5 AI vulnerabilities that can break your security posture

By Sharda Tickoo, Country Manager for India & SAARC, Trend Micro

Artificial intelligence promises efficiency and protection, but it brings new vulnerabilities. As AI systems become ubiquitous across organisations, they’re increasingly targeted by threat actors who exploit the same capabilities designed to defend us.

Numbers tell a darker story. 93% of security leaders are now preparing for daily AI onslaughts in 2025. The threat landscape has shifted from human adversaries probing for weaknesses to autonomous systems that learn, adapt, and strike at machine speed. Traditional cybersecurity operated like a chess match. It was strategic, deliberate, with time to think between moves. Today’s reality resembles speed chess against an opponent who sees a thousand moves ahead and never misses.

Trend Micro’s recent research has revealed five critical vulnerabilities that are already rewriting the rules of engagement, from elite hacking contests to exposed production servers and underground criminal markets.

1. Hijacking intelligence through hidden commands

Imagine a trusted advisor following instructions you can’t see. This is prompt injection. Attackers embed malicious directives within content that AI processes, invisible to human oversight. The Link Trap technique exemplifies the impact of the threat. Attackers manipulate GenAI models into crafting responses with malicious URLs masked as innocuous reference links. Just one click, and your data flows silently to adversary-controlled servers. More sophisticated variants leverage Unicode characters that remain imperceptible in user interfaces, creating what amounts to invisible ink for the digital age. These attacks subvert trust itself, transforming AI assistants from productivity tools into unwitting accomplices in data exfiltration.

2. Exposing AI’s chain of thought is a liability

DeepSeek-R1’s approach to transparency that prominently displayed its reasoning process, seemed like a milestone in trustworthy AI. Instead, it inadvertently provided adversaries with a roadmap to manipulation. When AI models reveal their internal logic, they hand attackers the blueprint for crafting precisely targeted prompts. This vulnerability crystallises a paradox at the heart of AI development, a feature that build user confidence can simultaneously architect pathways for compromise. As agentic AI systems grow more sophisticated, this tension between explainability and security will only intensify, demanding new frameworks that preserve transparency without weaponising it.

3. The Achilles’ heel hidden in dependencies

In complex AI infrastructure, security is only as strong as the weakest component. Third-party libraries, subsystems, and dependencies form an invisible web of potential compromise, components often considered “stable” and thus exempt from regular scrutiny. The solution demands vigilance that matches the complexity like comprehensive inventories, continuous assessment, and the recognition that maturity does not equal immunity.

4. When AI attacks itself

In the escalating arms race between AI-powered attacks and defences, deep fakes represent the great equaliser. 36% of consumers have encountered deepfake-based scams, a testament to how accessible sophisticated attack tools have become. Mainstream platforms now offer real-time video manipulation, multilingual voice cloning, and facial synthesis at marginal cost. Criminals are weaponising these capabilities to bypass electronic Know-Your-Customer systems protecting financial platforms. This is how AI versus AI looks. The battleground is identity itself, and traditional authentication paradigms built on “something you are” crumble when appearance becomes infinitely malleable.

5. Prompt leakage – stealing the system’s guidebook

More than a compromise in data, the Prompt Leakage (PLeak) exposes the very architecture of AI systems. By circumventing built-in security restrictions, PLeak divulges system prompts and fine-tuning details that constitute an AI’s operational blueprint. Once attackers possess these architectural insights, they can craft increasingly sophisticated, precisely targeted exploits. It is the difference between breaking into a building and stealing the building’s blueprints. The latter enables an entirely different class of threat.

The path forward is a security posture that learns as fast as the threats evolve

With AI systems gaining autonomy and evolving behaviour dynamically, conventional security models rooted in static perimeters are becoming obsolete. The threat environment moves at machine speed and the defence infrastructure must pace up or risk extinction.

Cybersecurity needs to cut across and should be embedded throughout the AI lifecycle rather than bolting it on after deployment. Comprehensive software inventories, continuous security assessments, rigorous output filtering, and persistent red teaming must become baseline practices. The organisations that thrive will be those that evolve as fast as their adversaries, treating security as a living, adaptive, intelligence-driven system.