model-signing 1.0.1

A tool for signing and verifying ML models

Model Transparency

• Overview
• Model Signing
  • Model Signing CLI
  • Model Signing API
  • Model Signing Format
• SLSA for ML
• Status
• Contributing

Overview

There is currently significant growth in the number of ML-powered applications. This brings benefits, but it also provides grounds for attackers to exploit unsuspecting ML users.

Building on the work with Open Source Security Foundation, we are creating this collection of projects to strengthen the ML supply chain in the same way as the traditional software supply chain.

The focus is on providing verifiable claims about the integrity and provenance of the resulting models, meaning users can check for themselves that these claims are true rather than having to just trust the model trainer.

Model Signing

This project demonstrates how to protect the integrity of a model by signing it. We support generating signatures via Sigstore, a tool for making code signatures transparent without requiring management of cryptographic key material. But we also support traditional signing methods, so models can be signed with public keys or signing certificates.

The signing part creates a sigstore bundle protobuf that is stored as in JSON format. The bundle contains the verification material necessary to check the payload and a payload as a DSSE envelope. Further the DSSE envelope contains an in-toto statment and the signature over that statement. The signature format and how the the signature is computed can be seen here.

Finally, the statement itself contains subjects which are a list of (file path, digest) pairs a predicate type set to https://model_signing/signature/v1.0 and a dictionary of predicates. The idea is to use the predicates to store (and therefor sign) model card information in the future.

The verification part reads the sigstore bundle file and firstly verifies that the signature is valid and secondly compute the model's file hashes again to compare against the signed ones.

When users download a given version of a signed model they can check that the signature comes from a known or trusted identity and thus that the model hasn't been tampered with after training.

When using Sigstore, signing events are recorded to Sigstore's append-only transparency log. Transparency logs make signing events discoverable: Model verifiers can validate that the models they are looking at exist in the transparency log by checking a proof of inclusion (which is handled by the model signing library). Furthermore, model signers that monitor the log can check for any unexpected signing events.

Model signers should monitor for occurences of their signing identity in the log. Sigstore is actively developing a log monitor that runs on GitHub Actions.

Model Signing CLI

After installing the package, the CLI can be used via either python -m model_signing <args> or by calling the binary directly, model_signing <args>.

Users that don't want to install the package, but want to test this using the repository can do the same using Hatch via hatch run python -m model_signing <args>.

For the remainder of the section, we would use model_signing <args> method.

The CLI has two subcommands: sign for signing and verify for verification. Each subcommand has another level of subcommands to select the signing method (sigstore -- the default, can be skipped --, key, certificate). Then, each of these subcommands has several flags to configure parameters for signing/verification.

For the demo, we will use the bert-base-uncased model, which can be obtained via:

[...]$ git clone --depth=1 "https://huggingface.co/bert-based-uncased

We remove the .git directory since that should not be included in the signature:

[...]$ rm -rf bert-base-uncased/.git

By default, the code also ignores git related paths.

The simplest example of the CLI is to sign a model using Sigstore:

[...]$ model_signing sign bert-base-uncased

This will open an OIDC flow to obtain a short lived token for the certificate. The identity used during signing and the provider must be reused during verification.

As another example, here is how we can sign with private keys. First, we generate the key pair:

[...]$ openssl ecparam -name prime256v1 -genkey -noout -out key.priv
[...]$ openssl ec -in key.priv -pubout > key.pub

And then we use the private key to sign.

[...]$ model_signing sign key bert-base-uncased --private_key key.priv

All signing methods support changing the signature name and location via the --signature flag:

[...]$ model_signing sign bert-base-uncased --signature model.sig

Consult the help for a list of all flags (model_signing --help, or directly model_signing with no arguments)

On verification we use the verify subcommand. To verify a Sigstore signed model we use

[...]$ model_signing verify bert-base-uncased \
      --signature model.sig \
      --identity "$identity" \
      --identity_provider "$oidc_provider"

Where $identity and $oidc_provider are those set up during the signing flow and --signature must point to the signature to verify.

For developers signing models with Sigstore, there are three identity providers that can be used at the moment:

• Google's provider is https://accounts.google.com.
• GitHub's provider is https://github.com/login/oauth.
  • GitHub Actions uses https://token.actions.githubusercontent.com
• Microsoft's provider is https://login.microsoftonline.com.

For automated signing using a workload identity, the following platforms are currently supported, shown with their expected identities:

• GitHub Actions (https://github.com/octo-org/octo-automation/.github/workflows/oidc.yml@refs/heads/main)
• GitLab CI (https://gitlab.com/my-group/my-project//path/to/.gitlab-ci.yml@refs/heads/main)
• Google Cloud Platform (SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com)
• Buildkite CI (https://buildkite.com/ORGANIZATION_SLUG/PIPELINE_SLUG)

Similarly, for key verification, we can use

[...]$ model_signing verify key bert-base-uncased \
       --signature resnet.sig --public_key key.pub

Model Signing API

We offer an API which can be used in integrations with ML frameworks, ML pipelins and ML model hubs libraries. The CLI wraps around the API.

The API is split into 3 main components:

• model_signing.hashing: responsible with generating a list of hashes for every component of the model. A component could be a file, a file shard, a tensor, etc., depending on the method used. We currently support only files and file shards. The result of hashing is a manifest, a listing of hashes for every object in the model.
• model_signing.signing: responsible with taking the manifest and generating a signature, based on a signing configuration. The signing configuration can select the method used to sign as well as the parameters.
• model_signing.verifying: responsible with taking a signature and verifying it. If the cryptographic parts of the signature can be validated, the verification layer would return an expanded manifest which can then be compared agains a manifest obtained from hashing the existing model. If the two manifest don't match then the model integrity was compromised and the model_signing package detected that.

The first two of these components allows configurability but can also be used directly, with a default configuration. The only difference is for the verification component where we need to configure the verification method since there are no sensible defaults that can be used.

The simplest way to generate a signature using Sigstore is:

import model_signing

model_signing.signing.sign("bert-base-uncased", "model.sig")

This will run the same OIDC flow as when signing with Sigstore from the CLI.

We can use explicit configurations to configure more about the signing:

import model_signing

model_signing.signing.Config().use_elliptic_key_signer(
    private_key="key.priv"
).sign(
    "finbert", "finbert.sig"
)

The same signing configuration can be used to sign multiple models:

import model_signing

signing_config = model_signing.signing.Config().use_elliptic_key_signer(
    private_key="key.priv"
)

for model in all_models:
    signing_config.sign(model, f"{model}_sharded.sig")

Verification needs a configuration. To verify using Sigstore:

import model_signing

model_signing.verifying.Config().use_sigstore_verifier(
    identity=identity, oidc_issuer=oidc_provider
).verify("finbert", "finbert.sig")

The same verification configuration can be used to verify multiple models:

import model_signing

verifying_config = model_signing.signing.Config().use_elliptic_key_verifier(
    public_key="key.pub"
)

for model in all_models:
    verifying_config.verify(model, f"{model}_sharded.sig")

Consult the official documentation for more details.

Model Signing Format

For a diagram showing the model signing format as well as an explanation of the layers, see the model signing format document.

SLSA for ML

This is a separate project from the model_signing package, aimed at generating SLSA provenance for ML models, using either Github Actions or Google Cloud Platform.

See slsa_for_models/README.md for more information.

Contributing

Please see the Contributor Guide for more information.