arXiv:2606.28929v1 Announce Type: cross Abstract: Cybersecurity is a real-life test-bed for many machine learning problems at once, especially when considering modern strides in using Large Language Models (LLMs) to automate processes as ``agents.'' Cybersecurity workflows require orchestrating hundreds of standard and bespoke tools through various formats. The scale of cybersecurity data is enormous; for example, a single malware sample can be viewed as a sequence of billions of tokens. The cost of labeling any file by experts is enormous and labor-intensive, in part because an adversary (pos
The rapid advancement and integration of Large Language Models (LLMs) into critical infrastructure like cybersecurity workflows makes their vulnerabilities and potential as autonomous agents a pressing concern.
The success or failure of generative AI in cybersecurity will directly impact global digital security, economic stability, and the trustworthiness of AI-driven automation across all sectors.
The battlefield for cybersecurity is evolving from human-versus-human or human-versus-bot to increasingly bot-versus-bot (AI-versus-AI), demanding new defensive paradigms and offensive capabilities.
- · Cybersecurity AI developers and researchers
- · Organizations with robust AI-driven security defenses
- · Security-focused AI infrastructure providers
- · Organizations slow to adopt AI-driven defenses
- · Bad actors leveraging unsophisticated AI
- · Traditional cybersecurity firms without AI expertise
Increased investment in AI-driven cybersecurity solutions to operationalize LLMs as agents for both defense and offense.
A new arms race in AI capabilities between cyber defenders and attackers, leading to more sophisticated and autonomous cyber warfare.
Potential for catastrophic cascading failures if autonomous AI agents in critical infrastructure are compromised or misperform in cyber defense.
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Read at arXiv cs.LG