AI Code Generation: Hidden Risks and Best Practices

Popular AI-powered code editors like Cursor are accelerating development, but at times, at a steep security cost. A newly disclosed flaw allows malicious repositories to execute arbitrary code the moment a folder is opened, exploiting Cursor’s default setting that disables Workspace Trust.  

Cursor’s autorun vulnerability highlights a growing risk in AI-assisted environments: speed and convenience often override secure defaults. Attackers can inject malicious code into public repos, triggering silent execution without user consent.  

Unlike Visual Studio Code, which gates risky actions behind explicit trust prompts, Cursor’s default behavior creates a direct compromise pathway. The flaw highlights old-school autorun exploits, underscoring the need for hardened defaults in AI-driven developer tools.  

However, the risk of AI-code assistants is not limited to Cursor; it’s a symptom of a broader trend: AI tools prioritize speed and usability, often at the expense of secure defaults.  

To understand the broader implications of AI-assisted development, here are four systemic risks that teams should watch for, and practical ways to mitigate these risks.  

Four Risks of AI Code Generation  

1) Insecure defaults at scale  

AI assistants optimize working code, not hardened code. Hardening means making code resilient, secure against tampering, misuse, and edge-case failure. AI tools often skip this step.  

Over time, AI code generation tools, if not set up correctly, can normalize weak input handling, permissive configs, or fragile error paths. These issues don’t trip unit tests, but they show up later in pen tests and production incidents as expensive rework.  

2) License and IP ambiguity  

AI-generated code may look clean and complete, but its legal origins are often murky. Even when vendors avoid direct copying, the lack of transparent provenance means developers can’t easily verify licensing or usage rights.   

Without clear rules, developers often guess what's legally acceptable, which frequently leads to copyright and IP law violations whenever they add code.  

3) Prompt and context leakage  

A common scenario is that a leaked token pasted into a prompt could end up in model training data or logs, creating a durable exposure that’s hard to trace or revoke.   

Once exposed, the data that originally entered a third-party system in the form of a prompt can persist in third-party systems. It will then create long-term compliance risks, breach trust, and trigger audit complications that are difficult to contain.  

4) Quiet Supply-Chain Creep  

AI tools often suggest new packages or build tweaks without calling attention to them. These small changes can slowly reshape your code’s dependencies, and increase your security risks, without anyone noticing. The real danger is the steady buildup of unreviewed updates that quietly expand your exposure.  

That being said, we know that the use of AI-generated code will only grow. According to GitHub’s latest survey of 2,000 software engineers, developers, and programmers, more than 97% of respondents reported having used AI coding tools at work.  

So, the best way to mitigate the risks is to put guardrails around the use of AI-code generation tools/assistants and adopt best practices.   

Best practices to mitigate AI-Code Generation Risks  

1. Establish a clear AI usage policy.  

The first step is to draft your AI usage policy. Begin by aligning your legal, security, and engineering teams on the definition of “responsible use” within your organization. Keep it short, plain-language, and easy to find.  

• Approved tools: List which AI coding assistants are allowed and why (security, logging, support).   

• Sensitive data rules: Define what must never enter a prompt—credentials, customer info, regulated identifiers.  

• License hygiene: Expect scrutiny of non-trivial output; use provenance tools where possible.  

• Accountability: Make it clear that engineers own every commit, whether AI-generated or not.  

2. Approve by task type  

Instead of giving blanket approvals to teams and departments, you can give approvals by task type. You can replace sprawling checklists with a simple, memorable model that sets out shared expectations without slowing delivery.  

🟢 Green (generally allowed): Boilerplate, adapters, tests, scaffolding, comments, docs, small utilities.  

🟠 Amber (allowed with an extra set of eyes): Data access logic, identity-adjacent changes, IaC/cloud templates, introducing new libraries.   

🔴 Red (human-led design first): Cryptography, payments, regulated flows, or anything resembling a third-party product.  

You can help developers self-govern by explaining the rationale behind each bucket and tagging the right reviewer for Amber tasks. Back it up with lightweight spot checks, rather than a heavy process.  

3. Implement guardrails  

To maintain speed while enhancing safety, enable these low-friction guardrails before broad rollout.  

• Short, clear policy: Define what’s OK to generate, what never belongs in a prompt (credentials, customer data), and how to escalate questions.  

• Obvious off-ramps: Provide a PR tag or quick design huddle for Amber work so people can get help without ceremony.  

• Gentle hygiene checks: Run friendly pre-commit or CI checks for secrets and obviously risky patterns, with brief explanations that teach.  

Visualize the policy in team dashboards or onboarding guides to keep it top-of-mind.  

4. Maintain Ownership of the Software Supply Chain  

AI assistants can silently reshape your build—adding dependencies, tweaking configs, and nudging your stack in new directions. To stay in control, make changes visible early and discuss tradeoffs. Add one PR line: “New or upgraded packages? List and why.”  

Instead of monitoring every commit, review monthly for drift. Look for packages outside your comfort zone or with policy-relevant licenses. And when delivery pressure forces a risky exception, time-box it, attach a revisit date. Speed is fine; however, don’t let risky changes become routine.  

5. Keep prompts and context inside approved boundaries.  

Leakage risk is mostly human convenience. You can make the safe path the easy path by following a simple workflow.  

• Describe/Write the “never list”: Keys, tokens, customer data, regulated identifiers.  

• Offer safer options, such as internal sandboxes, redaction helpers, or a “share this trace” tool that strips secrets by default.  

• Normalize asking: Engineers should know who to tag for gray areas and get quick, helpful responses.  

Takeaway  

As we wrote in one of our earlier blogs, the use of AI systems, including Agentic AI systems, will increase over time. Similarly, AI code generation does help teams ship more code, and it is here to stay. But you need to evaluate the four risks we identified (insecure defaults, IP ambiguity, prompt leakage supply-chain creep) and address them with a clear usage policy, a task-type approval model, and three lightweight controls that guide without slowing.   

The idea is to treat developer environments like production, prefer secure defaults, and make changes visible early. Do that, and incidents like Cursor’s autorun won’t become your problem tomorrow.  

In the context of mid-market firms and enterprise teams, we have previously discussed how tools like Microsoft 365 Copilot can be both a boon and a bane. It can act as a productivity partner for teams, such as those in manufacturing or other regulated industries, but also poses real risks if data governance is weak. 

If you want to check on these areas and have confidence in your team’s use of AI-code assistants like GitHub Copilot, Windsurf, or Cursor, our Internal Vulnerability Assessment & Risk Prioritization assessment can validate your current guardrails and prioritize the fixes to unlock safe adoption.