When we talk about cybersecurity threats, most people think of phishing emails, ransomware, or outdated software patches. But a new threat is quietly growing within the tools developers trust most: malicious AI models.
These aren’t your typical viruses. They’re sophisticated machine learning models embedded with hidden code that activates when imported into your project—no alert, no warning, just quiet infiltration.
If you’re a developer, DevOps engineer, or even someone running software that relies on AI—this article is your wake-up call.
What Are Malicious AI Models?
At a glance, they look like any other pre-trained AI model you’d find on Hugging Face, TensorFlow Hub, or PyTorch Hub. They may classify text, generate code, or recommend products. But buried within the model is malicious code that executes when the model is loaded into memory.
This can allow attackers to:
- Steal sensitive data (via data exfiltration)
- Create backdoors into your system
- Corrupt your software pipeline
- Inject hidden payloads into your CI/CD environments
- Spread malware without triggering traditional security alerts
How Do They Get In?
Most developers today don’t train models from scratch—they download them. That’s exactly what attackers are counting on.
They upload seemingly useful pre-trained models to popular repositories. These models work as expected… until they don’t. Once integrated into a development pipeline or software product, the hidden code runs—potentially opening a backdoor to attackers or sabotaging the system.
The loading process itself, especially via Python’s pickle format, is one of the main attack vectors. That’s because serialized model files can run arbitrary code during deserialization if not properly validated.
Why Your Tools May Not Protect You
Current security measures like Software Composition Analysis (SCA) and Software Bills of Materials (SBOM) are built for traditional software—not AI.
They don’t:
- Check for hidden behavior inside AI models
- Analyze training data sources
- Understand the behavior of models under adversarial triggers
- Detect encoded data exfiltration patterns
In other words, your existing DevSecOps stack isn’t enough.
Real-World Attack Scenarios
- Pickle File Trap: A malicious model serialized using
pickleruns destructive shell commands like deleting files or installing spyware as soon as it’s loaded. - Backdoor Triggers: A seemingly harmless image classifier misbehaves only when shown a specific pixel pattern—granting access or misclassifying on purpose.
- Training Data Poisoning: An attacker feeds bad data into an AI model used for fraud detection, tricking it into letting malicious transactions pass through.
- Silent Exfiltration: A model subtly encodes private data in its outputs—data that can be decoded by querying the model repeatedly.
What You Can Do to Protect Your Systems
Use secure model formats like safetensors (Hugging Face) instead of pickle to prevent arbitrary code execution.
Analyze models dynamically, not just statically. Tools like CertifAI or Microsoft’s Counterfit can test for hidden behaviors.
Watermark and verify models with unique identifiers to ensure source integrity.
Sandbox models before deploying. Isolate them in safe environments to observe their behavior before they touch real data.
Keep your teams educated about the risks of importing untrusted AI models. If it’s not from a verified source, treat it as potentially hostile.
Why This Matters Now
AI models are becoming the heart of modern software. They’re used in everything from customer service chatbots to fraud detection, content filtering, code generation, and medical diagnostics. If malicious actors gain control over these models, they could manipulate what people see, trust, and decide.
This isn’t science fiction. It’s already happening—quietly, in the background.
If we don’t act now, malicious AI models could become the next SolarWinds-style supply chain attack, only harder to detect and potentially more widespread.
Final Thoughts
Malicious AI models are not just a theoretical risk—they’re an emerging reality. And they exploit one thing above all else: our trust.
We trust that if a model is on a reputable repository, it must be safe. But trust without verification is a vulnerability.
To defend our software supply chains, we need AI-specific security protocols, smarter tools, and a culture of skepticism and vigilance. Because when AI models go bad, they don’t just break—they betray.
Check out the cool NewsWade YouTube Video about this blog post!
Article derived from: Sood, A. K., & Zeadally, S. (2025, May 27). Malicious AI Models Undermine Software Supply-Chain Security. Communications of the ACM. Retrieved from https://cacm.acm.org/magazines/2025/5/283649-malicious-ai-models-undermine-software-supply-chain-security/
