arXiv AI
Jul 23
The Ethics of Autonomous AI Agents for Offensive Security
arXiv:2607. 20255v1 Announce Type: cross Abstract: LLM-driven autonomous agents are reshaping offensive security.
A New Framework for Cybersecurity Refusals in AI Agents
arXiv:2606. 02644v1 Announce Type: cross Abstract: Agentic scaffolds have dramatically improved LLM performance on complex, long-horizon tasks, yielding both broad benefits and amplified risks in domains like cybersecurity.
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arXiv AI
Jun 12
The Emergence of Autonomous Penetration Capabilities in Large Language Model-Powered AI Systems
arXiv:2606. 13079v1 Announce Type: cross Abstract: Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical red lines that frontier AI systems must not cross.
arXiv AI
Jul 29
Cyber-Capable AI Agents: Vulnerabilities, Evaluation Containment, and Defensive Response
arXiv:2607. 25379v1 Announce Type: new Abstract: Cyber-capable AI agents combine language models with tools, memory, and execution en- vironments to perform multi-step offensive-security tasks.
arXiv AI
Jun 15
AgentCyberRange: Benchmarking Frontier AI Systems in Realistic Cyber Ranges
arXiv:2606. 14295v1 Announce Type: cross Abstract: Frontier AI systems are increasingly capable of cybersecurity tasks, including codebase inspection, vulnerability detection, and exploitation.
arXiv AI
Jun 30
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.
arXiv AI
Jun 16
MUZZLE: Adaptive Agentic Red-Teaming of Web Agents Against Indirect Prompt Injection Attacks
arXiv:2602. 09222v2 Announce Type: replace-cross Abstract: Large language model (LLM) based web agents are increasingly deployed to automate complex online tasks by directly interacting with web sites and performing actions on users' behalf.
arXiv Machine Learning
Jun 30
Cybersecurity is the True Frontier for Generative AI Success or Failure
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.
arXiv AI
Jun 16
A Survey on Agentic Security: Applications, Threats and Defenses
arXiv:2510. 06445v3 Announce Type: replace-cross Abstract: LLM-based agents are now used throughout cybersecurity.
arXiv AI
Jun 11
Can Open-Source LLM Agents Replace Static Application Security Testing Tools? An Empirical Assessment
arXiv:2606. 11672v1 Announce Type: cross Abstract: This paper explores the value of agentic AI tools for cybersecurity purposes.
arXiv AI
Jul 3
Behind the Refusal: Determining Guardrail Activation via Behavioral Monitoring
arXiv:2607. 02121v1 Announce Type: cross Abstract: As Large Language Models (LLMs) and agentic systems become integrated into real-world applications, ensuring their safety and security is critical.
arXiv Machine Learning
Aug 11
Benchmarking the Robustness of Agentic Systems to Adversarially-Induced Harms
arXiv:2508. 16481v3 Announce Type: replace Abstract: Ensuring the safe use of agentic systems requires a thorough understanding of the range of malicious behaviors these systems may exhibit.
arXiv AI
Jun 19
The Autonomy Tax: Defense Training Breaks LLM Agents
arXiv:2603. 19423v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents increasingly rely on external tools (file operations, API calls, database transactions) to autonomously complete complex multi-step tasks.