how models behave when inputs are intentionally manipulated.
Monitor Model Behavior
Unexpected changes in model performance, outputs, or usage patterns can provide useful warning signs.
Monitoring should cover both the model and the infrastructure around it.
Apply Strong Access Controls
Only authorized users and applications should be able to access sensitive datasets, models, and management systems.
Least-privilege access can help reduce unnecessary exposure.
Secure APIs and Applications
Machine learning APIs should receive the same security attention as other production applications.
Authentication, authorization, logging, rate limits, and monitoring are important controls.
Maintain Human Oversight
Automation can help security teams process large volumes of information, but important decisions should not always depend entirely on an automated model.
Human review can provide an additional layer when the consequences of an incorrect decision are significant.
Keep Models and Dependencies Updated
Organizations should monitor vulnerabilities in the software, frameworks, libraries, and infrastructure used by their machine learning systems.
Security reviews should be part of the model lifecycle rather than something performed only after deployment.
Why Machine Learning Security Needs Continuous Monitoring
A machine learning system is not necessarily secure simply because it passed a security test during deployment.
Threats change.
Data changes.
Models change.
Applications change.
Attackers change their techniques.
NIST’s current adversarial machine learning work emphasizes that the field continues to evolve and identifies ongoing challenges across the AI lifecycle.
Continuous monitoring therefore becomes an important part of machine learning security.
Organizations should watch for unusual data patterns, unexpected model behavior, suspicious API activity, unauthorized access, and changes in model performance.
Regular testing can also help identify weaknesses before attackers discover them.
The Future of Machine Learning Cybersecurity
Machine learning will continue to play an important role in cybersecurity.
It can help organizations analyze massive amounts of information, detect patterns, prioritize alerts, and support security teams in investigating suspicious activity.
At the same time, attackers have strong incentives to study these systems and search for weaknesses.
This creates a continuous security cycle.
Defenders develop better models. Attackers develop new ways to manipulate them. Security researchers identify weaknesses and develop mitigation techniques. Organizations then update their systems and security processes.
The goal is not to eliminate machine learning from cybersecurity.
The goal is to make machine learning systems more resilient.
Conclusion
Machine learning is giving cybersecurity teams new ways to detect and respond to threats, but it also introduces security challenges that organizations need to understand.
Data poisoning, adversarial evasion, model extraction, privacy attacks, backdoors, API vulnerabilities, and supply chain risks can affect different parts of the machine learning lifecycle.
The most important lesson is that a machine learning model should not be treated as a security solution that exists outside the normal cybersecurity environment.
It is part of that environment.
Organizations need to protect the data behind the model, secure the infrastructure around it, test how it behaves under attack, monitor its performance, and maintain appropriate human oversight.
As machine learning becomes more deeply integrated into business and cybersecurity operations, building security into the entire machine learning lifecycle will become increasingly important.
Frequently Asked Questions
1. What are machine learning cyber threats?
Machine learning cyber threats are attacks that target machine learning models, training data, applications, or supporting infrastructure. Attackers may manipulate data, deceive models, steal information, or exploit weaknesses in connected systems.
2. What is a data poisoning attack?
A data poisoning attack occurs when an attacker introduces manipulated or misleading information into the data used to train a machine learning model. This can affect what the model learns and may reduce the reliability of its future predictions.
3. How can organizations protect machine learning systems?
Organizations can protect machine learning systems by securing training data, controlling access, testing models against adversarial inputs, monitoring model behavior, securing APIs, updating dependencies, and maintaining appropriate human oversight.
4. Why is machine learning security important for cybersecurity?
Machine learning is increasingly used to detect threats and support security operations. Protecting these systems is important because attackers may attempt to manipulate their data, inputs, models, or surrounding infrastructure to avoid detection or gain access to sensitive information.
