AI Medical Device Security and Penetration Testing
Scope security testing for your AI-enabled medical device, inference endpoints, connected APIs, and model supply chain. We agree relevant attack paths, test access, evidence, exclusions, and a fixed fee before work begins. Your manufacturer team retains clinical validation and regulatory decisions.
See also: Medical Device Penetration Testing Services
The short answer
How do you secure an AI/ML-enabled medical device for the FDA?
AI medical device penetration testing examines how an attacker could abuse the model, its inputs and outputs, and the connected system. Testing an AI-enabled device is different from using AI tools to run a penetration test. Blue Goat scopes relevant AI security checks alongside web, API, cloud, and other device testing. Deliverables include a threat-traced Test Plan, Test Cases, and Test Report. Model access and representative non-clinical test data shape coverage. Clinical performance validation is outside this security engagement.
275+ FDA submissions supported, zero cybersecurity rejections.
- Threat-traced AI security tests
- Inference, API, and cloud scope
- Fixed fee after scoping
- Manufacturer-owned decisions
- Fixed-fee quote in 24 hours
- NDA available on request
- Agreed access and exclusions
What should an AI security testing proposal cover?
A generic application scan does not establish coverage of the model, training pipeline, or inference endpoints. Ask for the scope and evidence behind each claim.
The connected system, not only the model
Check access controls, inference endpoints, APIs, cloud boundaries, and model-update paths against the device threat model. Define which builds and suppliers are in scope.
Reproducible security evidence
Ask for the tested configuration, attack steps, observed results, limitations, and remediation verification. Tool output alone does not establish a verified finding.
Published AI-device testing evidence
Our anonymized cloud SaMD case documents web, API, and AI inference testing with threat traceability and retest evidence. It does not establish coverage of every AI modality.
510(k) AI SaMD Penetration Testing Case StudyWhich AI security checks are relevant to your device?
The threat model, architecture, model access, deployment, and intended use determine the agreed checks. Not every AI device includes an LLM or exposes a training pipeline.
Inference and connected interfaces
- Authentication, authorization, and patient-data isolation
- Malicious input and file-upload handling
- Insecure output handling and downstream trust boundaries
- Model extraction and sensitive-information disclosure as scoped
Training and model-update boundaries
- Who can alter training data, labels, or model artifacts
- Model provenance and integrity controls
- Training-data poisoning scenarios where access permits
- Model-release and update authorization
LLM features, when present
- Direct and indirect prompt injection
- Sensitive-data disclosure and output handling
- Tool-call permissions and external connections
- Attack evidence traced to the agreed system threats
AI/ML-specific attack surface
AI/ML devices add surfaces traditional pen tests miss: model weights, training data integrity, inference paths, and the PCCP cybersecurity boundary. Each is covered explicitly.
- 01Training-data pipeline + provenance
- 02Model weights (storage, transit, load-time integrity)
- 03Inference container / runtime
- 04Input pre-processing + validation
- 05Output post-processing + plausibility checks
- 06Model-update channel (PCCP boundary)
- 07Telemetry / monitoring path back to cloud
- 08Clinician + cloud APIs
Layers shown outermost (top) to innermost (bottom). Dashed rows are part of the surrounding system but out of scope for this view.
From AI threat model to retest evidence
Agree scope before testing. Keep security evidence separate from manufacturer-owned clinical performance and release decisions.
- 01
Review the system and access
Review the architecture, threat model, AI components, supplier boundaries, and representative non-clinical test environment. Record missing inputs and exclusions.
- 02
Agree the Test Plan and Test Cases
Map relevant attacks to documented threats and controls. Identify model versions, test data, tooling, rules of engagement, and expected secure behavior.
- 03
Execute and verify security tests
Test the agreed AI and connected-system surfaces. Record reproduction steps, observed behavior, findings, limitations, and clean results.
- 04
Report and retest
Deliver the Test Plan, Test Cases, and Test Report. Your engineers implement fixes; we verify remediation within the agreed scope. The manufacturer decides risk acceptance.
- 05
Define separately scoped lifecycle work
Discuss threat-model maintenance, SBOM work, security monitoring, QMS procedures, and optional PCCP cybersecurity content when needed. These are not automatically included in a standalone test.
Reviewer-ready deliverables in one engagement
Every engagement ships with documented artifacts, traceable, complete, and aligned with current guidance.
- Adversarial ML testing (evasion, poisoning, model inversion, prompt injection)
- PCCP authoring and FDA AI/ML transparency artifacts
- Model lifecycle, monitoring, and drift controls
- GMLP + AAMI CR34971 alignment
Public premarket cybersecurity history
Recalls, CISA ICS-MA advisories, and disclosed research that shape what reviewers ask about, and what this engagement is built to cover.
- the FDA·2025
FDA AI-enabled device software lifecycle draft guidance (January 7, 2025)
Proposes lifecycle and marketing-submission recommendations for AI-enabled device software functions. This January 2025 draft is not for implementation and establishes no current requirements. Assess applicable cybersecurity duties and risk-based evidence separately under Section 524B and final FDA guidance.
Advisory - Independent research·2019-2023
DICOM PE-in-preamble research (reinforced for AI ingest)
Executable code embedded in DICOM file preambles can survive ingestion. For AI/ML SaMD that consumes DICOM as inference input, this is a first-class threat vector that must be modeled and tested.
AI medical device penetration testing questions
AI/ML Medical Device Security - scoped, fixed-fee, built for FDA premarket submissions.
Scope security testing for your AI-enabled medical device, inference endpoints, connected APIs, and model supply chain. We agree relevant attack paths, test access, evidence, exclusions, and a fixed fee before work begins. Your manufacturer team retains clinical validation and regulatory decisions.