arXiv:2606. 10749v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly moving from conversational interfaces to software components that plan, invoke tools, maintain memory, and act on external environments.
arXiv:2509. 25624v3 Announce Type: replace-cross Abstract: As LLMs advance into autonomous agents with tool-use capabilities, they introduce security challenges that extend beyond traditional content-based LLM safety concerns.
arXiv:2606. 24496v1 Announce Type: cross Abstract: The use of agentic systems to perform offensive security operations has moved from a theoretical possibility to a commoditized capability.
The paper introduces CONTINUITY, a framework that ensures secure composition of large language model (LLM) agent controls by using assume‑guarantee contracts and authenticated security contexts. It models each component with signed root grants, provenance commitments, and other mechanisms to carry security context across transitions, formalizing end‑to‑end consequence integrity. A reference verifier and fault‑injection suite demonstrate that the full configuration prevents harmful effects while completing all benign tasks and correctly escalating ambiguous cases.
arXiv:2607. 06807v1 Announce Type: cross Abstract: While enabling effective collaboration on complex tasks, LLM-based Multi-Agent Systems (MAS) face critical security challenges due to vulnerabilities at the agent and interaction levels.
arXiv:2610.15529v2 Announce Type: new Abstract: Agent-to-agent protocols allow LLM agents to discover peers, exchange results, and coordinate tool use. A locally successful protocol attack, however,...
The use of agentic systems to perform offensive security operations has moved from a theoretical possibility to a commoditized capability. However, while the community has focused on creating more and more capable agents, less attention has been allocated to assessing the security of those systems.
The paper introduces DEFER1, a deterministic-first enforcement system for large‑language‑model based multi‑agent systems that uses 28 checks to block most attacks and refers only a small fraction to human judges. In tests across four domains, DEFER1 reduces attack success from about 30% to roughly 3%, with 78% of attacks blocked deterministically and only a quarter reaching the judges. The study highlights that rules effectively handle clear policy violations while judges address ambiguous intent, but also reveals weaknesses such as a risk‑score gate that misclassifies many proposals.
arXiv:2606. 12474v1 Announce Type: cross Abstract: LLM-based multi-agent systems (MAS) solve complex tasks through inter-agent collaboration, but their communication-driven nature also allows security risks to spread across agents and trigger system-wide failures.
arXiv:2609.23894v1 Announce Type: cross Abstract: Agentic AI extends LLM security beyond generated content to persistent state, autonomous actions, tool use, and interactions with humans and other ag...
arXiv:2606. 02302v1 Announce Type: cross Abstract: Autonomous LLM agents increasingly operate in stateful environments where they access tools, files, memory, and external services.
arXiv:2607. 26791v1 Announce Type: cross Abstract: Large Language Model (LLM) agents are increasingly adopted in real-world security operations with access to host artifacts and command-line interfaces (CLIs), making it critical to thoroughly assess their security capabilities.