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

• URL: http://arxiv.org/abs/2607.19433v1
• Date: Mon, 20 Jul 2026 22:50:33 GMT
• Title: The Chronos Vulnerability: A Taxonomy of Temporal Persistence and Memory-Based Deception in Agentic AI
• Abstract summary: This study formalizes the threat model for persistence-based attacks and the threat of Dynamics Blindness.<n>The transition from stateless generative models in artificial intelligence to stateful, autonomous agents represents an architectural evolution.<n>This study synthesizes a defense-in-depth landscape, categorizing emerging frameworks such as diagnostic trajectory guardrails (AgentDoG), formal temporal verification (Agent-C), immunological memory consensus (A-MemGuard) and hardware-anchored trust via GPU-based Trusted Environments (TEEs) and Zero-Trust memory architectures.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. The Chronos Vulnerability represents the threat of memory-based attacks, including the Memory Injection Attack (MINJA) and the sleeper agent, in which the internal belief system of the autonomous agent is compromised, effectively decoupling the attack vector from the final catastrophic event. This study formalizes the threat model for persistence-based attacks and the threat of Dynamics Blindness in the context of the World of Workflows benchmark, demonstrating that traditional endpoint content filters are insufficient for the current stateful architecture. Consequently, this study synthesizes a defense-in-depth landscape, categorizing emerging frameworks such as diagnostic trajectory guardrails (AgentDoG), formal temporal verification (Agent-C), immunological memory consensus (A-MemGuard), and hardware-anchored trust via GPU-based Trusted Execution Environments (TEEs) and Zero-Trust memory architectures.

Related papers

• Defending against Model Extraction for GNNs with Model Reprogramming [57.82196268649236]
  Graph Neural Networks (GNNs) serve as the backbone for high-stakes applications in Machine-Learning-as-a-Service (ML)<n>Still, their black-box deployment exposes them to Model Extraction (ME) attacks.<n>Existing defenses suffer from a critical ''Euclidean bias''<n>We propose GraphRP, a proactive defense framework that repurposes Model Reprogramming for security.
  arXiv  Detail & Related papers  (2026-08-11T23:20:15Z)
• Safety in Self-Evolving LLM Agent Systems: Threats, Amplification, and Case Studies [62.65225104423404]
  Self-evolving agent systems introduce a qualitatively new threat landscape in which adversarial influences become permanently encoded.<n>We present a systematic security and privacy analysis organized around the Module-Lifecycle Attack Surface matrix.<n>Our findings establish that self-evolution converts every known attack category from session-bounded to lineage-persistent.
  arXiv  Detail & Related papers  (2026-06-22T09:23:50Z)
• Provably Secure Agent Guardrail [89.79561918065122]
  Existing defense architectures rely on empirical semantic guardrails and probabilistic large model adjudicators.<n>This paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning.
  arXiv  Detail & Related papers  (2026-05-28T02:12:41Z)
• Enabling Adversarial Robustness in AI Models through Kubeflow MLOps [0.21290878226779877]
  This paper presents security measures for AI models deployed in clusters.<n>The proposed architecture integrates Kubeflow-based MLOps to automatically detect adversarial attacks during the inference phase.<n>Experiments indicate that the deployed defense robustifies the model, significantly recovering accuracy relative to the degradation caused by the attack.
  arXiv  Detail & Related papers  (2026-05-14T12:45:36Z)
• Agentic AI as a Cybersecurity Attack Surface: Threats, Exploits, and Defenses in Runtime Supply Chains [7.8562769948743965]
  Agentic systems built on large language models (LLMs) extend beyond text generation to autonomously retrieve information and invoke tools.<n>This runtime execution model shifts the attack surface from build-time artifacts to inference-time dependencies, exposing agents to manipulation through untrusted data and probabilistic capability resolution.<n>We systematize these risks within a unified runtime framework, categorizing threats into data supply chain attacks (transient context injection and persistent memory poisoning)<n>We also identify the Viral Agent Loop, in which agents act as vectors for self-propagating generative worms without exploiting code-level flaws.
  arXiv  Detail & Related papers  (2026-02-23T06:57:57Z)
• Zombie Agents: Persistent Control of Self-Evolving LLM Agents via Self-Reinforcing Injections [57.64370755825839]
  Self-evolving agents update their internal state across sessions, often by writing and reusing long-term memory.<n>We study this risk and formalize a persistent attack we call a Zombie Agent.<n>We present a black-box attack framework that uses only indirect exposure through attacker-controlled web content.
  arXiv  Detail & Related papers  (2026-02-17T15:28:24Z)
• CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents [60.98294016925157]
  AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss.<n>We introduce Single-Shot Planning for CUAs, where a trusted planner generates a complete execution graph with conditional branches before any observation of potentially malicious content.<n>Although this architectural isolation successfully prevents instruction injections, we show that additional measures are needed to prevent Branch Steering attacks.
  arXiv  Detail & Related papers  (2026-01-14T23:06:35Z)
• Towards Efficient Agents: A Co-Design of Inference Architecture and System [66.59916327634639]
  This paper presents AgentInfer, a unified framework for end-to-end agent acceleration.<n>We decompose the problem into four synergistic components: AgentCollab, AgentSched, AgentSAM, and AgentCompress.<n>Experiments on the BrowseComp-zh and DeepDiver benchmarks demonstrate that through the synergistic collaboration of these methods, AgentInfer reduces ineffective token consumption by over 50%.
  arXiv  Detail & Related papers  (2025-12-20T12:06:13Z)
• Securing Agentic AI: Threat Modeling and Risk Analysis for Network Monitoring Agentic AI System [2.5145802129902664]
  The MAESTRO framework was used to expose, evaluate, and eliminate vulnerabilities of agentic AI.<n>The prototype agent system was constructed and implemented, using Python, LangChain, and telemetry in WebSockets.
  arXiv  Detail & Related papers  (2025-08-12T00:14:12Z)
• QSAF: A Novel Mitigation Framework for Cognitive Degradation in Agentic AI [2.505520948667288]
  We introduce Cognitive Degradation as a novel vulnerability class in agentic AI systems.<n>These failures originate internally, arising from memory starvation, planner recursion, context flooding, and output suppression.<n>To address this class of failures, we introduce the Qorvex Security AI Framework for Behavioral & Cognitive Resilience.
  arXiv  Detail & Related papers  (2025-07-21T07:41:58Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

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.