Embodied AI systems integrate language models with real world sensing, mobility, and cloud connected mobile apps. Yet while model jailbreaks have drawn significant attention, the broader system stack of embodied intelligence remains largely unexplored. In this work, we conduct the first holistic security analysis of the Unitree Go2 platform and uncover ten cross layer vulnerabilities the "Ten Sins of Embodied AI Security." Using BLE sniffing, traffic interception, APK reverse engineering, cloud API testing, and hardware probing, we identify systemic weaknesses across three architectural layers: wireless provisioning, core modules, and external interfaces. These include hard coded keys, predictable handshake tokens, WiFi credential leakage, missing TLS validation, static SSH password, multilingual safety bypass behavior, insecure local relay channels, weak binding logic, and unrestricted firmware access. Together, they allow adversaries to hijack devices, inject arbitrary commands, extract sensitive information, or gain full physical control.Our findings show that securing embodied AI requires far more than aligning the model itself. We conclude with system level lessons learned and recommendations for building embodied platforms that remain robust across their entire software hardware ecosystem.

"AlphaZero crushes chess!" scream the headlinesa as the AlphaZero algorithm developed by Google and DeepMind took just four hours of playing against itself (with no human help) to defeat the reigning World Computer Champion Stockfish by 28 wins to 0 in a 100-game match. Only four hours to recreate the chess knowledge of one and a half millennium of human creativity! This followed the announcement just weeks earlier that their program AlphaGoZero had, starting from scratch, with no human inputs at all, comprehensively beaten the previous version AlphaGo, which in turn had spectacularly beaten one of the world's top Go players, Lee Seedol, 4-1 in a match in Seoul, Korea, in March 2016. Interest in AI has reached fever pitch in the popular imagination--its opportunities and its threats. The time is ripe for books on AI and what it holds for our future such as Life 3.0: Being Human in the Age of Artificial Intelligence by Max Tegmark, Android Dreams by Toby Walsh, and Artificial Intelligence by Melanie Mitchell.6,8,9

Let's start with an obvious fact: ML models can only be as good as the datasets that were used to build them! While there is a lot of emphasis on ML model building and algorithm selection, teams often do not pay enough attention to dataset selection! In my experience, investing time upfront in dataset selection saves endless hours later during model debugging and production rollout. Based on the ML model being built, outliers can either be a noise to ignore or important to take into account. Outliers arising from collection errors are the ones that need to be ignored.

During model training, there are scenarios when the loss-epoch graph keeps bouncing around and does not seem to converge irrespective of the number of epochs. There is no silver bullet as there are multiple root causes to investigate -- bad training examples, missing truths, changing data distributions, too high a learning rate. The most common one I have seen is bad training examples related to a combination of anomalous data and incorrect labels. Sometimes there are scenarios where the model seems to be converging, but suddenly the loss value increases significantly, i.e., loss value reduces and then increases significantly with epochs. There are multiple reasons for this kind of exploding loss.

Mistaken extrapolations, limited imagination, and other common mistakes that distract us from thinking more productively about the future. We are surrounded by hysteria about the future of artificial intelligence and robotics--hysteria about how powerful they will become, how quickly, and what they will do to jobs. I recently saw a story in MarketWatch that said robots will take half of today's jobs in 10 to 20 years. It even had a graphic to prove the numbers. How many robots are currently operational in those jobs? How many realistic demonstrations have there been of robots working in this arena? Similar stories apply to all the other categories where it is suggested that we will see the end of more than 90 percent of jobs that currently require physical presence at some particular site. Mistaken predictions lead to fears of things that are not going to happen, whether it's the wide-scale destruction of jobs, the Singularity, or the advent of AI that has values different from ours and might try to destroy us.

We are surrounded by hysteria about the future of artificial intelligence and robotics--hysteria about how powerful they will become, how quickly, and what they will do to jobs. I recently saw a story in MarketWatch that said robots will take half of today's jobs in 10 to 20 years. It even had a graphic to prove the numbers. How many robots are currently operational in those jobs? How many realistic demonstrations have there been of robots working in this arena? Similar stories apply to all the other categories where it is suggested that we will see the end of more than 90 percent of jobs that currently require physical presence at some particular site. Mistaken predictions lead to fears of things that are not going to happen, whether it's the wide-scale destruction of jobs, the Singularity, or the advent of AI that has values different from ours and might try to destroy us. We need to push back on these mistakes. But why are people making them?