Why AI model scanning: Payload obfuscation in AI supply chains

In the race to deliver faster and more capable AI systems, developers increasingly depend on open-source and third-party models to speed up innovation. But this convenience also comes with significant AI supply chain risks. One of the emerging and overlooked threats? Payload obfuscation attacks hidden in model packaging.

As AI systems grow in complexity and portability, models are commonly shared in standardized formats like ONNX, TensorFlow SavedModels, and Pickle (PKL). These formats are often compressed using tools like gzip, zlib, or packaged using utilities such as Joblib or ArchivLibs (used by NVIDIA NeMo). These techniques are vital for easy model transfer across platforms—but they also open the door to a subtle yet dangerous class of attacks.

The Vulnerability: Compression and Serialization

To be executed by machines, model files must be serialized—turned into a storable format that can later be deserialized back into code. Formats like Pickle are powerful but dangerously permissive: they allow arbitrary code execution during deserialization.

Add compression into the mix (gzip, lzip, etc)—especially with libraries like Joblib, which can load and execute models directly from a compressed state—and you've got the perfect smokescreen. Many scanners skip decompression or deserialization during analysis, creating a dangerous blind spot.

Remember that models may include additional model files for extensibility, custom layers, configuration, executable. Because these payloads live in formats trusted by MLOps pipelines, they often bypass traditional scanners—and silently execute when the model is loaded.

Payload obfuscation is a technique where attackers hide malicious payloads inside serialized or compressed models. 

Real-World Attack Scenarios

Here are two plausible attack vectors that could compromise your AI system:

1. Poisoned Model Drop

An attacker uploads a malicious model to a public repository. It’s compressed and serialized. A developer—assuming the model is safe—pulls it into the training pipeline. During loading, malicious code executes.

2. CI/CD Compromise

A serialized, malicious model is slipped into a pull request. Your CI pipeline automatically tests it—loading the model and running its contents. The attacker now has code execution inside your pipeline.

Mitigation Strategy: Secure Your Model Supply Chain

To prevent these attacks, you need to treat third-party models like untrusted code and secure every step of the model lifecycle. Here’s a recommended secure model handling flow for your MLOps pipeline:

1. Secure Gateway for Model Intake

• Validate repo access with access control rules.

• Enforce managed policies on model downloads.

• Scan the model for known payload obfuscation and serialization-based vulnerabilities using a model-aware scanner.

2. Gate Internal Development

• Only allow scanned and passed models into internal training and engineering workflows.

3. Vet the Data

• Ensure any training or fine-tuning uses validated data from trusted sources—no shortcuts.

4. Scan Again Before Registry Entry

• Before committing a trained model to your internal model registry, re-run the scan.

5. Cryptographic Signing

• Sign all models and their artifacts. This makes tampering detection possible during deployment.

6. Verify at Runtime

• If a model isn’t signed, re-scan before deployment to ensure it hasn’t been modified or injected with new threats.

7. Maintain an Audit Trail

• Log all scans, results, and signing events. Treat your model artifacts with the same integrity controls you’d apply to container images.

💡 Closing Thoughts

The rise of AI doesn’t only demand smarter models—it requires smarter security. As attackers grow more creative, payload obfuscation attacks through compression and serialization will become more common and harder to detect.

The good news? A layered, security-first approach to MLOps can keep these threats in check. By adding model scanning and artifact validation at each lifecycle stage, your AI systems can remain agile without becoming vulnerable.

Let’s treat models like code—and secure them like it, too.