Why the autonomous AI hacker is a myth and new cybersecurity threats are more dangerous
In recent years, expectations around AI in cybersecurity have become a widespread fear: an autonomous AI hacker could soon bring entire companies and banks down at the push of a button. Events in 2026, however, showed that this 'digital Terminator' still does not exist, and the scale and nature of the threats have developed in a different, more complex way.
Despite the work invested, the analysis argues that a fully autonomous AI agent still cannot independently carry out complex, multi-stage attacks on corporate infrastructure. Isolated tasks such as collecting data from a particular website are already possible. But understanding complex network maps, bypassing advanced defences and escalating privileges require an experienced specialist. According to the source, these tasks remain beyond the reach of current autonomous models.
Experienced cybercrime groups, rather than newcomers, have gained the most from AI. They use it for routine operations: automated information gathering, adapting standard malicious code and accelerating attack preparation. Creative and complex work remains with people, preserving flexibility and effectiveness.
In controlled experiments, for example, AI agents can identify and exploit known vulnerabilities in web applications. But as soon as the infrastructure becomes more complex, progress stalls without human intervention. Some popular projects promising 'unrestricted hacking AI' proved to be marketing myths. In practice, they were adapted open language models with additional filtering, unable to carry out real exploitation tasks.
For cybercriminals today, legitimate commercially available AI models are far more effective than improvised darknet solutions. Another approach is buying services that bypass safeguards in major AI systems, known as Jailbreak as a Service. This allows flexible use of popular providers' computing power while circumventing restrictions.
AI has not created fundamentally new types of attack. It has refined and accelerated existing methods. For example, phishing messages now use correct language and extensive personalisation based on data gathered from social media and corporate blogs. Voice deepfakes created from minimal audio samples can trick accounting teams with calls impersonating an executive.
Malware is integrating AI directly into infrastructure. Local neural processors let malicious software analyse screenshots, identify banking interface elements and extract confidential data without extra network traffic, making detection harder. Some samples do not store attack logic locally at all, instead accessing cloud AI services while disguising their requests as legitimate traffic.
A key metric for defenders is the time between discovery of a software vulnerability and the emergence of a working exploit. AI has shortened this from days or weeks to hours or even minutes, as algorithms quickly find code flaws and generate working proof-of-concept attack scripts.
The source identifies internal AI use as the greater corporate risk. Employees share sensitive business information with public chatbots on a large scale, creating data leakage and intellectual property risks because third parties may use that information to train their models. Traditional data protection systems struggle with this type of activity: the traffic looks like an ordinary web interaction.
AI coding assistants have accelerated programming, but do not always improve security. Training on public repositories containing vulnerabilities can lead to insecure generated code. Developers may use it without adequate review, allowing flaws to spread into critical systems.
AI infrastructure is also a target, from servers and cloud platforms to API keys. Stealing these resources lets attackers use others' computing capacity for purposes such as mining or brute-force attacks.
AI agents with access to corporate systems introduce a separate category of risk. Specialists highlight prompt injection: instructions that alter an agent's behaviour and can cause data loss or financial damage. Open-source libraries for building AI workflows can also become entry points for cyberattacks.
Statistics show AI use in cyberattacks growing mainly in the early stages: reconnaissance, information gathering and initial intrusion. Moving from entry to persistence and bypassing advanced protection still requires skilled specialists.
The main business vulnerabilities ultimately lie in familiar weaknesses rather than breakthrough AI technology: weak passwords, missing multifactor authentication, outdated software and human error, such as accidental clicks on malicious links. AI has simply shortened the time companies have to close these gaps.
Business owners and marketers need a systematic approach to AI security and a clear understanding of its limits. As specialists recommend, integrated digital marketing and analytics should be accompanied by responsible technology management to avoid creating vulnerabilities and use AI's capabilities effectively without unnecessary risk.
