How adversarial AI threatens healthcare security systems

Adversarial AI is a technique that deliberately manipulates artificial intelligence systems by feeding them carefully crafted inputs designed to cause errors, bypass security measures, or produce incorrect outputs. Unlike traditional cyberattacks that target software vulnerabilities or human weaknesses, adversarial AI specifically exploits the way machine learning models process and interpret data.

These attacks work by taking advantage of the mathematical foundations underlying AI systems. Machine learning models make decisions based on patterns they've learned from training data, but they can be fooled by inputs that appear normal to humans while containing subtle modifications that cause the AI to misclassify or respond inappropriately. As noted in Cybersecurity Challenges in AI-Enabled Healthcare Systems by Anthony Musk, "AI algorithms, particularly deep learning models, are vulnerable to adversarial examples, which are imperceptible alterations to input data that deceive the AI model."

Healthcare AI security systems under attack

Healthcare organizations use AI-powered security systems for multiple functions. Network intrusion detection systems monitor for unusual traffic patterns that might indicate cyberattacks. Behavioral analytics platforms track user activities to identify potentially compromised accounts or insider threats. Fraud detection algorithms analyze billing patterns and insurance claims to spot suspicious activity. Email security systems use machine learning to identify phishing attempts and malicious attachments.

Each of these systems represents a potential target for adversarial manipulation. When attackers successfully poison or mislead these AI defenses, they can operate undetected while accessing sensitive patient information, disrupting medical services, or stealing valuable healthcare data. Musk warns that "This type of attack can lead to incorrect disease diagnoses, improper treatment recommendations, and, in extreme cases, fatal medical errors."

The healthcare sector faces challenges in AI adoption, with research showing that "more than 60% of healthcare professionals have expressed their hesitation in adopting AI systems due to a lack of transparency and fear of data insecurity" notes Towards secure and trusted AI in healthcare: A systematic review of emerging innovations and ethical challenges. This hesitancy shows concerns about the security vulnerabilities that adversarial attacks can exploit.

Chapter 4 of the Research Handbook on Health, AI and the Law states, "What makes AI medical devices unique is the kind of risks they pose, as the models and methods used to create and control them are not unique or novel." This observation extends beyond medical devices to include the broader healthcare AI security systems, where the consequences of compromised systems can directly impact patient care and safety.

The inadequacy of traditional defenses

A challenge facing healthcare organizations is that conventional cybersecurity approaches are insufficient against these sophisticated threats. As Musk emphasizes, "Traditional cybersecurity measures, such as firewalls and antivirus software, are insufficient to counter adversarial AI attacks, necessitating the development of AI-specific security solutions."

This limitation exists because adversarial AI attacks don't necessarily rely on traditional attack vectors like malware or network intrusions. Instead, they manipulate the AI systems' decision-making processes using inputs that appear legitimate to conventional security tools while fooling the AI models themselves.

Data poisoning

Healthcare security systems learn to recognize threats by analyzing large datasets of known attacks, normal network behavior, and suspicious activities. If attackers can inject malicious data into these training sets, they can compromise the AI system's ability to detect real threats.

For example, an attacker might gradually introduce network traffic patterns that look like normal administrative activities but actually represent data exfiltration techniques. Over time, as the AI system incorporates this poisoned training data, it learns to classify these malicious patterns as legitimate. When the attacker later uses these same techniques in a real breach, the compromised AI system fails to raise any alarms.

According to the systematic review, "Poisoning is known as the biggest threat to ML-based systems, and if many types of AI deployed in healthcare use such decision-making mechanisms, examining this specific vulnerability is paramount." Research has demonstrated that these attacks can be devastatingly effective: "The accuracy of ML models decreased by as much as 24 percent."

Data poisoning can also work in reverse, causing security systems to generate excessive false positives. By introducing legitimate activities disguised as threats in training data, attackers can make AI systems overly sensitive, leading to alert fatigue among security teams who eventually begin ignoring warnings altogether.

Model inversion and extraction attacks

Adversarial AI can also target the models themselves rather than their outputs. Model inversion attacks attempt to reverse-engineer training data from AI systems, potentially exposing sensitive information about the healthcare organization's security infrastructure, network topology, or even patient data if it was inadvertently included in training datasets.

Model extraction attacks go further by attempting to steal the AI model's functionality. Attackers send queries to security systems and analyze the responses to build their own copies of the AI models. With a stolen model, attackers can then test various evasion techniques offline until they find approaches that successfully fool the original system. According to the systematic review, "Unlike social engineering attacks, there are no major mitigation measures, and the risks will always exist if the AI is built to respond to inputs." This vulnerability shows why extraction attacks represent such a persistent concern for AI security systems.

The human factor

The human element remains a weak point in healthcare cybersecurity. As highlighted in Cybersecurity Challenges in AI-Enabled Healthcare Systems, "Healthcare staff frequently lack sufficient cybersecurity training, making them susceptible to social engineering tactics." This vulnerability is concerning in healthcare environments where staff may prioritize patient care over security protocols during critical situations.

The problem is exacerbated by the fact that healthcare professionals often lack awareness of AI-specific threats. Traditional security training may not prepare staff to recognize the signs of adversarial AI attacks, which can appear as system quirks or false positives rather than deliberate attacks.

Regulatory and compliance challenges

Musk notes, "Compliance-based security models fail to address AI-specific risks, necessitating dynamic cybersecurity policies that incorporate real-time threat intelligence and adaptive defense mechanisms."

This regulatory gap means that healthcare organizations may believe they are protected by following existing compliance requirements like HIPAA, when in reality they remain vulnerable to AI-specific attack vectors that these frameworks don't address.

The amplification effect

Healthcare organizations are interconnected, sharing data and resources across networks of hospitals, clinics, research institutions, and technology vendors. A successful adversarial AI attack against one organization's security systems could provide attackers with insights and access that enable them to compromise entire healthcare networks.

The Research Handbook on Health, AI and the Law emphasizes this concern, noting that "Instead of just attacking a local network or device, the attacker can cause damage to the model, or the AI service used by the medical devices, causing damage at a larger scale instead of locally." 

Additionally, the high-stakes nature of healthcare operations means that security teams often face pressure to minimize disruptions to patient care. This environment can make organizations more tolerant of security system quirks or false positives, potentially allowing adversarial attacks to go unnoticed for extended periods.

Real-world vulnerabilities

The theoretical risks of adversarial AI are not merely academic concerns—they represent present and immediate threats to healthcare security systems. Recent research by Elnawawy at the University of British Columbia provides evidence of these vulnerabilities through systematic analysis of FDA-approved medical devices and practical attack demonstrations.

Systematic vulnerabilities across medical devices 

A study of 20 FDA-approved AI/ML-enabled medical devices across 13 physiological panels revealed widespread vulnerability to adversarial attacks. The research found that virtually all devices using machine learning algorithms are susceptible to inference-time attacks, with several also vulnerable to training-time attacks that could affect large numbers of patients.

Particularly concerning is that many of these devices are used in scenarios where medical practitioners have limited time to verify outputs. For example, the NuVasive Pulse System used during spinal surgeries for neurophysiological monitoring, or emergency diagnostic tools like the Cardiologs ECG Analysis Platform. In these high-stakes environments, a successful adversarial attack could lead to fatal medical errors.

Demonstrated attack on blood glucose management systems 

Researchers successfully demonstrated a practical adversarial attack against an ML-enabled blood glucose management system (BGMS), similar to the FDA-approved systems used by diabetic patients. The attack exploited Bluetooth communication vulnerabilities between continuous glucose monitors and mobile apps, allowing attackers to manipulate glucose readings sent to the AI decision-making engine.

The attack achieved success rates of up to 100% in forcing the system to misdiagnose patients as hyperglycemic when they actually had normal or low blood glucose levels. Such misdiagnosis could lead to dangerous insulin overdoses with potentially fatal consequences. The research demonstrated that individual patients showed varying levels of vulnerability, with some proving more resilient to attacks than others based on their medical history and data patterns.

Widespread security assessment failures 

The research revealed that over 80% of ML-enabled medical device manufacturers either provided no information about security assessments or employed inadequate evaluation methods. Only 12% utilized existing risk assessment techniques, and even these proved insufficient for addressing the unique challenges of AI-enabled connected medical systems.

This systematic failure in security assessment means that the medical devices currently protecting patient health and data may harbor vulnerabilities that remain undetected and unmitigated.

FAQs

How do adversarial AI attacks differ from conventional cyberattacks in healthcare?

They manipulate the way AI models interpret data rather than exploiting software code or human error.

Can adversarial AI affect patient-facing medical devices directly?

Yes, attacks on AI-powered devices like glucose monitors or ECG analysis tools can alter readings and impact patient care.

What role does data quality play in preventing adversarial attacks?

High-quality, well-curated datasets reduce vulnerabilities but cannot fully eliminate the risk of manipulation.