Originally published on CoreProse KB-incidents
As hospitals embed AI into pre-op planning, intra-op navigation, and post-op documentation, the incident surface expands far beyond model accuracy. Enterprises already show the pattern: 87% use AI in core operations, yet errors and rework still cost over $67 billion annually. [1] In surgery, similar failures mean preventable harm, not just lost margin.
1. Map the New Incident Surface for AI-Assisted Surgery
Surgical AI is a mesh of systems touching:
Imaging and 3D reconstruction
EHR data and perioperative checklists
Robotic consoles and navigation systems
Operative notes and coding workflows
Incidents often emerge from interactions between these parts, not a single prediction.
⚠️ Risk expansion
LLM-based attacks—data poisoning, adversarial prompts, model inversion—can manipulate or extract sensitive data from assistants that draft notes, summarize histories, or suggest plans. [2] A poisoned pre-op summarizer that downplays anticoagulation history could bias many surgeons toward unsafe choices.
MLOps research shows a single misconfiguration can leak credentials, poison training data, or silently alter deployments. [10] When pre-op risk models, intra-op guidance, and post-op analytics share infrastructure, one flaw can propagate corrupted scores or contours across the perioperative pathway.
📊 Documentation as an incident vector
Clinical evaluation of LLMs for medical summarisation finds hallucinations and unsafe summaries common enough to require safety frameworks and expert review. [11] In surgery, this can mean:
Mis-summarised contraindications and wrong device selection
Hallucinated steps in operative notes, distorting medico-legal records
Omitted complications, undermining quality metrics and audits
“Quiet” failures are equally dangerous. In other industries, LLM agents omit critical details, contradict policies, or answer outside scope without alerts. [12] In surgery, an AI that generates perioperative checklists but sometimes drops antibiotic timing or misstates consent language can break protocol without any security signal.
💡 Key takeaway: AI incidents in surgery are system-level failures across data, pipelines, and documents that invisibly reshape human decisions.
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## 2. Architect AI Surgery Systems for Security, Not Just Accuracy
Because incidents arise from the full system, curated accuracy benchmarks are necessary but insufficient. AI security guidance stresses the model is not the security boundary; the entire system—data flows, tools, and integrations—is the attack surface. [5] In the OR, this includes:
EHR connectors for medications and allergies
Imaging repositories feeding planning tools
Robotic and navigation interfaces translating plans into motion
OR device APIs reporting vitals and device states
Any channel can become a control path for adversaries or accidental overreach.
📊 Agentic AI as a new insider
Studies on agentic AI show over 40% of projects risk cancellation due to unclear value, messy data, and over-privileged access. [3] In hospitals, over-privilege is a safety issue: a scheduling agent that can reorder cases, modify fasting instructions, or place lab orders directly affects patients.
Security research on non-human identities warns machine identities will outnumber humans 80:1, and autonomous agents form a new insider class. [6] Each planning agent, navigation bot, or OR assistant should be treated as a privileged non-human identity, with:
Strong, individual credentials
Least-privilege access to data and tools
Comprehensive audit trails for every decision and action
⚠️ Supply chain and framework risk
Vulnerabilities in open-source AI tools—remote code execution, prompt tampering, access-control flaws—show that “peripheral” monitoring or annotation components can be weaponized. [7] In surgical pipelines, a compromised labeling or prompt-management tool could:
Corrupt segmentation labels for tumor margins
Alter intra-op guidance prompts in real time
Exfiltrate OR video feeds
Framework-level issues such as ChainLeak, enabling cloud key exfiltration and SSRF against AI hosts, show a conversational assistant can become a pivot for cloud takeover if its framework is not patched and isolated. [8]
💡 Key takeaway: Architect surgical AI as a Zero Trust system: treat every agent, connector, and framework as a potential insider, enforcing strict isolation and least privilege from day one.
3. Build a Surgical AI Safety Program: Monitoring, Red Teaming, Governance
A secure architecture only works if operated safely. Surgical AI must be run like critical infrastructure, not experimental software.
📊 Adversarial testing tuned to surgical harm
Model safety red teaming shows jailbreak success rates of 80–100% for leading models, and regulators expect documented adversarial testing for high-risk systems. [4] For surgical AI, red teaming should probe:
Misrouting or mislabeling instruments in robotic workflows
Incorrect dosage or infusion-rate suggestions during anesthesia
Misleading consent or discharge instructions for patients
LLM security work shows naive agents can leak data across sessions and be steered into unauthorized tool use via prompt injection. [9] In the OR, that requires:
Strict session isolation between patients and cases
Hardened tool whitelists with explicit approval for new integrations
Routine probe-based tests of assistants before each production release [9]
⚠️ End-to-end monitoring and human control
Secure MLOps research using MITRE ATLAS shows adversaries can target every phase, from data collection to deployment. [10] Surgical incident response playbooks must cover:
Compromised pre-op datasets (for example, manipulated imaging archives)
Tampered model artifacts or configurations
Real-time anomalies in intra-op recommendations
Clinical LLM safety frameworks recommend explicit scoring of hallucination and safety error rates with expert review. [11] In surgery, this means continuous sampling of AI-generated summaries, checklists, and recommendations, with surgeons labeling incidents and driving rapid updates.
Enterprise experience shows AI errors flourish when outputs are trusted without review. [1] Surgical governance should:
Mandate human verification for all high-stakes outputs
Restrict full automation until safety KPIs are consistently met
💡 Key takeaway: Treat AI surgery incidents as preventable through continuous red teaming, monitoring, and enforced human oversight.
AI will reshape surgery, but the same forces driving AI incidents in enterprise, MLOps, and security research now operate inside the OR, where failures are measured in lives, not dollars. By treating surgical AI as a system, hardening architectures around non-human identities and supply-chain risk, and institutionalizing red teaming and clinical safety evaluation, hospitals can capture algorithmic benefits while keeping surgeons in control.
Hospitals planning or running AI-assisted surgery should establish an AI safety council (surgeons, anesthesiologists, IT security, MLOps), mandate adversarial and hallucination audits before major releases, and require that no AI output can alter a patient’s course of care without explicit, documented human sign-off.
Sources & References (10)
- 1Loopex Digital: Survey Finds 87% of Companies Using AI in Core Operations A 2026 survey of nearly 1,000 C-suite executives found that 87% of companies now use AI in their core operations. However, AI errors and rework continue to cost businesses over $67bn a year. Loopex Di...
2How Can Engineers Monitor and Respond to Evolving LLM-Based Security Incidents? AI Security
October 18th, 2025 7 minute read
Engineers in development and cybersecurity roles face escalating challenges from LLM-based security incidents, where large language models (LLMs) are ex...35 Agentic AI Pitfalls That Derail Enterprise Projects Before Scaling - Accelirate 5 Agentic AI Pitfalls That Derail Enterprise Projects Before Scaling
January 16, 2026
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Enterprises hope their agentic AI implementation will bring significant advantages to their workfl...4Red Teaming Playbook: Model Safety Testing Framework 2025 # Red teaming playbook for model safety: complete implementation framework for AI operations teams
Jailbreak success rates hit 80-100% against leading models. This red teaming playbook helps AI ops t...- 5AI Security Fundamentals: An Architectural Playbook Most AI security conversations start in the wrong place. They fixate on the model, as if the neural network were the entire attack surface. Teams add guardrails and content filters, then wonder why in...
6The 6 security shifts AI teams can't ignore in 2026 - Gradient Flow The AI-Native Security Playbook: Six Essential Shifts
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A little over three dozen security vulnerabilities have been disclosed in various open-source artificial intelligence (AI) and mach...8ChainLeak: Critical AI framework vulnerabilities expose data, enable cloud takeover ChainLeak: Critical AI framework vulnerabilities expose data, enable cloud takeover
As part of this research, Zafran launches Project DarkSide: an initiative that exposes the hidden weaknesses in AI ...9AI Security Resources | LLM Testing & Red Teaming | Giskard Demo: How to test your LLM agents 🚀
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