Your autonomous warehouse robot, inspection drone, or robotic arm is only as secure as its most exploitable AI component. We conduct adversarial security testing to find and fix these vulnerabilities.
Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Proactively identify and remediate safety-critical vulnerabilities in AI-powered physical systems before they lead to operational failure or malicious control.
Your autonomous warehouse robot, inspection drone, or robotic arm is only as secure as its most exploitable AI component. We conduct adversarial security testing to find and fix these vulnerabilities.
Our red teaming uncovers risks that traditional IT security misses, including:
We employ frameworks like MITRE ATLAS to simulate real-world attack chains, providing actionable reports with prioritized remediation steps. This ensures your physical AI systems meet safety-critical standards and are resilient against novel threats.
This service is a core component of our broader AI Red Teaming and Adversarial Defense practice, and is often paired with our Physical AI and Industrial Robotics Integration development work to build secure systems from the ground up.
Our adversarial testing for physical AI systems delivers concrete security improvements and actionable intelligence, not just theoretical reports. We provide the evidence and remediation guidance to harden your robotics and autonomous systems against real-world threats.
Receive a prioritized list of exploitable security flaws—from sensor spoofing and actuator hijacking to network protocol weaknesses—with detailed proof-of-concept demonstrations and step-by-step remediation guidance.
We identify and help you remediate vulnerabilities that could lead to physical harm, property damage, or mission failure, directly supporting compliance with functional safety standards like ISO 26262 and IEC 61508.
Witness real-time demonstrations of attacks like LiDAR/radar spoofing, GPS jamming, and CAN bus injection on your hardware-in-the-loop systems, providing undeniable evidence of system weaknesses.
Leave the engagement with a fortified system. We provide specific configuration changes, code patches, and architectural recommendations validated to block the attack vectors we discovered.
Your engineering and security teams gain hands-on experience in adversarial thinking. We conduct knowledge transfer sessions on emerging physical AI attack vectors and defensive patterns.
Establish a security baseline and receive a roadmap for integrating continuous adversarial testing into your SDLC, enabling proactive defense against novel threats as your systems evolve.
Our phased approach to Physical AI and Robotics Security Red Teaming ensures systematic discovery and remediation of safety-critical vulnerabilities. Each engagement delivers actionable intelligence and hardening guidance.
| Phase & Deliverables | Starter (4-6 Weeks) | Professional (8-12 Weeks) | Enterprise (Ongoing Program) |
|---|---|---|---|
Kickoff & Scoping | |||
Threat Modeling & Attack Surface Mapping | Limited Scope | Comprehensive (MITRE ATLAS) | Continuous & Dynamic |
Physical Hardware & Sensor Manipulation Testing | Basic I/O Fuzzing | Advanced Signal Spoofing, CAN Bus Attacks | Full-spectrum (RF, LiDAR, GPS, IMU) |
Robotic Control Logic & Safety Bypass | Pre-defined Test Cases | Custom Adversarial RL Agent Development | Live, Adaptive Adversary Simulation |
AI Model Adversarial Attacks (Physical) | Digital-Physical Transfer Attacks | Real-world Adversarial Patch Deployment | Multi-modal, Coordinated Attack Campaigns |
Detailed Technical Risk Report | |||
Remediation Guidance & Hardening Blueprint | Prioritized List | Architectural Review & Code-level Fixes | Integration with CI/CD & Policy-as-Code |
Executive Briefing & Compliance Mapping | |||
Retesting & Validation of Fixes | 1 Round | 2 Rounds | Continuous Validation |
Ongoing Threat Intelligence & Attack Simulation | Quarterly Campaigns & Novel Vector Updates |
Our red teaming services are tailored to the unique threat models of AI-integrated physical systems. We identify vulnerabilities that could lead to safety failures, operational disruption, or malicious control before they are exploited.
Adversarial testing of perception systems (LiDAR, cameras) and control algorithms to prevent spoofing, sensor blinding, and trajectory hijacking that could cause collisions or loss of control.
Security assessment of robotic arms, AGVs, and collaborative robots for vulnerabilities in motion planning, human-robot interaction protocols, and PLC communication that could induce unsafe operations.
Rigorous adversarial testing of AI-assisted diagnostic and surgical systems to ensure resilience against data manipulation that could lead to misdiagnosis or compromised procedural safety.
Security testing of autonomous mobile robots (AMRs) and automated storage systems for vulnerabilities in fleet coordination, inventory tracking, and navigation that could disrupt operations.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get clear answers on how we secure AI-powered physical systems. Our methodology is based on frameworks like MITRE ATLAS and real-world adversarial testing.
We employ a structured, three-phase methodology aligned with the MITRE ATLAS framework and real-world adversarial tactics. Phase 1 involves threat modeling and asset mapping of the entire AI-hardware stack. Phase 2 is active adversarial testing, including hardware manipulation, sensor spoofing, and communication channel attacks. Phase 3 delivers a detailed risk report with prioritized, actionable remediation steps and validation retesting. This ensures vulnerabilities are not just found, but fixed.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
