Security Considerations

First, some of the “pain points” that make humanoid robots more vulnerable than “just another IoT gadget”:

They are cyber-physical systems: software + hardware + sensors + actuators. A compromise in software can turn into a physical hazard (e.g. making the robot move dangerously).
They often use complex stacks (Linux, middleware, ROS, AI/ML modules, cloud connectivity). Each layer is a potential attack surface.
They interact with people (speech, vision, gestures), so there's a “social interface” attack dimension (e.g. voice commands, adversarial audio).
They may be mobile, accessing different networks, moving through different physical zones — so their security context changes dynamically.
Physical access is a real risk (someone could open a panel, plug in a device, or tamper with hardware).
Updating firmware/software on robots is much riskier than on phones — you can brick the robot; rollback may be complex.

A recent survey / systematization, SoK: Cybersecurity Assessment of Humanoid Ecosystem, lays out how humanoids tend to be more exposed than simpler robots, and formalizes a 7-layer security model that maps known attacks and defenses. arXiv
Also, the “Seven-Layer Security for Humanoid Robots” idea (Emergent Mind article) describes decomposing threats into physical, sensing, perception, middleware, decision, application, social interface layers. Emergent Mind

So yeah — there’s no “one trick” — it’s defense in depth across multiple layers.

What security measures should be used

Here’s a menu of techniques and strategies that are or should be employed to keep humanoid robots from getting compromised:

  
      Layer / Domain
      Key Threats
      Defensive Measures
    
      Hardware / Physical
      Tampering, direct access, side-channel attacks, bootloader hacks
      Secure boot / measured boot, Trusted Platform Modules (TPM), tamper-evident casing, disabling unused physical ports, fuses, intrusion sensors, hardware root of trust
    
      Firmware / Bootstage
      Unauthorized firmware flashing, bootloader exploit
      Signed firmware updates, verified boot chains, boot-time integrity checks, rollback protections
    
      Sensors & Perception
      Sensor spoofing (e.g. blinding a camera/LiDAR), adversarial inputs, spoofed data
      Sensor fusion (multiple redundant sensors), sanity checks, outlier detection, adversarial robustness techniques, cross-checking sensor modalities
    
      Middleware / Communication
      Man-in-the-middle, spoofed messages, unauthorized ROS topics, replay attacks
      Use secure middleware (e.g. secure ROS / DDS, message-level encryption, mutual authentication, TLS/DTLS, certificate pinning), prevent open ROS topics, access control to message channels
    
      Control / Decision / AI Logic
      Malicious control commands, AI model poisoning, adversarial input to policies
      Input validation, anomaly detection (look for weird commands), fail-safe modes, adversarial training, “certified” control logic, sandboxing AI modules
    
      Application / APIs / Interfaces
      Unauthenticated APIs, weak endpoints, unprotected UIs, OTA update backdoors
      Strong authentication (multi-factor, keys), API gateways, rate-limiting, secure OTA updates (signed, versioned), hardened UI frameworks
    
      Social / HRI Interface
      Voice spoofing, hidden commands, deception, privacy leakag
      Voice authentication / speaker recognition, filtering inaudible commands, auditing logs, privacy controls, giving users transparency / control over data flows

The 7-layer model gives a structured way to think through which defenses protect which layers of attack.

Extra helpful work: SROS2, a security toolkit for ROS 2, offers tools to help secure robot middleware (authentication, encryption) in ROS-based systems.

Key challenges and tradeoffs

Performance vs security: Crypto, signed updates, integrity checks slow things down. In time-critical control loops, latency matters.
Usability / flexibility vs fixed defenses: Users/owners will demand flexibility (plugins, new behaviors), which may open vulnerabilities.
Update / patching risk: A bad update could brick the robot; rolling back can be hard. So updates are risk-bearing.
Certification / standardization: There’s no globally enforced “robot security rating” yet; vendors vary in rigor.
Zero-trust in robotics: Robots often assume trust in sensors or modules; building distrust / verification is hard.
AI / ML vulnerabilities: Attacks via adversarial inputs, model poisoning, or “jailbreaking” the robot's decision logic.
Detecting compromise: Unlike a server, if a robot is compromised you may not notice until it acts badly. Intrusion detection in robotics systems is still a developing field.

What the future should look like (and what you should demand)

If we’re smart (and lucky), here’s how things should evolve to reduce the chances of a humanoid going rogue:

Adaptive, AI-driven defense agents inside the robot that continuously monitor behavior, detect anomalies, and can isolate compromised subsystems (i.e. a “cybersecurity AI” inside the robot).
Formal verification of control and safety-critical modules — prove mathematically they can’t be driven into unsafe states under defined threat models.
Strong, hardware-backed identity / attestation for robots: cryptographic identity anchored in hardware so each robot can prove its integrity to networks and vice versa.
Standard security audits & benchmarks — like how cars have crash tests, robots should have “hack tests.” The SoK / attack-defense matrix is a step in that direction.
Certification bodies / regulation: Governments or industry bodies might require minimum security levels for robots deployed in public, healthcare, defense, or infrastructure roles.
Collaborative security & bug bounty programs: Encourage external hackers to test and find vulnerabilities with safe disclosure.
Fail-safe / “kill-switch” decoupled paths: Even if the main system is compromised, there should be an out-of-band mechanism to override or shut down movement.
User transparency and control: Let users see what data is streaming, where, and allow them to disable features (especially cameras, sensors).
Layered redundancy: Don’t rely on a single sensor, channel, or logic path — use overlapping checks so a hack of one piece doesn’t collapse the system.