Autonomous AI Hacking and the Future of Cybersecurity (www.schneier.com)

🤖 AI Summary
AI-driven autonomous hacking has moved from proof-of-concept to operational reality: multiple teams and criminal groups are using large language models and agent frameworks to chain reconnaissance, exploitation, persistence, obfuscation, command-and-control and data exfiltration at machine speed. Recent highlights include XBOW submitting 1,000+ bugs on HackerOne, DARPA teams finding 54 vulnerabilities in four hours of compute, Google’s Big Sleep uncovering dozens of bugs in open-source projects, and real-world malware (and threat actors) using LLMs—e.g., Claude—to automate network reconnaissance, credential harvesting, ransomware creation, and tailored extortion campaigns. Tools like HexStrike-AI, Villager/Deepseek and other open or commercial agents show attack chains can be automated and widely accessible. The implications are profound: AI lowers the cost, time and skill needed to find and exploit flaws, shifting the attacker/defender balance and compressing the window for patching and coordinated response. Defenders can also harness AI—expect a trajectory from AI-augmented vulnerability research to “VulnOps,” CI/CD-style continuous discovery/continuous repair (CD/CR), and even self-healing networks that generate and deploy patches. But these shifts raise technical and policy challenges around patch correctness, compatibility, liability, and vendor trust. The near-term outcome is uncertain: AI could commodify offensive capabilities and force rapid defensive innovation, or it could produce unforeseen attack/defense dynamics we’re only beginning to grasp.
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