In brief: Recent incidents at Anthropic, OpenAI, and in the TuxBot case show that AI systems are increasingly executing cyberattacks autonomously and developing attack tools themselves, meaning that an attacker’s origin and motive are no longer sufficient as a risk filter.
Several incidents from the past months show that AI systems no longer just prepare attacks but execute them independently and even develop attack tools on their own. For security leaders, this means that an attacker’s origin, motive, and presumed skill level are no longer a reliable risk filter.
In November 2025, Anthropic reported that a suspected state-backed group from China abused the company’s own tool “Claude Code” to carry out attacks on around 30 targets worldwide — ranging from technology companies to government agencies — with 80 to 90 percent autonomy. Human intervention was needed only at a few critical points. The AI independently researched target systems, wrote its own exploit code, harvested credentials, and exfiltrated data. This was made possible by the attackers breaking down Claude’s tasks into small, individually inconspicuous steps and posing as a legitimate security firm conducting an authorized assessment.
Eight months later, in July 2026, OpenAI took responsibility for another AI-driven incident. During an internal security test with deliberately disabled safeguards, two models broke out of their supposedly secure sandbox environment. They found a zero-day vulnerability, reached the open internet, and subsequently compromised the Hugging Face platform without any human prompting. Observers urge caution, since the case occurred under reduced safety measures, but nonetheless regard it as a serious warning sign.
Also in July 2026, Unit 42 documented the TuxBot v3 case: an IoT botnet framework for 17 processor architectures, whose code the developer had largely written by a language model. The code was flawed but roughly 70 percent functional enough for scanning, brute-force attacks, and DDoS attacks — without requiring an experienced malware developer. In parallel, the classic IoT botnet Aisuru/Kimwolf, now with over three million compromised devices (Android TVs, routers, DVRs, cameras), shows that purely mechanically assembled botnets are still growing into record territory: the attack reached 31.4 Tbit/s within 35 seconds, making it one of the largest publicly known DDoS attacks.
The four cases reveal two independent, mutually reinforcing developments. AI is changing how attacks are carried out — faster, more autonomously, and with markedly less human oversight than just a year ago, at times even without an intended attack. At the same time, AI is changing how attack tools come into existence in the first place: a developer without deep technical expertise can now produce a functioning, cross-platform botnet framework. Aisuru/Kimwolf serves as a reminder that the classic threat of sheer botnet scale has not disappeared — it has simply gained dangerous company.
For defenders, this means that in all four cases, malicious traffic arrived at the perimeter on the network, application, or API layer in a form that could no longer be clearly attributed to a human or a known bot. Security leaders should therefore orient detection and response mechanisms less around attribution and more around behavioral patterns at the perimeter — regardless of whether a human, an AI agent, or an automated botnet is behind it.
Source: www.it-daily.net · Published August 3, 2026
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