arXiv AI
Jun 30
MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems
arXiv:2606. 30602v1 Announce Type: cross Abstract: Multi-agent systems (MAS) are increasingly used to automate complex, distributed workflows.
SAIGuard: Communication-State Simulation for Proactive Defense of LLM Multi-Agent Systems
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.
Related stories
arXiv AI
Jul 9
When Agents Go Rogue: Activation-Based Detection of Malicious Behaviors in Multi-Agent Systems
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 AI
Jun 10
Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation
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 AI
Jul 21
STAC: When Innocent Tools Form Dangerous Chains for LLM Agents
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 AI
Jun 24
Red-Teaming the Agentic Red-Team
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.
arXiv Machine Learning
Jul 1
When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems
arXiv:2605. 01133v3 Announce Type: replace-cross Abstract: Large language model (LLM)-powered multi-agent systems (MAS) enable agents to communicate and share information, achieving strong performance on complex tasks.
arXiv AI
Jun 12
PI-Hunter: Automated Red-Teaming for Exposing and Localizing Prompt Injections
arXiv:2606. 12737v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly evolving into agentic systems that interact with external tools and environments, introducing new security risks such as indirect prompt injection attacks through untrusted external sources.
arXiv AI
Jul 9
Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors
arXiv:2607. 07368v1 Announce Type: cross Abstract: AI control is a family of techniques to prevent an AI with malicious goals from subverting its operator's intent.
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.
Hugging Face Trending Papers
Jun 23
Red-Teaming the Agentic Red-Team
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.
arXiv AI
5d ago
Beyond Handcrafted Security: Towards Self-Evolving Defense for LLM Agents
arXiv:2608. 12977v1 Announce Type: cross Abstract: The expanding operational capabilities of large language model (LLM) agents introduce sophisticated security threats.
arXiv AI
Jul 29
Hybrid Analysis for Secure MCP Tool Use in LLM Agents
arXiv:2607. 25297v1 Announce Type: cross Abstract: The rapid development of large language model (LLM) agents has enabled their broad adoption across diverse real-world tasks.