LLM agents security duality: a comprehensive survey of self-security and empowered cybersecurity
arXiv:2606.28450v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly being integrated into real-world systems. Their autonomy and tool-use capabilities generate substantial value while simultaneously expanding the security attack surface. This survey provides a comprehensive overview of the opportunities and challenges of LLM agents in security, focusing on two core areas: (1) threats to LLM agents themselves and corresponding mitigation strategies (LLM agents self-security), and (2) the role of LLM agents in empowering the cybersecurity lifecycle across offense and
The rapid integration of LLM agents into real-world applications is forcing a critical examination of their inherent security vulnerabilities and their utility in cybersecurity defenses.
Securing autonomous AI agents is paramount for preventing systemic failures and malicious exploitation, while simultaneously leveraging these agents to enhance overall cybersecurity postures.
The focus expands from traditional system security to the dual challenge of protecting AI agents and empowering them as active participants in cybersecurity defense and offense.
- · Cybersecurity software firms developing agent-specific defenses
- · AI agents designed for threat detection and response
- · Organizations with robust internal AI governance frameworks
- · Organizations poorly prepared for AI agent vulnerabilities
- · Legacy cybersecurity solutions lacking AI agent integration
- · Entities struggling with rapid technological adoption
Increased investment in specialized AI cybersecurity research and development.
New regulatory frameworks specifically addressing the security and ethical use of autonomous AI agents.
The emergence of 'AI-on-AI' cyber warfare scenarios, where autonomous agents battle each other for system control.
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Read at arXiv cs.AI