TimeClaw is an exploratory execution learning system that turns multiple valid tool-use paths into hierarchical distilled experience for improved time-series reasoning without test-time adaptation.
Clawkeeper: Comprehensive safety protection for openclaw agents through skills, plugins, and watchers.arXiv preprint arXiv:2603.24414
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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DeepTrap automates discovery of contextual vulnerabilities in OpenClaw agents via trajectory optimization, showing that unsafe behavior can be induced while preserving task completion and that final-response checks are insufficient.
SUDP is a protocol allowing untrusted agents to cause bounded, secret-backed operations through fresh user grants redeemed by a custodian, preventing reusable secret exposure.
Arbiter-K is a new execution architecture that treats LLMs as probabilistic processors inside a neuro-symbolic kernel with a semantic ISA to enable deterministic security enforcement and unsafe trajectory interdiction in agentic AI.
All six evaluated OpenClaw agent frameworks exhibit substantial security vulnerabilities, with reconnaissance behaviors as the most common weakness and agent systems proving significantly riskier than isolated backbone models.
CapSeal introduces a capability-sealed broker architecture that lets AI agents perform constrained secret-using actions without ever receiving the secrets themselves.
The paper develops a unified framework that organizes computer-use agent reliability around perception-decision-execution layers and creation-deployment-operation-maintenance stages to map security and alignment interventions.
citing papers explorer
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TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning
TimeClaw is an exploratory execution learning system that turns multiple valid tool-use paths into hierarchical distilled experience for improved time-series reasoning without test-time adaptation.
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Red-Teaming Agent Execution Contexts: Open-World Security Evaluation on OpenClaw
DeepTrap automates discovery of contextual vulnerabilities in OpenClaw agents via trajectory optimization, showing that unsafe behavior can be induced while preserving task completion and that final-response checks are insufficient.
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SUDP: Secret-Use Delegation Protocol for Agentic Systems
SUDP is a protocol allowing untrusted agents to cause bounded, secret-backed operations through fresh user grants redeemed by a custodian, preventing reusable secret exposure.
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From Craft to Kernel: A Governance-First Execution Architecture and Semantic ISA for Agentic Computers
Arbiter-K is a new execution architecture that treats LLMs as probabilistic processors inside a neuro-symbolic kernel with a semantic ISA to enable deterministic security enforcement and unsafe trajectory interdiction in agentic AI.
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A Systematic Security Evaluation of OpenClaw and Its Variants
All six evaluated OpenClaw agent frameworks exhibit substantial security vulnerabilities, with reconnaissance behaviors as the most common weakness and agent systems proving significantly riskier than isolated backbone models.
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CapSeal: Capability-Sealed Secret Mediation for Secure Agent Execution
CapSeal introduces a capability-sealed broker architecture that lets AI agents perform constrained secret-using actions without ever receiving the secrets themselves.
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Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability
The paper develops a unified framework that organizes computer-use agent reliability around perception-decision-execution layers and creation-deployment-operation-maintenance stages to map security and alignment interventions.