arXiv AI July 23, 2026 By Om Narayan, Ramkinker Singh, Praveen Baskar

The Chronos Vulnerability: A Taxonomy of Temporal Persistence and Memory-Based Deception in Agentic AI

arXiv:2607. 19433v1 Announce Type: new Abstract: The transition from stateless generative models in artificial intelligence to stateful, autonomous agents represents an architectural evolution that, while providing the capabilities of long-term planning and the automation of enterprise workflows, also represents the introduction of a new form of security threat, the Chronos Vulnerability.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 9

When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents

arXiv:2607. 06595v1 Announce Type: cross Abstract: Personal AI agents powered by large language models can reason and act using available tools to access emails, manage calendars, and push code to remote repositories, all with minimal oversight.

By George Torres, Sharad Shrestha, Satyajayant Misra

llms agents benchmarks

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.

By Yuchen Ling, Shengcheng Yu, Zhenyu Chen, Chunrong Fang

llms agents benchmarks

arXiv AI

Jun 12

The Containment Gap: How Deployed Agentic AI Frameworks Fail Public-Facing Safety Requirements

arXiv:2606. 12797v1 Announce Type: new Abstract: Agentic large language model systems that autonomously invoke tools, maintain persistent memory, and execute multi-step plans are increasingly deployed in public-facing domains, including government services, healthcare triage, and financial advising.

By Md Jafrin Hossain, Mohammad Arif Hossain, Weiqi Liu, Nirwan Ansari

llms agents safety

arXiv AI

Jul 17

Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems

arXiv:2607. 14611v1 Announce Type: cross Abstract: A growing class of agentic systems maintain persistent state across sessions through memory files, behavioral preferences, and knowledge bases.

By Soham Gadgil, David Alexander, Sai Sunku, Franziska Roesner

llms agents safety

arXiv AI

Jun 4

From Untrusted Input to Trusted Memory: A Systematic Study of Memory Poisoning Attacks in LLM Agents

arXiv:2606. 04329v1 Announce Type: cross Abstract: Memory is a core component of AI agents, enabling them to accumulate knowledge across interactions and improve performance.

By Pritam Dash, Tongyu Ge, Aditi Jain, Tanmay Shah, Zhiwei Shang

llms agents benchmarks safety

arXiv AI

Jul 3

ElephantAgent: Contextual State Continuity in Agentic Systems

arXiv:2607. 01919v1 Announce Type: new Abstract: Agentic systems enhance their capabilities by invoking external tools and maintaining persistent memory.

By Jiankai Jin, Xiangzheng Zhang, Zhao Liu, Wenzhuo Xu, Dongdong Yang, Deyue Zhang, Quanchen Zou

agents safety

Related stories

When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents

Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation

The Containment Gap: How Deployed Agentic AI Frameworks Fail Public-Facing Safety Requirements

Bad Memory: Evaluating Prompt Injection Risks from Memory in Agentic Systems

From Untrusted Input to Trusted Memory: A Systematic Study of Memory Poisoning Attacks in LLM Agents

ElephantAgent: Contextual State Continuity in Agentic Systems