Advances in large language models and machine learning have transformed the threat landscape, making it easier for less-experienced attackers to develop sophisticated breaches. With AI’s ability to generate scripts and provide step-by-step hacking guidance, criminals no longer need a deep technical background to pose a serious threat.
LLM jacking
As businesses rush to integrate AI features into their products, hastily built implementations often contain vulnerabilities that attackers exploit. By discovering leaked or weak credentials, cybercriminals can hijack large language model (LLM) endpoints and use them for illegitimate queries—potentially racking up massive charges and wreaking havoc on operational costs.
Prolonged infiltration
Many cyberattacks used to be quick hits, aiming for immediate payoffs. Now, slow and methodical intrusions are on the rise. Thanks to AI, even average attackers can learn advanced tactics. This shift puts organizations at risk of deeper and more damaging breaches, as criminals probe systems, collect data, and remain undetected for longer periods.
Threat modeling
Recognizing how these threats evolve is crucial. Threat modeling, based on frameworks like Atlas Mitre, helps visualize how attackers move through a network and identify which defenses can break that chain. By studying real-world breaches and applying lessons learned, security teams can strengthen their systems before an intrusion happens.
Deepfakes
Phishing attacks are no longer limited to emails and links. AI-generated deepfakes now mimic voices, video calls, and personal identities with alarming accuracy. Automated checks, multi-factor authentication, and verified approval processes are becoming vital to ensure that actions taken are truly authorized, rather than prompted by a convincing AI-powered impostor.
Evolving security tools
From anomaly detection to comprehensive security solutions, many vendors tout AI-powered products. While some of these tools offer sophisticated methods of identifying unusual behavior, attackers who move more slowly can circumvent purely pattern-based systems. Vigilance against both technical exploits and overhyped marketing claims remains essential.
