AgentWard: A Lifecycle Security Architecture for Autonomous AI Agents

• URL: http://arxiv.org/abs/2604.24657v1
• Date: Mon, 27 Apr 2026 16:22:27 GMT
• Title: AgentWard: A Lifecycle Security Architecture for Autonomous AI Agents
• Abstract summary: This paper presents AgentWard, a lifecycle-oriented, defense-in-depth architecture that systematically organizes protection across five stages.<n>AgentWard integrates stage-specific, heterogeneous controls with cross-layer coordination, enabling threats to be intercepted along their propagation paths while safeguarding critical assets.
• Score: 16.761926457126428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous AI agents extend large language models into full runtime systems that load skills, ingest external content, maintain memory, plan multi-step actions, and invoke privileged tools. In such systems, security failures rarely remain confined to a single interface; instead, they can propagate across initialization, input processing, memory, decision-making, and execution, often becoming apparent only when harmful effects materialize in the environment. This paper presents AgentWard, a lifecycle-oriented, defense-in-depth architecture that systematically organizes protection across these five stages. AgentWard integrates stage-specific, heterogeneous controls with cross-layer coordination, enabling threats to be intercepted along their propagation paths while safeguarding critical assets. We detail the design rationale and architecture of five coordinated protection layers, and implement a plugin-native prototype on OpenClaw to demonstrate practical feasibility. This perspective provides a concrete blueprint for structuring runtime security controls, managing trust propagation, and enforcing execution containment in autonomous AI agents. Our code is available at https://github.com/FIND-Lab/AgentWard .

Related papers

• HazardAuditor: From Executable Threats to Safer Computer-Use Agents [50.06351661912873]
  Existing guard models target static prompts and responses.<n>Existing safety platforms produce evaluation verdicts rather than the normalized supervision a guard model needs to learn.<n>We introduce HazardAuditor, an execution-grounded framework that closes both gaps.
  arXiv  Detail & Related papers  (2026-09-14T07:09:33Z)
• Towards Security-Auditable LLM Agents: A Unified Graph Representation [22.355591892994642]
  We propose Agent-BOM, a unified structural representation for agent security auditing.<n>We show that Agent-BOM can reconstruct stealthy attack chains, including cross-session memory poisoning and tool misuse.<n>Agent-BOM provides a unified and auditable foundation for root-cause analysis and security adjudication in complex agentic ecosystems.
  arXiv  Detail & Related papers  (2026-05-07T18:14:29Z)
• DecodingTrust-Agent Platform (DTap): A Controllable and Interactive Red-Teaming Platform for AI Agents [121.77550256034]
  DecodingTrust-Agent Platform (DTap) is a controllable and interactive red-teaming platform for AI agents.<n>DTap-Red is the first autonomous red-teaming agent that explores diverse injection vectors and autonomously discovers effective attack strategies.<n>Through DTap, we conduct large-scale evaluations of popular AI agents built on various backbone models.
  arXiv  Detail & Related papers  (2026-05-06T11:59:48Z)
• ClawKeeper: Comprehensive Safety Protection for OpenClaw Agents Through Skills, Plugins, and Watchers [32.10899571321103]
  We present ClawKeeper, a real-time security framework that integrates multi-dimensional protection mechanisms across three complementary architectural layers.<n>We argue that this Watcher paradigm holds strong potential to serve as a foundational building block for securing next-generation autonomous agent systems.
  arXiv  Detail & Related papers  (2026-03-25T15:27:54Z)
• Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw [42.9467373594646]
  This report presents a comprehensive security analysis of the OpenClaw ecosystem.<n>We highlight critical vulnerabilities such as prompt injection-driven Remote Code Execution (RCE), sequential tool attack chains, context amnesia, and supply chain contamination.<n>To address these systemic architectural flaws, we introduce the Full-Lifecycle Agent Security Architecture (FASA)<n>This theoretical defense blueprint advocates for zero-trust agentic execution, dynamic intent verification, and cross-layer reasoning-action correlation.
  arXiv  Detail & Related papers  (2026-03-13T04:33:05Z)
• AgenticCyOps: Securing Multi-Agentic AI Integration in Enterprise Cyber Operations [7.660803748903973]
  Multi-agent systems (MAS) promise adaptive, reasoning-driven enterprise, yet granting agents autonomous control over tools, memory, and communication introduces attack surfaces absent from deterministic pipelines.<n>We introduce AgenticCyOps, a framework built on a systematic decomposition of attack surfaces across component, coordination, and protocol layers.<n>We apply the framework to a Security Operations Center (SOC) workflow, adopting the Model Context Protocol (MCP) as the structural basis.
  arXiv  Detail & Related papers  (2026-03-10T03:15:36Z)
• Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs [65.6660735371212]
  We present textbftextscJustAsk, a framework that autonomously discovers effective extraction strategies through interaction alone.<n>It formulates extraction as an online exploration problem, using Upper Confidence Bound--based strategy selection and a hierarchical skill space spanning atomic probes and high-level orchestration.<n>Our results expose system prompts as a critical yet largely unprotected attack surface in modern agent systems.
  arXiv  Detail & Related papers  (2026-01-29T03:53:25Z)
• AJAR: Adaptive Jailbreak Architecture for Red-teaming [1.356919241968803]
  AJAR is a proof-of-concept framework designed to bridge the gap between "red-teaming" and "action security"<n>AJAR decouples adversarial logic from the execution loop, encapsulating state-of-the-art algorithms like X-Teaming as standardized, plug-and-play services.<n> AJAR is open-sourced to facilitate the standardized, environment-aware evaluation of this emerging attack surface.
  arXiv  Detail & Related papers  (2026-01-16T03:30:40Z)
• 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)
• OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety [58.201189860217724]
  We introduce OpenAgentSafety, a comprehensive framework for evaluating agent behavior across eight critical risk categories.<n>Unlike prior work, our framework evaluates agents that interact with real tools, including web browsers, code execution environments, file systems, bash shells, and messaging platforms.<n>It combines rule-based analysis with LLM-as-judge assessments to detect both overt and subtle unsafe behaviors.
  arXiv  Detail & Related papers  (2025-07-08T16:18:54Z)
• DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents [52.92354372596197]
  Large Language Models (LLMs) are increasingly central to agentic systems due to their strong reasoning and planning capabilities.<n>This interaction also introduces the risk of prompt injection attacks, where malicious inputs from external sources can mislead the agent's behavior.<n>We propose a Dynamic Rule-based Isolation Framework for Trustworthy agentic systems, which enforces both control and data-level constraints.
  arXiv  Detail & Related papers  (2025-06-13T05:01:09Z)
• LLM Agents Should Employ Security Principles [60.03651084139836]
  This paper argues that the well-established design principles in information security should be employed when deploying Large Language Model (LLM) agents at scale.<n>We introduce AgentSandbox, a conceptual framework embedding these security principles to provide safeguards throughout an agent's life-cycle.
  arXiv  Detail & Related papers  (2025-05-29T21:39:08Z)
• DoomArena: A framework for Testing AI Agents Against Evolving Security Threats [81.73540246946015]
  We present DoomArena, a security evaluation framework for AI agents.<n>It is a plug-in framework and integrates easily into realistic agentic frameworks.<n>It is modular and decouples the development of attacks from details of the environment in which the agent is deployed.
  arXiv  Detail & Related papers  (2025-04-18T20:36:10Z)

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.