We build secure software on a seemingly obvious assumption: the processor executes instructions correctly and enforces the boundaries between programs, operating systems, and virtual machines. But what if the CPU itself breaks these rules? Over the past decade, research has found increasingly powerful ways in which processors violate the abstractions on which software relies. What started with tiny timing differences revealing secrets led to attacks such as Spectre and Meltdown, and more recently to CPU bugs that can directly corrupt data, bypass isolation, or expose information across security boundaries.

In this talk, I will show how seemingly harmless implementation details and useful optimizations turn into practical security vulnerabilities. Using examples from our research across Intel, AMD, and RISC-V processors, I will talk about how we discover and understand CPU vulnerabilities and ultimately turn them into security problems. The broader lesson goes beyond processor security: abstractions are only as strong as the layers implementing them. If we want trustworthy computer systems, hardware must increasingly be tested, analyzed, and secured with the same rigor that we already apply to software.

In regression and classification tasks with tabular datasets, most of the literature on explanation in ML (e.g. SHAP, permutation importance) has focused on predictive attribution: does feature X_i help predict outcome Y? At its heart, predictive attribution captures correlation, not causation. We instead ask a causal question: would manipulating feature X_i change outcome Y? We define a natural quantity to answer this question, the feature intervention effect: Δ_i = E[Y | do(X_i=+1)] − E[Y | do(X_i=−1)]. In general, estimating this quantity from observational data alone would entail strong causal assumptions. We propose FADO (Feature-wise Amortized Do-Operators), an amortized inference method using prior-fitted networks trained on synthetic tabular datasets generated by structural causal models. During training, the synthetic data generating process is known, so we can compute ground truth intervention effects Δ_i, and train a model to predict them at test time from a purely observational tabular dataset. As we demonstrate through extensive experiments, our new causal explainability method is easy to train and provides better explanations under a range of settings for which standard predictive attribution fails, including proxy variables, confounders, colliders, and placebos. We evaluate it on real, synthetic and semi-synthetic datasets, with and without hidden confounders, and compare it to existing predictive attribution methods and state-of-the-art causal inference methods.