In early 2024, a hanging deepfake fraud case in Hong Kong introduced the vulnerabilities of AI-driven deception into sharp reduction. A finance worker was duped throughout a video name by what gave the impression to be the CFO—however was, in reality, 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 identified vulnerabilities and patch cycles. AI adjustments the equation. A line of code could be generated on the fly by a mannequin, formed by manipulated prompts or knowledge—creating new, unpredictable classes of threat like immediate injection or emergent conduct exterior conventional taxonomies.
A 2025 Veracode research discovered that 45% of all AI-generated code contained vulnerabilities, with widespread flaws like weak defenses in opposition to XSS and log injection. (Some languages carried out extra poorly than others. Over 70% of AI-generated Java code had a safety problem, for example.) One other 2025 research confirmed that repeated refinement could make issues worse: After simply 5 iterations, essential vulnerabilities rose by 37.6%.
To maintain tempo, frameworks just like the OWASP High 10 for LLMs have emerged, cataloging AI-specific dangers akin to knowledge leakage, mannequin denial of service, and immediate injection. They spotlight how present safety taxonomies fall brief—and why we want new approaches that mannequin AI menace surfaces, share incidents, and iteratively refine threat frameworks to mirror how code is created and influenced by AI.
Simpler for Adversaries
Maybe essentially the most alarming shift is how AI lowers the barrier to malicious exercise. What as soon as required deep technical experience can now be executed 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 PromptLocker, the primary AI-powered ransomware. Although solely a proof of idea, it confirmed how theft and encryption may very well be automated with a neighborhood LLM at remarkably low value: about $0.70 per full assault utilizing business APIs—and basically free with open supply fashions. That sort of affordability may make ransomware cheaper, quicker, and extra scalable than ever.
This democratization of offense means defenders should put together for assaults which can be extra frequent, extra assorted, and extra inventive. 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 strategy 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, knowledge 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 expertise hole: Most builders lack coaching in AI safety, and lots of safety professionals don’t totally perceive how LLMs work. In consequence, the outdated patchwork fixes really feel more and more insufficient when the fashions are writing and operating code on their very own.
The rise of “vibe coding”—counting on LLM ideas 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 received’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 steerage on embedding governance and safety straight into AI pipelines, serving to organizations transfer past advert hoc defenses towards systematic resilience. The objective isn’t to remove threat however to allow innovation whereas sustaining belief within the code AI helps produce.
Transparency and Accountability
Analysis exhibits AI-generated code is commonly less complicated and extra repetitive, but in addition extra weak, with dangers like hardcoded credentials and path traversal exploits. With out observability instruments akin to 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 could seem to “work” but conceal vulnerabilities which can be troublesome to hint or clarify. With out explainability and safeguards, autonomy rapidly turns into a recipe for insecure techniques. Instruments like MITRE ATLAS may help by mapping adversarial techniques in opposition to AI fashions, providing defenders a structured strategy to anticipate and counter threats.
Trying 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 could 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 techniques and real-world assault case research, giving defenders a structured strategy to anticipate and mitigate threats to AI techniques.
The alternatives we make now will decide whether or not AI turns into a trusted associate—or a shortcut that leaves us uncovered.
