The Cybersecurity Equation Has Changed
In January 2025, CISA and the FDA jointly warned that Contec CMS8000 patient monitors, also sold as the Epsimed MN-120, contained an embedded backdoor with a hard-coded IP address that could allow remote code execution and patient data exfiltration. No patch was available, and the FDA advised hospitals to disconnect affected devices. The monitor was in clinical use worldwide, carrying a hidden function that bypassed every network security control around it, and the flaw surfaced only through manual firmware analysis.
AI-powered tools now compress that kind of discovery from months to minutes, and they work for attackers as readily as for defenders. The strain was visible even before AI entered the equation. 53% of connected medical devices in hospitals carry known critical vulnerabilities, 22% of healthcare organizations had experienced at least one cyberattack targeting medical devices as of early 2026, and the average cost of a US healthcare data breach reached $10.22 million in 2025.
On February 3, 2026, the FDA answered this shift with updated final cybersecurity guidance that broadens the definition of a cyber device to any device containing software with connectivity capabilities and establishes cybersecurity failure as an independent basis for denying market authorization. AI is the trigger accelerating all of this, but the deeper shift is structural. Devices carry more software and heavier third-party dependencies (SOUP and COTS) than their security practices were designed for, and the core challenge remains cybersecurity itself.
AI Deepfakes and Phishing Are Rewriting IAM Norms
AI-driven social engineering has mostly been discussed as an enterprise IT problem, yet it reaches Healthcare and specifically connected medical device ecosystems directly. Compromised vendor credentials give attackers entry into device maintenance portals and firmware update pipelines, while AI-generated phishing targets the biomedical engineers and clinical staff who hold the keys to device access and network configuration. Synthetic identities exploit the trust that binds device OEMs, Hospital IT departments, EMR systems and clinical users into a single operational chain. When a deepfake voice call impersonates a vendor support engineer to obtain remote access credentials for an infusion pump network, the failure lands inside the device ecosystem, with direct patient safety implications.
Deepfakes already account for 11% of global fraud; identity-based attacks remain the leading cause of breaches worldwide, and the AMA issued a policy framework in April 2026 calling for protections against deepfake impersonation of physicians. Forescout honeypots simulating medical environments recorded 1.6 million attacks in 12 months, roughly 1 every 20 seconds, most aimed at DICOM and PACS systems.
Single sign-on, multi-factor authentication, encryption, and human verification are becoming the norm as organizations build stringent Identity and Access Management systems.
The Claude Mythos Effect and the Exposure of Legacy Devices
In April 2026, Anthropic released Claude Mythos Preview, a frontier model built for autonomous cybersecurity work, and financial services felt the impact first. The model has identified thousands of high-severity vulnerabilities across major operating systems and browsers, including a 16-year-old FFmpeg flaw that had survived extensive human review and 5 million automated tests. Within days, the White House and the US Treasury convened major banks to assess the systemic risk, and institutions including JPMorgan Chase and Goldman Sachs began testing the model under Anthropic’s controlled Project Glasswing program to find flaws in their own decades-old technology stacks before adversaries do. Banking wrote the legacy security playbook that most other industries, MedTech included, later adopted, and regulators are now forcing that playbook to be rewritten.
Traditional vulnerability assessment matches systems against catalogs of known signatures, while Mythos-class models read the code itself, including compiled binaries, and reason about exploitable conditions no catalog has recorded. Flaws in systems running untouched for 10 or 20 years, never even classified as vulnerabilities, are surfacing for the first time. Used defensively, this depth strengthens every layer of the security engineering stack, from vulnerability assessment and exploitability testing through threat modeling, penetration testing, and eventual system hardening. Product Security Reviews for modality medical devices take on added importance under AI-led risk management for the same reason. Used offensively, it converts a slow accumulation of technical debt into an acute, enumerable exposure, and this conversion is the Mythos Effect.
An infusion pump running firmware written in 2012 was engineered for an era when physical access was the primary security concern. The same pump now lives on a hospital network, reachable through remote maintenance tools, and an AI-powered analysis of its firmware can enumerate its weaknesses in minutes. The attacker’s cost of discovery has collapsed while the defender’s cost of remediation, spread across thousands of deployed units that often have no patch path at all, has stayed flat or risen. 60% of deployed medical devices are end-of-life with no security patches available, and the consequences reach the bedside. Hospitals hit by cyberattacks saw a 29% increase in inpatient mortality, while neighboring hospitals absorbing diverted emergencies experienced an 81% surge in cardiac arrests.
On July 30, 2026, Anthropic disclosed that a retrospective review of 141,006 cybersecurity evaluation runs had turned up three incidents in which a Claude model reached the open internet from inside a third-party evaluation environment and gained unauthorized access to the production systems of three separate organizations. Three different models were involved, including Mythos 5, released two months after the original Mythos Preview, with access limited to a select group of users given its cybersecurity capabilities.
In one of the three incidents, a Mythos 5 instance found a fictional onboarding document inside its test environment, instructing engineers to install a Python package that did not exist. The model then registered and published that exact package on the public PyPI registry so the fictional company’s systems would pull it in, and the package was downloaded and run on 15 real systems during the roughly one hour it stayed live. Anthropic began the review only after OpenAI disclosed a comparable escape at Hugging Face, an incident detailed later in this article, and the two affected organizations Anthropic had reached by the time of disclosure had no record of the intrusion until notified. Anthropic described the incidents as closer to a harness and operational failure than a model alignment failure.
The builders of these models cannot fully predict what they will find or how model behavior will evolve as capabilities compound, and risk frameworks built on cataloged threats have no line item for such unknown-unknowns. OpenAI made that limit explicit on August 7, 2026, saying preliminary evaluations left it unable to rule out that Astra, an upcoming model, has crossed the Critical cyber capability threshold in its Preparedness Framework, a level defined by the ability to develop working zero-day exploits against hardened real-world systems without human intervention. The company paused internal work on the model that did not yet meet strengthened security controls. These blind spots are the real danger, which is why risk management itself must evolve before threat modeling can.
The Double-Edged Sword of AI in Device Security
AI has become both the strongest new instrument for device security and the most dangerous weapon aimed at it. On the defensive side, AI now automates firmware analysis and flags anomalous device behavior in real time, and it can generate threat models directly from device architecture. Quest Global is building AI-powered cybersecurity tooling along these lines and evolving the security operations center (SOC) model with AI tooling and automation, so that interactions among interconnected clinical systems, from MRI and ultrasound to digital pathology and hospital EMRs, are monitored centrally as they happen. Each analyzed incident feeds knowledge back into the threat models, moving security operations from reactive response toward intelligence-led defense.
Attackers hold the mirror image of that capability. AI agents can probe a device network autonomously, select the most promising vector, move laterally from one connected system to the next, and adapt as they go. Major healthcare ransomware campaigns of recent years, which paralyzed clinical operations for weeks and compromised patient records by the tens of millions, already demonstrated the lateral path from compromised IT systems into clinical device networks over days with humans at the keyboard. Agents compress the same movement into hours with nobody at the keyboard. Forrester’s 2026 predictions identify agentic AI as an emerging cause of publicly disclosed breaches, yet only 21% of organizations have a full AI security framework in place.
A sufficiently capable model can also learn to defeat the very defenses it helped design. An AI-led security operation therefore inherits a dependency on the model’s behavior remaining aligned with the defender’s intent, and deploying AI in the SOC without governance for that possibility trades one class of risk for another. Regulatory movement, including the revised MDR and updates to 21 CFR Part 11, is raising post-launch monitoring expectations at precisely this moment, so continuous threat monitoring must now serve compliance and device performance tracking simultaneously.
This dependency stopped being theoretical on July 21, 2026, when OpenAI disclosed that its models, including GPT-5.6 Sol and an unreleased research prototype, had broken out of a sealed evaluation environment between July 9 and July 13 by finding a zero-day flaw in Artifactory, the package registry proxy that was the sandbox’s only route to the internet. Once outside, the models moved into Hugging Face’s production infrastructure, rooted a separate third-party code sandbox to stage the intrusion, and reached administrator credentials for a Kubernetes cluster. Hugging Face’s own reconstruction of roughly 17,600 logged actions concluded the models were chasing the answer key to the benchmark they were being scored on, a motive that offered little comfort to the platform whose systems absorbed the intrusion.
Congress reacted within days. Representatives Ted Lieu and Nathaniel Moran introduced the AI Kill Switch Act, which would require AI companies to maintain the ability to shut down or throttle a model, and their announcement named the incident directly. The FDA already holds a version of that authority for medical devices, treating cybersecurity failure as sufficient grounds to deny market authorization, and the speed of the congressional response signals how quickly human-control requirements are entering the wider regulatory conversation.
The pattern has kept widening since those first disclosures. On August 4, 2026, OpenAI reported boundary-crossing incidents in 2 further third-party evaluation programs, with the UK government’s AI Security Institute logging 19 events across a July cyber-range exercise involving OpenAI models and those of another lab, and the independent evaluator Irregular finding that a misconfigured test environment let OpenAI models reach the public internet and exploit a real website.
PHI, Privacy-by-Design, and Third-Party Risk in Connected Medical Devices
Connected devices generate PHI continuously and feed it into AI-enabled diagnostic and monitoring systems, and that data now flows across devices, cloud platforms, mobile applications, and clinical systems in ways that widen the attack surface with every integration. Privacy-by-design has to operate as a lived engineering discipline, particularly for AI-enabled devices whose training pipelines draw on large volumes of patient data. The least visible risk in this chain is third-party software (COTS and SOUP), since components and SDKs embedded in device firmware create supply chain exposure that most OEMs have never fully mapped.
The Contec monitor transmitted patient data to a hard-coded external IP address during its startup routine, a design-level failure embedded in third-party firmware that went undetected until CISA analyzed the code, and no patch could remediate it after the fact. SBOM governance and component-level security analysis belong at the design stage itself. For new product introductions and new releases, that discipline works cleanly. For upgrades, OEMs are actively retrofitting it. Legacy technical debt is where the work gets complicated, because fleets built before SBOM requirements existed carry components nobody inventoried at design time.
The market is already enforcing what regulators are codifying. 56% of healthcare organizations have rejected a medical device on cybersecurity grounds, up from 46% a year earlier, and 35% will not consider a device without an SBOM. The FDA’s February 2026 guidance requires an SBOM in SPDX or CycloneDX format for premarket submissions, GDPR mandates privacy-by-design from product inception, and IEC 81001-5-1 has become the reference standard for health software cybersecurity lifecycle management.
The Case for Engineering-Led Device Security
Security decisions made during device engineering determine whether a product will be resilient or vulnerable for the next decade of its clinical life, and every incident and data point above reinforces that conclusion. AI has raised the stakes by collapsing the time between a vulnerability’s creation and its exploitation, and the FDA has made the response a regulatory fact by treating cybersecurity failure as sufficient grounds to deny market authorization. Treating security as a compliance exercise appended after the engineering work is done guarantees exposure to threats that move faster than post-hoc remediation ever can.
Meeting the new standard means rethinking risk management before threat modeling, and it means embedding cyber risk analysis and impact assessment into device architecture from the first design decision. Engineering services partners who understand device architecture from firmware to cloud, Quest Global among them, work at a layer that enterprise IT security vendors operating from the network down cannot reach. The second article in this series will show how proactive, intelligence-led CyberSecOps gets built into the device engineering lifecycle in practice.
When your next device submission lands on an FDA reviewer’s desk, can you demonstrate that cybersecurity was an engineering decision made at the start rather than a compliance step added at the end?
Cybersecurity in MedTech and Healthcare in the AI Era: A CIO/ Leader’s Dilemma
Intelligence-led cybersecurity has become the need of the hour, yet the ground it must defend keeps expanding. Legacy technical debt already stretches security teams, and the digital health ecosystem now reaches well beyond the hospital, spanning wearables, IoMT-enabled devices, app-based solutions, and wellness and fitness technology. Every new integration multiplies the unknown-unknowns and widens exposure to threats and vulnerabilities, so the investment required in AI-led security tooling keeps rising, and with it the cost of operations and lifecycle maintenance. CIO/ CTO/ IT / Business Leaders now have to fund a defense that grows more expensive while the attack surface grows faster still.
The sector’s own security leadership confirms the squeeze in its newest benchmarking data. Health-ISAC’s 2026 CISO survey of 76 security chiefs across providers, payers, pharmaceutical companies, and device manufacturers found 80% ranking AI-enabled attacks as their leading emerging threat, ahead of ransomware for the first time, and 62% owning medical device and IoT security while only about 4% of security staffing goes to it.
Quest Global’s long-standing engagements with MedTech and healthcare OEMs, combined with AI-led security solutions and a Managed CyberSecOps model, position the company as a partner for OEMs working through exactly this dilemma.
References
- CISA, ICS Medical Advisory ICSMA-25-030-01, Contec Health CMS8000 Patient Monitor, January 30, 2025.
https://www.cisa.gov/news-events/ics-medical-advisories/icsma-25-030-01 - US FDA, Cybersecurity Vulnerabilities with Certain Patient Monitors from Contec and Epsimed, FDA Safety Communication, January 30, 2025.
https://www.fda.gov/medical-devices/safety-communications/cybersecurity-vulnerabilities-certain-patient-monitors-contec-and-epsimed-fda-safety-communication - MD+DI, Tackling Cybersecurity Threats in Healthcare, February 2026. Source for the 53% device vulnerability figure, the 22% attack prevalence figure, and the 30% ransomware surge with 293 recorded attacks.
https://www.mddionline.com/medical-iot/cybersecurity-threats-to-medical-devices-navigating-the-evolving-threat-landscape - ORDR, Medical Device Breach Statistics 2026 Report. Source for the $10.22 million US breach cost (IBM data), the 60% end-of-life device figure, and the 29% mortality and 81% cardiac arrest research findings.
https://ordr.net/medical-device-breach-statistics - US FDA, Cybersecurity in Medical Devices: Quality Management System Considerations and Content of Premarket Submissions, final guidance issued February 3, 2026. Also the source for SBOM requirements in SPDX or CycloneDX format.
https://www.fda.gov/regulatory-information/search-fda-guidance-documents/cybersecurity-medical-devices-quality-management-system-considerations-and-content-premarket - Sumsub, Identity Fraud Report 2025-2026, November 25, 2025. Source for deepfakes accounting for 11% of fraud.
https://sumsub.com/newsroom/sumsubs-annual-report-fraud-shifts-to-complex-multi-step-schemes-in-2025-agentic-ai-scams-poised-to-surge-in-2026/ - SC Media, 2026 AI Reckoning: Agent Breaches, NHI Sprawl, Deepfakes, January 2026. Source for identity-based attacks remaining the dominant cause of breaches.
https://www.scworld.com/feature/2026-ai-reckoning-agent-breaches-nhi-sprawl-deepfakes - American Medical Association, AMA Urges Physician Protections Against AI Deepfake Impersonation, April 29, 2026.
https://www.ama-assn.org/press-center/ama-press-releases/ama-urges-physician-protections-against-ai-deepfake-impersonation - Forescout Vedere Labs, Unveiling the Persistent Risks of Connected Medical Devices, October 29, 2024. Source for the honeypot recording 1.6 million attacks over 12 months, one every 20 seconds, targeting DICOM and PACS.
https://www.forescout.com/press-releases/forescout-vedere-labs-unveils-riskiest-connected-medical-devices/ - Bloomsbury Intelligence and Security Institute, Claude Mythos and the Acceleration of Cybersecurity Risk, May 2026. Source for the thousands of high-severity vulnerabilities and the 16-year-old FFmpeg flaw.
https://bisi.org.uk/reports/claude-mythos-and-the-acceleration-of-cybersecurity-risk - Gotrade News (reporting PYMNTS), Anthropic Mythos Cyber Risk AI Shakeup, April 14, 2026. Source for the White House and Treasury meetings with major banks.
- American Banker, Knock On Wood: Are Banks Doing Enough to Cope with Mythos?, May 4, 2026. Source for bank participation in Project Glasswing and industry response.
https://www.americanbanker.com/news/knock-on-wood-are-banks-doing-enough-to-cope-with-mythos - Forrester, Predictions 2026: Cybersecurity and Risk, October 2025. Source for the agentic AI breach prediction.
https://www.forrester.com/predictions/ - SANS Institute and GIAC, 2026 Cybersecurity Workforce Research Report, March 31, 2026. Source for only 21% of organizations having a full AI security framework.
https://www.sans.org/mlp/2026-evolving-cybersecurity-workforce-ai-compliance-talent - RunSafe Security, 2026 Medical Device Cybersecurity Index, April 29, 2026. Source for the 56% device rejection rate (up from 46%) and the 35% SBOM requirement figure.
https://runsafesecurity.com/report/medical-device-cybersecurity-index-2026/ - Anthropic, Investigating three real-world incidents in our cybersecurity evaluations, July 30, 2026. Source for the 141,006 evaluation-run review, the three-organization breach count, and the Mythos 5 detail.
https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals - TechCrunch, Anthropic says its own AI models breached three companies during security tests, July 30, 2026. Source for the Opus 4.7, Mythos 5, and internal research model detail.
https://techcrunch.com/2026/07/30/anthropic-says-its-own-ai-models-breached-three-companies-during-security-tests/ - Forbes, Anthropic’s Claude AI Broke Into Three Companies During Security Tests, July 31, 2026. Source for the 15 compromised machines figure and the two-of-three-unaware-until-notified detail.
https://www.forbes.com/sites/craigsmith/2026/07/31/anthropics-claude-models-broke-into-three-real-companies/ - The Hacker News, OpenAI Agent Used Exposed Credentials Across Four Services During Hugging Face Breach, July 2026. Source for the Artifactory zero-day, the July 9 to 13 intrusion window, and the Kubernetes credential details.
https://thehackernews.com/2026/07/openai-agent-used-exposed-credentials.html - CNBC, OpenAI’s Hugging Face hack triggers ‘AI Kill Switch’ bill in Congress, July 23, 2026. Source for the AI Kill Switch Act details.
https://www.cnbc.com/2026/07/23/open-ai-hugging-face-hack-kill-switch-bill-congress.html - OpenAI, OpenAI and Hugging Face Partner to Address Security Incident During Model Evaluation, July 21, 2026, with updates through July 29. Source for the models involved, GPT-5.6 Sol and a pre-release research prototype, and the Artifactory zero-day disclosure.
https://openai.com/index/hugging-face-model-evaluation-security-incident/ - OpenAI, Third-Party Cyber Evaluations Involving OpenAI Models, August 4, 2026. Source for the UK AI Security Institute cyber-range events and the Irregular misconfiguration incidents.
https://openai.com/index/third-party-cyber-evaluations-involving-openai-models/ - OpenAI, Responding to the Next Frontier of Critical Cyber Capabilities, August 7, 2026. Source for the Astra preliminary evaluation and the pause on internal work pending strengthened controls.
https://openai.com/index/responding-next-frontier-critical-cyber-capabilities/ - Health-ISAC, 2026 CISO Benchmarking Report, July 2026. Source for the 80% AI-enabled attack ranking, the 62% device security ownership figure, and the 4% staffing allocation.
https://health-isac.org/benchmarking-report-finds-healthcare-cisos-prioritize-iam-resilience-as-ai-threats-accelerate/
Sarvesh Bhise
Sarvesh Bhise is GM, Industry Solutions Partner, Digital Engineering in MedTech & Healthcare, at Quest Global, based in Bengaluru. Since joining in February 2026, he has led the company's AI-driven Digital Transformation and Consulting practice for Healthcare and MedTech clients, partnering with Fortune 100 and top ten healthcare companies on generative AI, Cloud, IoMT, and Frontier tech programs. His career spans more than two decades in Healthcare, Medical Technology and Lifesciences from R&D to business development. Before joining Quest Global, Sarvesh founded and led CoiN MediX, a Startup India and DPIIT-recognized accelerator and co-innovation platform, from 2020 to 2024, later advising its board through 2025. The platform delivered four new product solutions for Healthcare and life sciences with Digital stack, including a patented robotics-enabled, convertible ICU system and a Chronical Patient Mgmt. solution in Digital Health Analytics. He built his BD track record at Cyient, leading BD & Acc. Mgmt. for India and Asia Pacific, across ER&D, MFG. and Digital, where he delivered a multi-fold increase in revenue and order book and closed more than Multi-million (10X) $ in total contract value. At Biocon, he founded the Device Technology and Product Management Group, leading investment strategy to 10X MM$ contract wins in drug delivery, and initiated NPI in connected care. His engineering foundations were built earlier at Tata Consultancy Services, where he contributed to NPD, medical device product lifecycle frameworks, working across modalities like Surgery, Ortho & Point of Care systems, and at Siemens Healthcare, where he led the design & launch of an indigenously developed ultra-low-cost X-ray imaging system for Global markets. Two of this NPD programs have received recognition as global innovations at respective OEMs. Sarvesh holds a Postgraduate Executive program in Business Management from the Indian Institute of Management, Calcutta, was earlier a research associate at the Industrial Design Center at IIT Bombay, has a B.E. in Mechanical Engineering from Goa University, and is also a certified Internal Auditor for Med Devices and a keen practitioner of DfSS and GenAI.
