Bad Actor Threat Modeling For AI Coding Assistants
Anyone who’s spent any time with an AI coding assistant knows that while they are incredibly useful to all levels of software developers, they also make mistakes. And if your AI coding assistant can generate bugs on your behalf, it only stands to reason that some of those might include security bugs.
One reason for this is that AI models are largely trained on sample code, and sample code is notorious for lacking proper error handling and demonstrating security best practices. I’d suggest this is largely by design: If secure coding is ancillary to the point you’re trying to convey when, say, answering a question on Stack Overflow or developing teaching or training material, it’s arguably pedagogically correct to omit such code. It gets in the way of the point that you need to convey. Sometimes people will add a “// security goes here” disclaimer comment. That’s fine—until an AI coding assistant starts echoing the code back as truth to everyone else on the Internet.
And because AI coding assistants look pretty magical (particularly to the junior devs that are supposedly “dying” — a claim I will dissect in an upcoming post), they start to occupy a special place of trust in the minds of software developers. AI-generated code may receive more benefit of the doubt in terms of correctness and quality than it really deserves.
These risks are fairly well understood already. But we need to go further and examine how these vulnerabilities could be exploited by bad actors.
Enter The Bad Actor
As AI coding assistants gain widespread adoption, they become an increasingly attractive target for exploitation. Bad actors, whether motivated by financial gain, sabotage, or espionage, will inevitably look for ways to leverage these systems. By manipulating training data, tricking users into trusting insecure code, or directly attacking the infrastructure that supports AI coding assistants, attackers could intentionally introduce security flaws at scale or even target specific organizations. Unlike traditional software vulnerabilities, which often require direct access to source code or system infrastructure, AI-generated vulnerabilities could be much more subtle and difficult to detect, yet equally dangerous. This makes it even more important to proactively understand and address the unique threats posed by bad actors in this space.
Let’s explore some attack vectors that bad actors could leverage to compromise AI-generated code.
1. Target the Model
A bad actor could potentially manipulate the AI model itself by introducing vulnerable code into the training pipeline. This type of attack could focus on poisoning the model's training data, particularly in open-source or community-driven datasets that AI assistants rely on. For instance, the attacker could inject security vulnerabilities disguised as common solutions or coding patterns, knowing that future AI-generated code might echo these flawed examples when responding to specific prompts. If enough such flawed code examples are included in the model’s training data, certain vulnerabilities could be intentionally introduced into various contexts without being flagged as suspicious.
2. Target an Organization
What’s more, now that enterprises are looking into training their own in-house models on proprietary code bases, they may become even more vulnerable to insider threats or sophisticated phishing-style attacks. An insider with malicious intent could subtly inject compromised code into the organization’s training data, making security bugs part of the AI’s suggestions within that company’s environment. Similarly, an external attacker with access to the organization’s training infrastructure — particularly one armed with knowledge of its security practices — could manipulate data sources more effectively, giving the model faulty data that leads to insecure code.
3. Target the UI
A bad actor doesn’t necessarily have to manipulate the AI model to cause harm. They could also target the coding assistant’s UI or infrastructure to insert malicious code. For example, a browser extension or compromised plugin designed to enhance AI suggestions could be tampered with. This tampered tool might subtly adjust or rewrite AI-generated code to introduce vulnerabilities in security-sensitive contexts, such as encryption handling or authentication logic. Additionally, if the requests between the UI and the AI are vulnerable to a man-in-the-middle attack, an attacker could intercept and alter responses in transit, injecting harmful code. This kind of attack can bypass AI models altogether and leverage the UI or network to inject flaws, creating a blind spot for organizations that assume the AI itself is secure.
Mitigation
There are several ways to mitigate these potential threats:
Train Developers on Secure Coding Practices
No matter how advanced AI coding assistants become, they should never be a substitute for developers who understand and practice secure coding techniques. Developers should be well-versed in spotting security vulnerabilities, even in AI-suggested code.Be Skeptical of AI-Generated Code
Developers need to remember that AI is a tool, not an infallible expert. AI-generated code, just like human-written code, should always be scrutinized for potential bugs and security risks. Developers should avoid blindly copying and pasting suggestions without reviewing them.Rigorous Code Reviews
AI-generated code should be subject to the same code review processes as human-written code. A strong code review process that includes experienced engineers will catch many potential vulnerabilities, reducing the likelihood that malicious or flawed code slips through.Monitor Training Pipelines
Organizations that are building or fine-tuning their own AI models should implement rigorous controls around their training pipelines. Regular audits and monitoring can help detect any attempts to inject malicious data into the training process. They should also consider adding security layers that prevent untrusted sources from contributing code to the model’s training data.Guard Against Targeted Attacks
Enterprises must be extra vigilant in safeguarding the specific security practices, packages, and libraries they implement. Bad actors may target commonly used or vulnerable components, so it’s critical to audit external dependencies and maintain tight control over what’s included in production environments. Ensuring that packages are up-to-date, reviewing third-party libraries, implementing a secure software supply chain, and avoiding outdated or insecure components are essential to reducing risk.
Concluding Thoughts
AI coding assistants are a new frontier in software development. But frontiers come with risks. Bad actors are likely to target the vulnerabilities of AI-generated code, and without careful oversight, these threats could compromise the systems we build. Software engineers are the last line of defense in maintaining secure practices—by questioning the code they receive, strengthening review processes, and guarding their development pipelines. AI is transforming software development, and with the right precautions, we can ensure this transformation enhances security rather than compromises it.
