In early 2024, a putting deepfake fraud case in Hong Kong introduced the vulnerabilities of AI-driven deception into sharp aid. A finance worker was duped throughout a video name by what gave the impression to be the CFO—however was, actually, a classy AI-generated deepfake. Satisfied of the decision’s authenticity, the worker made 15 transfers totaling over $25 million to fraudulent financial institution accounts earlier than realizing it was a rip-off.
This incident exemplifies extra than simply technological trickery—it alerts how belief in what we see and listen to could be weaponized, particularly as AI turns into extra deeply built-in into enterprise instruments and workflows. From embedded LLMs in enterprise techniques to autonomous brokers diagnosing and even repairing points in stay environments, AI is transitioning from novelty to necessity. But because it evolves, so too do the gaps in our conventional safety frameworks—designed for static, human-written code—revealing simply how unprepared we’re for techniques that generate, adapt, and behave in unpredictable methods.
Past the CVE Mindset
Conventional safe coding practices revolve round recognized vulnerabilities and patch cycles. AI modifications the equation. A line of code could be generated on the fly by a mannequin, formed by manipulated prompts or information—creating new, unpredictable classes of threat like immediate injection or emergent habits exterior conventional taxonomies.
A 2025 Veracode examine discovered that 45% of all AI-generated code contained vulnerabilities, with widespread flaws like weak defenses towards XSS and log injection. (Some languages carried out extra poorly than others. Over 70% of AI-generated Java code had a safety difficulty, as an example.) One other 2025 examine confirmed that repeated refinement could make issues worse: After simply 5 iterations, important vulnerabilities rose by 37.6%.
To maintain tempo, frameworks just like the OWASP Prime 10 for LLMs have emerged, cataloging AI-specific dangers resembling information leakage, mannequin denial of service, and immediate injection. They spotlight how present safety taxonomies fall quick—and why we want new approaches that mannequin AI menace surfaces, share incidents, and iteratively refine threat frameworks to replicate how code is created and influenced by AI.
Simpler for Adversaries
Maybe probably the most alarming shift is how AI lowers the barrier to malicious exercise. What as soon as required deep technical experience can now be carried out by anybody with a intelligent immediate: producing scripts, launching phishing campaigns, or manipulating fashions. AI doesn’t simply broaden the assault floor; it makes it simpler and cheaper for attackers to succeed with out ever writing code.
In 2025, researchers unveiled PromptLock, the primary AI-powered ransomware. Although solely a proof of idea, it confirmed how theft and encryption could possibly be automated with a neighborhood LLM at remarkably low price: about $0.70 per full assault utilizing business APIs—and primarily free with open supply fashions. That form of affordability may make ransomware cheaper, quicker, and extra scalable than ever.
This democratization of offense means defenders should put together for assaults which might be extra frequent, extra assorted, and extra artistic. The Adversarial ML Risk Matrix, based by Ram Shankar Siva Kumar throughout his time at Microsoft, helps by enumerating threats to machine studying and providing a structured method to anticipate these evolving dangers. (He’ll be discussing the issue of securing AI techniques from adversaries at O’Reilly’s upcoming Safety Superstream.)
Silos and Talent Gaps
Builders, information scientists, and safety groups nonetheless work in silos, every with totally different incentives. Enterprise leaders push for speedy AI adoption to remain aggressive, whereas safety leaders warn that shifting too quick dangers catastrophic flaws within the code itself.
These tensions are amplified by a widening abilities hole: Most builders lack coaching in AI safety, and plenty of safety professionals don’t absolutely perceive how LLMs work. Because of this, the outdated patchwork fixes really feel more and more insufficient when the fashions are writing and working code on their very own.
The rise of “vibe coding”—counting on LLM recommendations with out evaluate—captures this shift. It accelerates growth however introduces hidden vulnerabilities, leaving each builders and defenders struggling to handle novel dangers.
From Avoidance to Resilience
AI adoption gained’t cease. The problem is shifting from avoidance to resilience. Frameworks like Databricks’ AI Danger Framework (DASF) and the NIST AI Danger Administration Framework present sensible steering on embedding governance and safety straight into AI pipelines, serving to organizations transfer past advert hoc defenses towards systematic resilience. The aim isn’t to get rid of threat however to allow innovation whereas sustaining belief within the code AI helps produce.
Transparency and Accountability
Analysis reveals AI-generated code is commonly less complicated and extra repetitive, but additionally extra weak, with dangers like hardcoded credentials and path traversal exploits. With out observability instruments resembling immediate logs, provenance monitoring, and audit trails, builders can’t guarantee reliability or accountability. In different phrases, AI-generated code is extra prone to introduce high-risk safety vulnerabilities.
AI’s opacity compounds the issue: A perform might seem to “work” but conceal vulnerabilities which might be troublesome to hint or clarify. With out explainability and safeguards, autonomy shortly turns into a recipe for insecure techniques. Instruments like MITRE ATLAS may also help by mapping adversarial ways towards AI fashions, providing defenders a structured method to anticipate and counter threats.
Wanting Forward
Securing code within the age of AI requires greater than patching—it means breaking silos, closing talent gaps, and embedding resilience into each stage of growth. The dangers might really feel acquainted, however AI scales them dramatically. Frameworks like Databricks’ AI Danger Framework (DASF) and the NIST AI Danger Administration Framework present constructions for governance and transparency, whereas MITRE ATLAS maps adversarial ways and real-world assault case research, giving defenders a structured method to anticipate and mitigate threats to AI techniques.
The alternatives we make now will decide whether or not AI turns into a trusted companion—or a shortcut that leaves us uncovered.
