A clinical AI tool can read patient records, summarize encounters, interpret medical images, answer clinical questions, recommend actions, and connect to systems containing protected health information.
That is exactly why “the AI works” is no longer enough.
The real question is:
What happens when someone deliberately tries to break it?
Imagine a clinical AI assistant connected to an EHR. An attacker does not necessarily need to breach the EHR directly. They may be able to manipulate a prompt, poison a document the AI retrieves, exploit an API, abuse the AI’s permissions, or persuade an agent to perform an action it was never supposed to perform.
One vulnerability can potentially turn an AI interface into a pathway to sensitive clinical data or connected healthcare systems.
And this is no longer a hypothetical concern. The FDA has authorized over 1,600 AI-enabled medical devices for marketing in the United States as of September 2026, highlighting how deeply AI is becoming embedded in clinical and medical environments. At the same time, healthcare organizations are dealing with a rapidly expanding AI attack surface that traditional penetration testing was not designed to fully address.
Accorian helps organizations identify and validate these risks through AI Security Assessments, AI Chatbot Penetration Testing, LLM Security Testing, Prompt Injection Testing, AI Red Teaming, Agentic AI Security Assessments, AI Threat Modeling, Third-Party AI Security Validation, AI Risk Assessments, and AI Security Governance. Because for clinical AI, finding a vulnerability is only the beginning.
The important question is:
Can an attacker turn that vulnerability into patient-data exposure, unauthorized access, privilege escalation, or an unsafe clinical action?
Why Is Clinical AI a Different Security Problem?
A clinical AI system is rarely just a model. It can look more like this:
Patient data → EHR → API → AI application → LLM → RAG/knowledge base → clinical output → downstream system
Every connection introduces another potential attack path. Consider a clinical documentation assistant. A clinician asks it to summarize a patient’s encounter. The AI retrieves information from the patient’s record, processes it through an LLM, and generates documentation. Now imagine an attacker manages to insert malicious instructions into content that the AI later retrieves.
The attack could become:
Malicious content → indirect prompt injection → AI instruction manipulation → unauthorized retrieval → sensitive information exposure
Or consider an AI agent that can interact with healthcare APIs:
Prompt injection → agent manipulation → privileged API call → unauthorized action
This is why testing only the chatbot interface is not enough.
You need to test the entire AI attack path.
What Can Go Wrong With a Clinical AI Tool?
The biggest mistake healthcare organizations can make is treating AI security as a generic application-security problem. A clinical AI system introduces security questions that are specific to how AI interprets instructions, retrieves information, generates outputs, and interacts with other systems.
An attacker can manipulate what the AI sees
Prompt injection allows an attacker to introduce instructions designed to override or interfere with the AI’s intended behavior. With clinical AI, this could involve prompts, uploaded documents, emails, web content, clinical notes, knowledge bases, or other information consumed by the system.
The question isn’t simply:
“Can prompt injection work?”
It is:
“What can prompt injection make the AI do?”
That distinction matters.
The AI may expose information it should never reveal
Clinical AI may have access to highly sensitive information, including:
- Patient records
- Diagnoses
- Medications
- Clinical notes
- Medical images
- Insurance information
- Contact information
- Credentials and API tokens
- Internal clinical guidance
- System instructions
Security testing needs to determine whether an attacker can extract information outside their authorization boundary.
RAG can become an unexpected data-exfiltration path
Retrieval-augmented generation can make clinical AI significantly more useful. It can also make the attack surface significantly larger. A healthcare organization may connect an AI assistant to clinical guidelines, internal policies, patient records, research repositories, or enterprise knowledge bases. The critical security question becomes:
Can the AI retrieve information simply because it exists, or only because the user is authorized to access it?
An effective RAG security assessment should test:
- Retrieval authorization
- Document-level permissions
- Tenant isolation
- Vector database security
- Malicious document injection
- Retrieval manipulation
- Sensitive-data leakage
- Cross-user information exposure
A secure application with an insecure retrieval layer can still expose sensitive information.
What Should an AI Security Assessment Test?
A meaningful clinical AI assessment should be based on the architecture and actual attack paths, not a generic checklist.
1. Prompt injection and jailbreaks
Test whether an attacker can:
- Override system instructions
- Manipulate model behavior
- Circumvent safety controls
- Extract restricted information
- Force unintended outputs
- Chain multiple prompts to bypass safeguards
For clinical systems, testing should include both direct and indirect prompt injection. Indirect injection is particularly important when AI consumes external content.
2. Sensitive information disclosure
Attempt to determine whether the AI can be manipulated into revealing:
- PHI
- PII
- Patient records
- Internal instructions
- Secrets
- Credentials
- Proprietary information
- Data belonging to another user or patient
Testing should validate whether existing authorization controls remain effective when information is accessed through the AI layer.
3. AI agent and tool security
If the clinical AI can call tools, APIs, databases, or other applications, test what happens when the AI is manipulated.
For example:
- Can the AI access a patient’s entire record when it only needs a subset?
- Can it call an API using excessive privileges?
- Can it modify a record?
- Can it send an external message?
- Can it execute a workflow without human approval?
This is where AI Red Teaming and Agentic AI Security Assessments become particularly valuable. The objective is to identify whether an attacker can move from manipulating the model to compromising a connected system.
4. API and application security
Clinical AI still has conventional attack surfaces. Assess:
- Authentication
- Authorization
- Session management
- API security
- Input validation
- Access controls
- Privilege escalation
- Cloud configuration
- Application vulnerabilities
- Third-party integrations
AI-specific testing should complement, not replace, traditional application and API penetration testing.
5. RAG and knowledge-base security
Test whether an attacker can manipulate the information the model retrieves. This includes testing for:
- Knowledge-base poisoning
- Malicious documents
- Unauthorized retrieval
- Cross-patient data exposure
- Cross-tenant access
- Vector database weaknesses
- Retrieval manipulation
6. Model and data integrity
Where applicable, assess whether an attacker can manipulate training, fine-tuning, retrieval, or other data sources that influence model behavior. For medical AI, integrity is especially important because manipulated inputs can potentially influence clinical outputs.
7. Output and downstream security
Never stop testing at the AI response. Ask what happens after the model produces the output. Does the output get:
- Written into an EHR?
- Sent to a clinician?
- Used to trigger an API?
- Passed to another AI model?
- Used to generate a prescription workflow?
- Stored as clinical documentation?
- Sent to a patient?
An unsafe AI output becomes significantly more dangerous when another system automatically trusts and acts on it.
What Does Accorian Test in a Clinical AI Security Assessment?
Accorian’s approach is designed around the AI system’s actual architecture and attack paths rather than treating every AI application the same. Depending on the environment, testing can include:
- AI Security Assessment: Evaluate the AI application’s overall technical security posture, including models, data, APIs, integrations, RAG, application controls, and supporting infrastructure.
- AI Chatbot Penetration Testing: Test conversational AI for prompt injection, data exposure, LLM-specific weaknesses, application vulnerabilities, privilege escalation, and unauthorized actions. Accorian’s methodology combines chatbot-specific testing with traditional application and API testing.
- LLM Security Testing: Assess the LLM-powered application for vulnerabilities such as prompt injection, jailbreaks, system prompt leakage, sensitive information disclosure, and model-specific attack scenarios.
- Prompt Injection Testing: Attempt to manipulate the AI through direct and indirect instructions, including malicious content introduced through documents, knowledge bases, and other data sources.
- AI Red Teaming: Move beyond isolated vulnerabilities and simulate realistic attacker objectives to determine whether multiple weaknesses can be chained into a meaningful compromise.
- Agentic AI Security Assessment: Test AI agents that can access tools, APIs, databases, or other systems for excessive permissions, tool misuse, privilege escalation, and unauthorized actions.
- AI Threat Modeling: Map the AI architecture, data flows, trust boundaries, users, integrations, attack surfaces, and potential abuse cases before testing begins.
- Third-Party AI Security Validation: Evaluate AI platforms, models, vendors, and third-party solutions before they are introduced into a healthcare environment.
- AI Risk Assessment: Identify broader security, privacy, regulatory, operational, ethical, and business risks associated with the AI use case. Accorian distinguishes this from an AI Security Assessment, which focuses specifically on technical vulnerabilities and exploitable attack paths.
- AI Security Governance: Help organizations establish the controls and governance needed to manage AI security beyond an individual penetration test.
What Would a Real Clinical AI Attack Path Look Like?
This is where a security assessment becomes much more valuable than a compliance checklist. Consider an AI assistant that can retrieve clinical information. A tester might begin with a seemingly harmless prompt.
Step 1: Attempt to override the AI’s instructions.
Step 2: Identify whether internal instructions can be extracted.
Step 3: Determine whether the AI has access to restricted information.
Step 4: Introduce an indirect prompt injection through retrieved content.
Step 5: Attempt to influence the AI’s retrieval behavior.
Step 6: Test whether authorization controls remain effective.
Step 7: Determine whether sensitive information can be extracted.
Step 8: If the AI has tools, test whether the compromised context can trigger an unauthorized API or workflow.
The finding isn’t simply:
“Prompt injection exists.”
The meaningful finding is:
“Prompt injection can be chained with an authorization weakness to retrieve information outside the user’s permitted clinical-data boundary.”
That is the difference between identifying an AI weakness and understanding its real security impact.
What Are AI Security Assessments Finding in Production AI?
Accorian’s own testing illustrates why organizations should validate their AI controls rather than assume their guardrails are sufficient. Across more than 100 real-world AI chatbots assessed by Accorian:
- 82% showed prompt injection exposure
- 61% exposed internal instructions
- 49% allowed jailbreak bypass
- 35% exposed PII
These figures represent Accorian’s assessed sample, not the prevalence of vulnerabilities across all AI systems.
The takeaway for healthcare organizations is straightforward:
Do not assume that a clinical AI system is secure because it has guardrails. Test whether those guardrails survive an attack.
AI Security Assessment vs. AI Risk Assessment: Do You Need Both?
Yes, in many clinical AI environments.
They answer different questions.
AI Risk Assessment:
What could go wrong with this AI use case?
It considers security, privacy, regulatory, operational, ethical, business, and other risks.
AI Security Assessment:
Can an attacker actually exploit the technical weaknesses associated with those risks?
For example:
Risk assessment: Unauthorized access to patient information is a high-risk scenario.
Security assessment: Test whether prompt injection, broken authorization, RAG weaknesses, or API abuse can actually produce unauthorized access.
A mature program can connect the two:
AI Risk Assessment → Threat Model → Security Controls → Technical Testing → Remediation → Retesting → Continuous Monitoring
That is far more actionable than performing a risk assessment once and filing the report away.
How Does Clinical AI Security Testing Support Healthcare Compliance?
Security testing should not be treated as a replacement for compliance. Instead, it provides technical evidence that can support broader security and risk-management activities. Depending on the organization and use case, clinical AI programs may need to consider requirements and guidance associated with:
- HIPAA
- HITRUST
- FDA cybersecurity expectations
- NIST AI RMF
- ISO/IEC 42001
- ISO/IEC 42005
- OWASP LLM and GenAI security guidance
- Applicable state and federal AI requirements
The FDA continues to evolve its approach to AI-enabled medical devices, including considerations around AI-enabled device software functions and lifecycle management. Accorian’s AI practice combines technical AI security with AI risk and governance services, including HITRUST for AI Systems, NIST AI RMF, ISO 42001, ISO 42005, AI Risk Assessment, and AI Security Governance.
When Should You Test a Clinical AI Tool?
Waiting until after production deployment is a poor security strategy. A clinical AI security assessment should be considered:
Before launch
Identify exploitable weaknesses before clinicians or patients depend on the system.
Before connecting to sensitive healthcare systems
Especially before connecting AI to EHRs, patient databases, clinical repositories, or privileged APIs.
Before granting additional permissions
Every new tool or integration can introduce a new attack path.
After major model or architecture changes
Changing the model, prompt architecture, RAG pipeline, APIs, tools, or permissions can change the security posture.
After an incident
Determine whether the vulnerability was isolated or part of a broader attack path.
Continuously for high-risk systems
AI systems change. Models change. Prompts change. Data changes. Integrations change.
Your security assessment should evolve with them.
How Should Healthcare Organizations Choose an AI Security Testing Provider?
Do not select a provider simply because it offers “AI penetration testing.” Ask five questions.
Can they test AI-specific vulnerabilities?
Look for demonstrated experience with:
Prompt injection, jailbreaks, LLM security, RAG security, AI agents, excessive agency, data leakage, and AI-specific attack chains.
Can they test the surrounding application?
Your provider should understand APIs, authentication, authorization, cloud environments, databases, identity, and integrations.
Can they demonstrate exploitation?
A finding that says “prompt injection is possible” is less useful than evidence showing exactly what an attacker can achieve.
Can they test healthcare-specific data flows?
For clinical AI, the provider should understand the consequences of PHI exposure, authorization failures, clinical-data manipulation, and integrations with healthcare systems.
Can they help you remediate?
The objective should not be a PDF full of vulnerabilities.
It should be:
Find → Exploit → Prioritize → Remediate → Retest
What Should the Final AI Security Assessment Report Tell You?
A useful report should answer five questions:
What is vulnerable?
Identify the affected AI component, application, API, data source, or integration.
How can it be exploited?
Provide the attack path and evidence.
What can an attacker achieve?
Translate technical weaknesses into actual security and business impact.
How serious is it?
Prioritize findings based on exploitability, affected assets, data sensitivity, privileges, and potential impact.
How do we fix it?
Provide practical remediation guidance and validate the fix through retesting.
For clinical AI, the report should make it possible for security, engineering, compliance, and clinical technology teams to understand the same risk from their respective perspectives.
Why Accorian for Clinical AI Security?
Clinical AI security requires more than testing whether an LLM can be tricked. It requires understanding how the AI connects to patient data, applications, APIs, users, knowledge repositories, clinical workflows, and downstream systems. Accorian brings AI security testing together with broader cybersecurity, compliance, and assurance expertise. Its AI security services include:
- AI Security Assessments
- AI Chatbot Penetration Testing
- LLM Security Testing
- Prompt Injection Testing
- AI Red Teaming
- Agentic AI Security Assessments
- AI Threat Modeling
- Third-Party AI Security Validation and Vendor Risk Assessment
- AI Risk Assessments
- AI Security Governance
- HITRUST for AI Systems
- NIST AI RMF
- ISO 42001
- OWASP LLM and Generative AI Security
Accorian’s current AI security practice is designed to assess in-house AI, third-party AI tools, and open-source LLM environments, while connecting technical security testing with risk and governance. For organizations that need to operationalize those controls and risks after the assessment, GORICO, Accorian’s AI-enabled GRC platform, can centralize risks, controls, evidence, and compliance workflows.
Don’t Wait for Clinical AI to Become the Attack Path
A clinical AI system can be accurate, useful, compliant on paper, and still be exploitable.
The question is not:
“Does our AI work?”
It is:
“Can an attacker manipulate the AI into doing something it was never supposed to do?”
- Can they retrieve another patient’s information?
- Can they bypass authorization?
- Can they poison the knowledge base?
- Can they manipulate a clinical workflow?
- Can they turn an AI agent into a privileged proxy?
- Can they move from the AI layer into the systems behind it?
Those are the questions an AI security assessment should answer.
If your organization is deploying a clinical AI assistant, AI medical device, healthcare chatbot, clinical copilot, RAG application, or AI agent, security testing should happen before attackers discover the same attack paths.
Test the AI. Test the attack path. Test what happens when the guardrails fail.
Ready to test your clinical AI?
Accorian can help assess your AI application, LLM, chatbot, RAG architecture, AI agent, APIs, integrations, and supporting infrastructure through targeted AI Security Assessments, AI Chatbot Penetration Testing, LLM Security Testing, AI Red Teaming, Prompt Injection Testing, and Agentic AI Security Assessments.
Talk to Accorian to define the right AI security assessment scope for your clinical AI environment.
