LLM agents security duality: a comprehensive survey of self-security and empowered cybersecurity

• URL: http://arxiv.org/abs/2606.28450v1
• Date: Fri, 26 Jun 2026 09:32:11 GMT
• Title: LLM agents security duality: a comprehensive survey of self-security and empowered cybersecurity
• Abstract summary: Large language model (LLM) agents are rapidly being integrated into real-world systems.<n>This survey focuses on the role of LLM agents in empowering the cybersecurity lifecycle across offense and defense.
• Score: 13.20232770906974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 defense (LLM agents empowered cybersecurity). We first examine the internal and external attack surfaces of agents, propose a taxonomy organized by threat sources, and analyze associated mitigations and evaluation frameworks. We then investigate how agent capabilities are applied in cybersecurity practice and present, to our knowledge, the first agent-empowerment framework aligned with the full cyber offense-defense lifecycle. By systematically surveying these two areas, we are the first to highlight a positive feedback synergy between LLM agents self-security and empowered cybersecurity, offering new insights for the advancement of both. We further identify current limitations and outline promising directions for future research. The insights provided aim to catalyze the coordinated development of LLM agents self-security and agent empowered cybersecurity, paving the way for more capable and robust agent applications.

Related papers

• Toward Secure AI-Powered Penetration Testing Agents: Security Threats, Guardrails, and Architectural Perspectives [0.0]
  LLM-powered autonomous agents are transforming the penetration testing space with dynamic, multi-step offensive security.<n>But the ability to have persistent memory, to take actions in the real world, and to do long-horizon reasoning raises qualitatively different security concerns.<n>Existing guardrail mechanisms for conversational AI may not be sufficient to secure autonomous AI pentesting agents accordingly.
  arXiv  Detail & Related papers  (2026-09-15T06:19:50Z)
• LLM Scalability Risk for Agentic-AI and Model Supply Chain Security [1.1205855201611814]
  Large Language Models (LLMs) & Generative AI are transforming cybersecurity, enabling both advanced defenses and new attacks.<n>This paper presents a unified analysis integrating offensive and defensive perspectives on GenAI-driven cybersecurity.
  arXiv  Detail & Related papers  (2026-02-22T03:20:49Z)
• A Survey on Agentic Security: Applications, Threats and Defenses [6.83318476483428]
  The rapid shift from passive LLMs to autonomous LLM-agents marks a new paradigm in cybersecurity.<n>While these agents can act as powerful tools for both offensive and defensive operations, the very agentic context introduces a new class of inherent security risks.<n>We provide a comprehensive taxonomy of over 150 papers, explaining how agents are used, the vulnerabilities they possess, and the countermeasures designed to protect them.
  arXiv  Detail & Related papers  (2025-10-07T20:32:20Z)
• Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques [11.217261201018815]
  Large Language Models (LLMs) are transforming cybersecurity by enabling intelligent, adaptive, and automated approaches to threat detection, vulnerability assessment, and incident response.<n>With their advanced language understanding and contextual reasoning, LLMs surpass traditional methods in tackling challenges across domains such as IoT, blockchain, and hardware security.
  arXiv  Detail & Related papers  (2025-07-18T03:41:18Z)
• LLM Agents Should Employ Security Principles [60.03651084139836]
  This paper argues that the well-established design principles in information security should be employed when deploying Large Language Model (LLM) agents at scale.<n>We introduce AgentSandbox, a conceptual framework embedding these security principles to provide safeguards throughout an agent's life-cycle.
  arXiv  Detail & Related papers  (2025-05-29T21:39:08Z)
• From Assistants to Adversaries: Exploring the Security Risks of Mobile LLM Agents [17.62574693254363]
  We present the first comprehensive security analysis of mobile large language models (LLMs)<n>We identify security threats across three core capability dimensions: language-based reasoning, GUI-based interaction, and system-level execution.<n>Our analysis reveals 11 distinct attack surfaces, all rooted in the unique capabilities and interaction patterns of mobile LLM agents.
  arXiv  Detail & Related papers  (2025-05-19T11:17:46Z)
• AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents [54.29555239363013]
  We propose a generic black-box fuzzing framework, AgentVigil, to automatically discover and exploit indirect prompt injection vulnerabilities.<n>We evaluate AgentVigil on two public benchmarks, AgentDojo and VWA-adv, where it achieves 71% and 70% success rates against agents based on o3-mini and GPT-4o.<n>We apply our attacks in real-world environments, successfully misleading agents to navigate to arbitrary URLs, including malicious sites.
  arXiv  Detail & Related papers  (2025-05-09T07:40:17Z)
• Global Challenge for Safe and Secure LLMs Track 1 [57.08717321907755]
  The Global Challenge for Safe and Secure Large Language Models (LLMs) is a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks.
  arXiv  Detail & Related papers  (2024-11-21T08:20:31Z)
• Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends [64.57762280003618]
  It is foreseeable that in the near future, LM-driven general AI agents will serve as essential tools in production tasks.<n>This paper investigates scenarios involving the autonomous collaboration of future LM agents.
  arXiv  Detail & Related papers  (2024-09-22T14:09:49Z)
• The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies [58.94148083602662]
  Large Language Models (LLMs) agents have evolved to perform complex tasks.<n>The widespread applications of LLM agents demonstrate their significant commercial value.<n>However, they also expose security and privacy vulnerabilities.<n>This survey aims to provide a comprehensive overview of the newly emerged privacy and security issues faced by LLM agents.
  arXiv  Detail & Related papers  (2024-07-28T00:26:24Z)
• Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents [47.219047422240145]
  We take the first step to investigate one of the typical safety threats, backdoor attack, to LLM-based agents. Specifically, compared with traditional backdoor attacks on LLMs that are only able to manipulate the user inputs and model outputs, agent backdoor attacks exhibit more diverse and covert forms.
  arXiv  Detail & Related papers  (2024-02-17T06:48:45Z)
• TrustAgent: Towards Safe and Trustworthy LLM-based Agents [50.33549510615024]
  This paper presents an Agent-Constitution-based agent framework, TrustAgent, with a focus on improving the LLM-based agent safety. The proposed framework ensures strict adherence to the Agent Constitution through three strategic components: pre-planning strategy which injects safety knowledge to the model before plan generation, in-planning strategy which enhances safety during plan generation, and post-planning strategy which ensures safety by post-planning inspection.
  arXiv  Detail & Related papers  (2024-02-02T17:26:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.