arXiv:2605.26195v1 Announce Type: cross Abstract: LLM-based agents are increasingly used for cybersecurity tasks, but most existing systems rely on fixed, human-designed scaffolds that struggle to adapt across diverse targets and failure modes. We introduce \textsc{CyberEvolver}, a self-evolving cybersecurity agent framework that iteratively revises its own scaffold based on experience from failed execution attempts. Self-evolution in cybersecurity is challenging because the space of possible scaffold changes is largely unstructured, execution feedback is sparse and often obscured by the envir
The proliferation of LLM-based agents in cybersecurity creates an immediate need for adaptive, self-improving defense mechanisms as static systems prove insufficient against evolving threats.
This development indicates a significant leap in AI agent capabilities, specifically their ability to self-evolve and adapt 'on the fly,' which is critical for dynamic and adversarial environments like cybersecurity.
Cybersecurity agents can now move beyond fixed, human-designed scaffolds, enabling more resilient and autonomous defense systems that learn and adapt from failures directly.
- · Cybersecurity industry
- · Organizations with advanced threat landscapes
- · AI agent developers
- · Threat actors relying on static defenses
- · Cybersecurity solutions with fixed architectures
- · Human-in-the-loop security analysts (for routine tasks)
Cybersecurity defenses become more robust and automated due to self-evolving AI agents.
This capability could extend to other adversarial AI applications, accelerating agent self-improvement in various domains beyond cybersecurity.
The development of truly self-evolving AI agents poses new challenges for control, ethics, and explaining their decision-making processes.
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Read at arXiv cs.AI