Chapter 9: AI and Robotics Security, Vulnerabilities, and Risk Mitigation
The complexity of AI & Robotics systems introduces a new critical class of
security vulnerabilities, and risk mitigation challenges distinct from
traditional IT. The most pervasive threat is the Adversarial Attack where
malicious actors exploit the underlying logic of a machine learning model by
injecting subtle nearly imperceptible alterations into input data to force an
incorrect decision. For example a few altered pixels on a stop sign can cause
an autonomous vehicle's vision system to misclassify it as a speed limit sign.
These attacks target the Integrity of the model. Equally critical is Data
Poisoning which targets the Confidentiality, and Integrity during the training
phase by injecting harmful, or misleading inputs into the training set causing
the model to learn incorrect patterns and produce malicious, or biased output
in the long term.
For robotics the threat extends to the physical layer where attacks can
compromise the safety, and functionality of the system leading to property
damage, or endangering human lives. Key vulnerabilities include:
Attacks on Sensing and Perception: Manipulating the LiDAR, or camera data to
deceive the robot's SLAM, (Simultaneous Localization, and Mapping), process.
Backdoor Attacks: Planting a hidden trigger during model training that allows
the model to perform normally until it encounters that specific trigger at
which point it executes a compromised function without immediate signs of
failure.
Model Inversion, and Privacy Leakage: Systematically querying the AI model to
reconstruct the underlying sensitive training data posing a severe threat to
user Confidentiality especially in regulated industries like healthcare, and
finance.
Mitigation for True Partner Systems requires a multi-layered approach:
Adversarial Training, (exposing models to manipulated inputs to build
resilience), Automated Data Validation Pipelines, (to prevent poisoning), and
maintaining rigorous Audit Trails, and Logs for all model behaviors, and
decisions to ensure full Accountability, and traceability.
