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766 interactive lessons (4x more than most competitors), 16 different diagram types that build step by step, real production examples from Netflix, Google, Amazon, Uber, and Stripe, and lifetime access for a one-time payment of $7.99 instead of annual subscriptions costing 100 to 200 dollars per year.

What is AI supply chain security?

It is securing everything an LLM application imports rather than what it does at runtime: base model weights, tokenizers, training and fine-tune datasets, the pip or npm dependency tree, and third-party tools and MCP servers. Each is code or data authored by someone else that runs with your application's privileges, so each is an entry point that pinning, hashing, scanning, and provenance checks are meant to control.

Why is loading a model file dangerous?

Because pickle-based weight formats like PyTorch .bin and .pt files execute code when you load them. Python's pickle reconstructs objects by calling their __reduce__ method during deserialization, and an attacker can make __reduce__ run os.system, so a malicious checkpoint runs code before you ever get a model object. The fix is the safetensors format, which stores only tensor metadata and raw bytes with no code path.

What is the difference between safetensors and pickle?

A pickle file is a program that runs code when loaded, which is the vulnerability, while a safetensors file is data: a JSON header of tensor shapes and dtypes followed by raw bytes, with no way to execute anything. safetensors is also faster because it memory-maps. It makes the load safe but not the weights, since a safetensors file can still contain a backdoor in its numbers.

Why can a benchmark not catch a backdoored model?

Because a backdoor is a conditional the model learned that only fires on a trigger the attacker chose, such as a rare phrase or a specific date, and that trigger is not in your evaluation set. On every input you test, the model behaves like a clean one and scores well. Anthropic's Sleeper Agents study showed such backdoors can even survive supervised fine-tuning and adversarial training, so provenance matters more than a passing score.

Why pin and hash dependencies instead of just pinning the version?

Because a version pin names a version but the package index still serves the bytes, so a compromised mirror or a hijacked maintainer account can re-serve the same version number with tampered content. A recorded hash, enforced with pip's hash-checking mode, refuses any bytes that do not match what you reviewed. The version stops a surprise upgrade and the hash stops a same-version byte swap.

An AI-BOM extends a Software Bill of Materials past packages to the two artifacts an LLM app also ships, models and datasets, recording each with its source, exact version or snapshot, hash, format, and license. Its value is incident response: when an advisory names an artifact, an accurate inventory answers whether you are affected in minutes. CycloneDX added ML-BOM support and SPDX 3.0 added AI and dataset profiles.

You download a PyTorch checkpoint saved as a .bin file from a public hub and load it on a training host with cloud credentials, using torch.load on an older PyTorch. What is the core supply chain risk?

Loading the pickle-based file can execute attacker code with your privileges before you get a model object, A .bin checkpoint deserializes through Python's pickle, and pickle reconstructs objects by calling __reduce__ during load, which an attacker can set to os.system. The payload runs the instant you call torch.load, before any model object exists, with whatever privileges your process holds. On a host with cloud credentials that is a credential theft primitive. The mitigations are preferring safetensors, weights-only loading, scanning, and loading anything untrusted in a sandbox with no secrets or network.

A vendor's model passes every accuracy and safety benchmark you run. Why is that not sufficient evidence that the weights are not backdoored?

A backdoor fires only on an attacker-chosen trigger that is not in your eval set, so the model scores like a clean one on everything you test, A backdoor is a conditional the model learned that activates only on a specific trigger the attacker chose, such as a rare phrase or a date. On all the inputs in your benchmark, which do not contain the trigger, the model behaves normally and scores well. Anthropic's Sleeper Agents work showed the behavior can even survive standard safety fine-tuning. This is why the defense is provenance, pinning to an immutable revision from a trusted source, not a higher benchmark score.

Your requirements file uses helper-lib >= 1.2.0. The maintainer's account is compromised and 1.2.1 is published with an import-time payload, and a compromised mirror also re-serves 1.2.0 with tampered bytes. Which specification actually stops both?

An exact version pin plus a recorded artifact hash, enforced with hash-checking mode, The range silently pulls the malicious 1.2.1, so it fails the first threat. An exact pin stops the version bump but a version number does not describe bytes, so a mirror re-serving 1.2.0 with tampered content still slips past a version-only pin. Recording the artifact hash and enforcing it with pip's hash-checking mode refuses any bytes that do not match what you reviewed, which stops the same-version swap. The version answers which release, the hash answers which exact bytes, and you need both.

Your team verifies Sigstore signatures on every model and package before install and fails closed on a mismatch. A widely used package is then compromised through maintainer account takeover, and the malicious release is signed by the legitimate publisher identity. What does signature verification give you here?

It proves origin and integrity but not safety, so a release signed by a compromised-but-legitimate publisher still verifies and must be caught by other controls, A signature binds bytes to a publisher identity, so it proves who produced the artifact and that it was not altered after signing. It does not prove the artifact is benign. When a legitimate account is taken over, the attacker signs with the real identity, so verification passes. Signing still narrows who you must trust and makes post-publication tampering detectable, which is why it sits alongside allowlisting, pinning, hashing, and scanning rather than replacing them. Defense in depth, not a single gate.

AI Supply Chain Security: What You Import Runs as You

Name: System Design Masterclass
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