When AI Gets Phished: What Machine Learning Supply Chain Attacks Can Teach Us About Trust

Imagine this: a new hire joins your team who is smart, capable, and ready to make an impact. But what if someone tampered with their resume, or worse, secretly trained them to ignore your instructions? Even the most promising employee can introduce risk if not properly vetted and onboarded. In security and cloud governance, missteps like misconfigured access, unmonitored services, or flawed assumptions can lead to costly exposures. That’s why we take hiring seriously, with background checks, defined roles, and structured oversight, because trust without validation is a risk.

Now imagine your new AI model is that same eager new hire, but digital. If it was trained on compromised data, modified by an unknown actor, or lacks guardrails, it can make decisions that carry just as much risk. Machine learning supply chain threats are real, and blindly trusting a model is no safer than trusting a stranger with admin rights. Like any new employee, ML tools must be screened, validated, and continuously monitored to ensure they work for your goals and not against them.

Over the past year, we’ve had more conversations with clients asking not just “Is our AI working?” but “Can we trust it?” The question isn’t about accuracy, it’s about integrity. In a world where machine learning models are trained on external data, use pre-built code libraries, and operate across shared infrastructure, it’s increasingly difficult to pinpoint where things might have gone wrong or who introduced the risk in the first place.

We’re seeing this across sectors from healthcare and financial services to retail where AI is being used to drive decisions that carry real consequences. For example, deploying a language model trained on outdated and manipulated financial data will skew predictions and erode stakeholder trust. This isn’t rare, many organizations pull models from open repositories like Hugging Face or GitHub, unaware of who contributed, what data was used, or whether the model has been altered since publication.

The MITRE ATLAS framework and OWASP’s Top 10 for LLM Applications are making it clear: AI isn’t a black box, it’s a whole box truck of third-party risk. And if you wouldn’t let an unknown contractor manage your payroll, you probably shouldn’t let an unaudited model interpret sensitive client data.

Here’s how we typically frame the risks and what leaders can do to reduce exposure:

Not All Models Are Who They Say They Are

We often see organizations treat model ingestion like downloading a PDF, quick, easy, and relatively harmless. But just as you wouldn’t hire someone off the street without a background check, you shouldn’t deploy a model without vetting its origins.

• Validate provenance: Where did the model come from? Who trained it?

• Has it been peer-reviewed, signed, or tested independently?

• Does the provider have clear update and vulnerability management policies?

Just because a model performs well in testing doesn’t mean it’s trustworthy. Consider this due diligence part of your vendor risk management program. Models are intellectual assets, and deploying one blindly is a bit like hiring an anonymous consultant to sit in your boardroom.

Poison Isn’t Just in the Data, It’s in the Process

Model poisoning is like onboarding an employee who’s secretly loyal to a competitor. During training, attackers can subtly skew the model to favor certain outputs, ignore edge cases, or embed logic bombs. This is especially true when using public datasets for AI training. One poisoned entry in thousands can influence behavior in subtle, hard-to-spot ways, and this becomes especially concerning when these models are used for decisions that affect people.

Imagine a machine learning model used in a healthcare setting to assist with diagnostics. If the model has been poisoned during training, it might consistently downplay certain symptoms, leading to misdiagnoses, ineffective treatment plans, or even harmful side effects for patients. In high-stakes environments like healthcare, finance, or security, these subtle manipulations can have severe real-world consequences.

Or take facial recognition in a secure facility. A poisoned model could be trained to misidentify a known threat actor as an approved employee, granting them physical access to restricted areas. These aren’t hypotheticals; researchers and adversaries alike have demonstrated how compromised training data can quietly reshape a model’s behavior, often in ways that evade detection until damage is done.

Poisoned models often perform well in initial benchmarks, much like an employee who aces onboarding but quietly undermines decisions over time. For regulated industries, this subtle degradation can lead to non-compliance, bias, or flawed automated decisions that put customer trust and legal standing at risk.

What to do:

• Sanitize and audit training data, especially if it’s open-source or crowd-sourced.

• Leverage secure data pipelines and version control for training inputs.

• Monitor for unusual or biased behavior during model inference.

Model Hijacking: When Your AI Joins the Dark Side

Even after deployment, models can be hijacked, think of this as your new employee turning into a spy or espionage agent. This might involve unauthorized API exposure, adversarial prompt injection, or corrupted downstream dependencies that get loaded in runtime.

Mitigation tips:

• Use least privilege principles for model access and APIs.

• Monitor model endpoints for strange queries, unusually large payloads, or abuse patterns.

• Continuously validate and re-sign libraries and dependencies, not just during initial deployment, but during patch cycles and major updates.

This isn’t just a tech problem, it’s an ongoing governance need that intersects with your DevSecOps practices. The assumption that models are static and unchangeable post-deployment is outdated. Models evolve, and so do their threats.

Introduce ML to Your Existing Security Playbooks

Your supply chain program probably covers hardware, software, and SaaS. But is ML part of that list? If not, now’s the time.

• OWASP’s Top 10 for LLM Applications is a great place to start identifying common threats.

• MITRE ATLAS offers a visual map of known attacker behaviors targeting ML systems.

• NIST’s AI RMF is also emerging as a solid benchmark for AI risk governance.

At a minimum, treat ML models as untrusted by default, subject to the same review, approval, and lifecycle management as any critical vendor or system. This means logging who’s building models, how they’re tested, how data is sourced, and who has access to inference pipelines.

Embed Continuous Trust Checks

Just like employees go through annual reviews, ML models need regular evaluation too. We often recommend setting up a cadence for:

• Re-testing model outputs for expected behaviors.

• Checking for data drift, performance degradation, or bias.

• Re-assessing dependency and platform risk as part of broader patch management.

Think of this as an “AI performance review”, not to catch bad output, but to ensure consistent alignment with business needs and ethical standards. This also creates an opportunity to evaluate whether older models should be retrained, deprecated, or replaced.

In Summary:

Machine learning supply chain attacks aren’t just theoretical; they’re already happening. From poisoning training data to hijacking inference logic, the risks mirror familiar human threats: phishing, insider sabotage, and fraud.

By applying the same scrutiny and controls you’d use for people and software to your ML models, you can drastically reduce exposure. ATLAS and OWASP provide the frameworks; what’s needed now is leadership discipline and cross-functional awareness.

Regulatory scrutiny is increasing, too. The EU AI Act and U.S. Executive Order on AI both call for transparency and accountability in AI supply chains. Now is a good time to establish a cross-functional governance team, bringing together security, legal, and data science, to create oversight that scales with your AI ambitions.

This is a new frontier in risk management. As AI becomes more embedded in your operations, start treating your models like employees. Interview them. Monitor them. Retrain them when needed. And, when necessary, fire them.

If this feels like a blind spot for your team, we’re always up for a thoughtful conversation. Trust in AI isn’t built with code; it’s built with governance.