When AI Writes Code, Who Secures It?

In early 2024, a striking deepfake fraud case in Hong Kong brought the vulnerabilities of AI-driven deception into sharp relief. A finance employee was duped during a video call by what appeared to be the CFO—but was, in fact, a sophisticated AI-generated deepfake. Convinced of the call’s authenticity, the employee made 15 transfers totaling over $25 million to fraudulent bank accounts before realizing it was a scam.

This incident exemplifies more than just technological trickery—it signals how trust in what we see and hear can be weaponized, especially as AI becomes more deeply integrated into enterprise tools and workflows. From embedded LLMs in enterprise systems to autonomous agents diagnosing and even repairing issues in live environments, AI is transitioning from novelty to necessity. Yet as it evolves, so too do the gaps in our traditional security frameworks—designed for static, human-written code—revealing just how unprepared we are for systems that generate, adapt, and behave in unpredictable ways.

Beyond the CVE Mindset

Traditional secure coding practices revolve around known vulnerabilities and patch cycles. AI changes the equation. A line of code can be generated on the fly by a model, shaped by manipulated prompts or data—creating new, unpredictable categories of risk like prompt injection or emergent behavior outside traditional taxonomies.

A 2025 Veracode study found that 45% of all AI-generated code contained vulnerabilities, with common flaws like weak defenses against XSS and log injection. (Some languages performed more poorly than others. Over 70% of AI-generated Java code had a security issue, for instance.) Another 2025 study showed that repeated refinement can make things worse: After just five iterations, critical vulnerabilities rose by 37.6%.

To keep pace, frameworks like the OWASP Top 10 for LLMs have emerged, cataloging AI-specific risks such as data leakage, model denial of service, and prompt injection. They highlight how current security taxonomies fall short—and why we need new approaches that model AI threat surfaces, share incidents, and iteratively refine risk frameworks to reflect how code is created and influenced by AI.

Easier for Adversaries

Perhaps the most alarming shift is how AI lowers the barrier to malicious activity. What once required deep technical expertise can now be done by anyone with a clever prompt: generating scripts, launching phishing campaigns, or manipulating models. AI doesn’t just broaden the attack surface; it makes it easier and cheaper for attackers to succeed without ever writing code.

In 2025, researchers unveiled PromptLocker, the first AI-powered ransomware. Though only a proof of concept, it showed how theft and encryption could be automated with a local LLM at remarkably low cost: about $0.70 per full attack using commercial APIs—and essentially free with open source models. That kind of affordability could make ransomware cheaper, faster, and more scalable than ever.

This democratization of offense means defenders must prepare for attacks that are more frequent, more varied, and more creative. The Adversarial ML Threat Matrix, founded by Ram Shankar Siva Kumar during his time at Microsoft, helps by enumerating threats to machine learning and offering a structured way to anticipate these evolving risks. (He’ll be discussing the difficulty of securing AI systems from adversaries at O’Reilly’s upcoming Security Superstream.)

Silos and Skill Gaps

Developers, data scientists, and security teams still work in silos, each with different incentives. Business leaders push for rapid AI adoption to stay competitive, while security leaders warn that moving too fast risks catastrophic flaws in the code itself.

These tensions are amplified by a widening skills gap: Most developers lack training in AI security, and many security professionals don’t fully understand how LLMs work. As a result, the old patchwork fixes feel increasingly inadequate when the models are writing and running code on their own.

The rise of “vibe coding”—relying on LLM suggestions without review—captures this shift. It accelerates development but introduces hidden vulnerabilities, leaving both developers and defenders struggling to manage novel risks.

From Avoidance to Resilience

AI adoption won’t stop. The challenge is moving from avoidance to resilience. Frameworks like Databricks’ AI Risk Framework (DASF) and the NIST AI Risk Management Framework provide practical guidance on embedding governance and security directly into AI pipelines, helping organizations move beyond ad hoc defenses toward systematic resilience. The goal isn’t to eliminate risk but to enable innovation while maintaining trust in the code AI helps produce.

Transparency and Accountability

Research shows AI-generated code is often simpler and more repetitive, but also more vulnerable, with risks like hardcoded credentials and path traversal exploits. Without observability tools such as prompt logs, provenance tracking, and audit trails, developers can’t ensure reliability or accountability. In other words, AI-generated code is more likely to introduce high-risk security vulnerabilities.

AI’s opacity compounds the problem: A function may appear to “work” yet conceal vulnerabilities that are difficult to trace or explain. Without explainability and safeguards, autonomy quickly becomes a recipe for insecure systems. Tools like MITRE ATLAS can help by mapping adversarial tactics against AI models, offering defenders a structured way to anticipate and counter threats.

Looking Ahead

Securing code in the age of AI requires more than patching—it means breaking silos, closing skill gaps, and embedding resilience into every stage of development. The risks may feel familiar, but AI scales them dramatically. Frameworks like Databricks’ AI Risk Framework (DASF) and the NIST AI Risk Management Framework provide structures for governance and transparency, while MITRE ATLAS maps adversarial tactics and real-world attack case studies, giving defenders a structured way to anticipate and mitigate threats to AI systems.

The choices we make now will determine whether AI becomes a trusted partner—or a shortcut that leaves us exposed.