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Why model weights are the new crown jewels

If you spent $100 million and three years building something, you probably wouldn't leave the keys under the doormat, right? Well, that is exactly what's happening with ai model weights today.

Model weights are basically the "brain" of an ai. They represent the final settings after crunching petabytes of data on thousands of gpus. While the training costs a fortune, the resulting file is actually pretty small and easy to move.

A 2024 report by the RAND Corporation identifies at least 38 distinct attack vectors for stealing these weights. We've already seen this happen in the real world—like when a customer employee leaked the Mistral "miqu-1-70b" model on HuggingFace, or when Llama-3 weights appeared as a torrent on 4chan.

• High R&D, Low Transfer Cost: A model like Llama cost millions in compute to train, but once those weights are stolen, an attacker has total control without spending a dime on training.
• The Insider Threat: Traditional firewalls don't do much if a disgruntled admin or a compromised service account just copies the file to a usb.
• Non-Human Identity (NHI) Risks: We often focus on human logins, but the apis and automated workloads moving these weights around are the real weak spots.

In healthcare or finance, a leaked model doesn't just mean lost ip; it means someone can reverse-engineer the training data or find bypasses for safety filters. So, we really need to start treating these weights as machine-driven assets that need their own identity and lifecycle.

The "Screen Door" Problem: Why Disk Encryption Fails

Before we go further, we gotta talk about why traditional encryption isn't cutting it. Most people think "Oh, my disk is encrypted, I'm safe." But disk encryption (Encryption at Rest) only protects data while the server is turned off.

As soon as the ai starts running, those weights are decrypted and loaded into the RAM or GPU memory in plain text. If an attacker gets root access or if there is a "voltage glitching" attack on the chips (as noted in the RAND framework), they can scrape the weights directly from the hardware while they're "in use." To stop this, we need to protect data while it's actually being processed, not just when it's sitting on a hard drive.

Treating AI workloads as non-human identities

Ever wonder why we still treat a million-dollar ai model like it's just another file on a share drive? We spend months training these things, but then we give access to basically anyone with a service account. It's kind of wild when you think about it.

Traditional service accounts are basically just "dumb" keys. If a dev or a rogue admin grabs the credentials, they have the keys to the kingdom. We need to stop thinking about the person and start thinking about the workload identity.

The goal is to move toward what's often called Non-Human Identity Management (NHIM). Instead of a static password, we should be using workload identity federation. This lets you grant just-in-time access to those heavy weight files only when the specific inference engine needs them.

• Static secrets are a trap: If you hardcode an api key to access a model bucket, you're one leak away from a 4chan torrent.
• Context matters: A workload identity doesn't just ask "who are you?" but "where are you running and is your code signed?"
• Industry standards: As established in the RAND report, we need to centralize access to weights in monitored systems.

In a bank or a hospital, you wouldn't let a random doctor perform surgery just because they have a badge. You check if they’re the right doctor for that room. AI is the same. We need to verify the "who" and the "what" of the machine.

This is where cryptographically signed identities come in. If the code in your inference engine changes—even by one line—the identity should break. This is the foundation for the higher security levels we'll define later. I've seen teams in fintech try to use standard iam roles for gpus, but it falls apart because those roles don't account for the integrity of the container. If someone swaps the container image, the iam role still works. That is a huge gap.

How cryptographic attestation actually works for weights

So, how do we actually stop someone from just walking off with the "brain" of our ai? It comes down to hardware-level trust, because honestly, if you're relying on just a software password, you've already lost.

The big shift here is moving toward confidential computing. Instead of the weights sitting in plain sight in the gpu memory, we use things like AMD SEV-SNP or Intel TDX. These create a "black box" in the hardware known as a Trusted Execution Environment (TEE).

Inside this enclave, the data is encrypted even while it’s being used. This ensures that even if a rogue admin has root access to the host machine, they can't see what's happening inside the enclave. The weights are only decrypted once they are safely inside that hardware-protected space.

But hardware isn't enough if the code running inside it is malicious. This is where remote attestation kicks in. This is the technical mechanism that proves the system is safe. Before the key management system hands over the keys to decrypt those weights, it demands a cryptographic "receipt" from the hardware.

• Hash Verification: The system checks the hash of the inference container. If a single line of code was changed to leak weights to an external api, the hash won't match, and the keys stay locked.
• Hardware Identity: It proves the workload is running on actual secure hardware, not a simulator designed to intercept data.
• No Man-in-the-Middle: Because the identity is tied to the hardware and the code (the NHI), you don't have to worry about someone intercepting the weights during the loading process.

In healthcare, this means a hospital can run a diagnostic model on a public cloud without the cloud provider ever seeing the proprietary weights. It's about building a chain of trust that doesn't care about "who" logged in, but "what" is actually running.

Implementing a security level framework for ai

While attestation is the technical tool, we need an organizational policy to decide when to use it. We generally look at this through a five-level security framework (SL1 to SL5) to help teams figure out how much pain they’re willing to tolerate for the sake of safety.

Most startups are living at SL1 or SL2. But if you're handling sensitive financial data or health records, you really need to be aiming higher. As noted in the RAND framework, shifting to SL3 can take about a year of focused effort, while SL4 might take much longer because of the complexity in hardware.

The biggest mistake I see is giving a thousand researchers full "read" access to a bucket. They don't need the file; they just need to run experiments on it.

• Predefined Code Only: Force your inference engine to only run signed, vetted code. If the code hasn't been audited, it shouldn't be able to touch the weights.
• Rate-Limiting: If someone tries to exfiltrate 100GB of weights through an api, the system should choke.
• NHI context: In a retail setting, your recommendation engine's workload identity should only be able to request weights during specific maintenance windows.

I've talked to architects in fintech who struggle with this because it feels like it "breaks" agile. But honestly, it's just about moving the trust from the person to the workload identity.

Future proofing ai with machine identity management

Look, we can't keep pretending that a simple password or a standard service account is going to stop a nation-state from swiping your ai weights. If we're being honest, the tech for a total lockdown—what we call SL5—is still a few years out, but the roadmap starts with how we handle machine identities today.

Current gpu confidential computing is a great start, but it isn't quite ready for the big leagues yet. We’re still seeing gaps where physical attacks or advanced side-channels could leak data. To get to that "holy grail" of SL5, we need to move toward:

• Hardware-level isolation: Moving weights into environments so locked down they don't even have a path to the outside world during training.
• Cryptanalytic resistance: We need to prepare for a future where standard encryption gets cracked by developing much tougher access controls.
• Identity convergence: This is where zero trust meets machine identity. Your model shouldn't just check a token; it needs to verify the entire "state" of the hardware and the code before it even thinks about decrypting a single weight.

You can't just buy a "silver bullet" for ai security. Trust me, I've seen enough vendors claim they have one. It starts with a real-world threat model for every single non-human identity in your pipeline.

In retail, that might mean rate-limiting your recommendation engine so a leak takes years, not seconds. In finance, it’s about ensuring no human ever sees the weights, only the attested code does. Invest in this defense-in-depth now, because by the time the next big leak hits 4chan, it’ll be too late.

Basically, stop trusting people and start verifying the machines.