Three security risks of AI-augmented coding: what SWEs need to know

As AI coding tools go mainstream inside engineering orgs, their impact is reshaping everything from architecture decisions to mentorship patterns. But security is a blind spot for many teams, even as the risks multiply. 

Senior engineers need to be ready.

Three emerging AI security risks

AI coding tools are becoming a core part of the modern engineering stack. They’re used during development, for refactoring, for testing, and increasingly as part of CI/CD workflows. But the way they interact with the broader ecosystem, especially open-source packages and large context windows, creates entirely new vulnerabilities.

Let’s look at three categories of risk that engineers need to understand:

1. Inference-time risks (LLM behavior and model response)

Spotting subtle vulnerabilities in code has always been part of senior engineering work. AI doesn’t replace that responsibility; it increases the surface area where things can go wrong. 

Even when LLMs are used only for suggestions, they can introduce issues that require the same level of judgment and scrutiny senior engineers already apply to human-written code.

For example, you might ask an AI assistant to rewrite a utility for clarity or performance. If that utility touches authorization, rate limits, or API throttling, the assistant may remove guardrails in the name of “cleaner code.” At runtime, that becomes a latent vulnerability. One recent study found that 62% of AI-generated code solutions have design flaws or security vulnerabilities, even when using the latest models.

Greater access to production context doesn’t automatically make the model safer; it can make its outputs harder to reason about. If a model sees internal docs, environment details, or configuration hints, it can generate code with unexpected coupling or behavior — the kind of subtle drift that traditional review may not catch on first pass.

Senior engineers already own the skill of seeing around these corners. AI just adds a new, faster-moving layer where that same judgment stays essential..

2. Supply chain poisoning

Modern AI stacks have deep, tangled dependency trees, which means your risk surface grows long before you write any code of your own. When you rely on open-source libraries, pre-trained models, or external packages, you inherit every vulnerability upstream — and in AI ecosystems, those vulnerabilities spread fast.

Research on the LLM supply chain shows how quickly that risk spreads. For example, one study of the large language‑model supply‑chain found that “critical vulnerabilities propagate to an average of 142.1 downstream nodes at the second layer of dependency trees and peak at 237.8 nodes at the third layer.” In practice, this means a flaw in a single core model or library can ripple into hundreds of downstream packages your team depends on.

Another study found that over 75% of open‑source LLMs included vulnerable dependencies, and vulnerabilities lingered undisclosed for over 56 months on average. This isn’t theoretical. Attackers are already targeting model hubs and package repositories, uploading malicious AI-themed packages to PyPI and NPM that look legitimate but hide backdoors or poisoned logic. 

If your AI tooling ecosystem pulls from public artifacts — open-source packages, model weights, scaffolding tools, or community-maintained extensions — you may be downstream of a compromise without knowing it. And the more your organization depends on AI-powered tools and helpers, the more exposure you have to whatever weaknesses live in the supply chain itself.

3. Integration risks (model access and data architecture)

Even when the model behaves well and the training data is clean, integrating AI into engineering tools creates new surface area.

Consider prompt injection. When a model is embedded in dev tools, like a generator, summarizer, or internal assistant, prompt inputs might include test cases, user stories, or issue logs. If unsanitised, malicious input can cause unexpected model behaviour or data leaks.

When AI tools are deeply embedded, automatically summarizing issues, writing tests, or creating documentation, the prompt context may include sensitive internal logic. Without access controls and sandboxing, a seemingly innocuous model request could leak privileged information or accidentally expose your stack design to contractors, vendors, or even attackers who know how to manipulate output.

And once teams start connecting tools across systems (Slack → code → CI → production), LLMs can become a bridge between domains that weren’t meant to be joined. The model becomes a shadow integration layer, and if you’re not careful, a shadow leak.

What senior engineers need to do next

Security has always been part of the senior engineer’s job, but AI changes what that responsibility looks like.

Here’s how senior engineers can lead the charge:

Treat model AI tools like you would new services
Treat any AI assistant — internal or vendor-supplied — as a service with unknown behavior. Ask where it gets its data, how it’s sandboxed, and what it can see. Run red-team prompts or adversarial examples. Know what it logs.

Build workflows that prioritize review
AI can write a lot of code, fast. But that doesn’t mean it should be merged fast. Protect your team from regression by making review checkpoints visible and non-optional, especially for auth, data access, and infrastructure code.

Push for visibility across AI integrations
When AI tools touch multiple systems, audit logs, access control, and change tracking become essential. Fight for observability at the AI layer, not just the API or server level.

Mentor with security in mind
Your mentorship shouldn’t just be about good code anymore. It should include how to safely use AI tools, how to spot sketchy model behavior, and how to think about upstream risks. Model this mindset for your team.

Be diligent about collecting data
When you’re working with LLMs, every handoff matters. Be very conscious of where data flows and disciplined about sending only what’s required to the model. Extend your existing privacy and permission rules to cover anything that interacts with the LLM to protect the people behind the data.

Speed without safety won’t scale

The move to AI-augmented engineering isn’t slowing down. A growing body of research shows that productivity gains from AI are real and significant, especially for senior developers who know how to wield the tools well.

But the same tools that increase velocity also increase risk, and the faster your team moves, the less time there is to catch subtle security problems.

If you’re the most experienced person in the room, you’re now the first line of defense. AI might write the code. But senior engineers are the ones responsible for making sure it doesn’t compromise everything else.