arXiv AI
Jul 1
Understanding and Evaluating Claw-like Agent Security Through a Computer-Systems Lens
arXiv:2606. 30755v1 Announce Type: cross Abstract: Claw-like AI agents (e.
From Tool Connection to Execution Control: Benchmarking Security Invariants in MCP-Style Agent Runtimes
arXiv:2606. 29073v1 Announce Type: cross Abstract: Model Context Protocol (MCP)-style ecosystems give language-model applications a practical connection layer for tools, resources, prompts, and transports.
Related stories
arXiv AI
Jun 10
GitInject: Real-World Prompt Injection Attacks in AI-Powered CI/CD Pipelines
arXiv:2606. 09935v1 Announce Type: cross Abstract: AI-powered agents are increasingly embedded in continuous integration and continuous delivery/deployment (CI/CD) pipelines to autonomously review pull requests (PRs), triage issues, and maintain codebases.
arXiv AI
Jun 2
Attested Tool-Server Admission: A Security Extension to the Model Context Protocol
arXiv:2605. 24248v2 Announce Type: replace-cross Abstract: The Model Context Protocol (MCP) standardizes how a large-language-model (LLM) agent and an external tool server exchange messages, but not trust: a host reads a server's self-declared tool list and dispatches calls, with no notion of which servers it may use, at what sensitivity, or which of a server's tools are in bounds.
arXiv AI
Jul 8
Unicode TAG-Block Concealment of Tool-Metadata Payloads in the Model Context Protocol: An Approval-View Fidelity Gap Across Three Independent Server Implementations
arXiv:2607. 05744v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) is the dominant way coding agents discover and invoke external tools.
arXiv AI
Jun 30
ChainCaps: Composition-Safe Tool-Using Agents via Monotonic Capability Attenuation
arXiv:2605. 26542v2 Announce Type: replace-cross Abstract: Tool-using agents increasingly operate in open-ended deployment environments, where they compose file systems, web APIs, code interpreters, and enterprise services at runtime.
arXiv AI
Jun 2
AgentRedBench: Dynamic Redteaming and Integration-Aware Defense for LLM Agents over SaaS Integrations
arXiv:2606. 02240v1 Announce Type: cross Abstract: Indirect prompt injection in tool-use agents is a concrete production threat: LLM agents read from integrations (third-party services such as Gmail, Salesforce, or Jira accessed through tool calls) whose response content the user neither writes nor controls.
arXiv AI
Jul 7
Governed MCP: Kernel-Level Tool Governance for AI Agents via Logit-Based Safety Primitives
arXiv:2604. 16870v2 Announce Type: replace-cross Abstract: AI agents increasingly call external tools (file system, network, APIs) through the Model Context Protocol (MCP).
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 22
Trusted Credentials, Untrusted Behavior: Benchmarking LLM-Agent Security in High-Performance Computing
arXiv:2607. 18485v1 Announce Type: cross Abstract: Large language model (LLM) agents are starting to take on routine work in high-performance computing (HPC), including monitoring Slurm jobs, diagnosing failed builds, inspecting simulation output, and coordinating scientific workflows.
arXiv AI
Jul 7
Securing Multi-Tool AI Agent Chains With Dynamic, Real-Time Compositional Policies
arXiv:2607. 03423v1 Announce Type: cross Abstract: Modern AI agent implementations such as frontier coding agents chain multiple tools at runtime that create a security surface that per-tool guardrails are unable to address, as individually permitted tools can violate organizational policies when composed.
arXiv AI
Jul 8
The Balkanization of Execution-Security Research for AI Coding Agents: Isolation, Access Control, and Time-of-Check-to-Time-of-Use Vulnerabilities
arXiv:2607. 05743v1 Announce Type: cross Abstract: AI coding agents now read repositories, call tools, and execute shell commands with limited human oversight, and a fast-growing body of work studies whether the execution layer around them is actually safe.
arXiv AI
Jul 22
Data Leakage Prevention in Agentic Applications via Preemptive Hardening
arXiv:2607. 18847v1 Announce Type: cross Abstract: Agentic systems integrate LLM driven planning with interfaces to external tools, making data leakage and tool misuse feasible via instruction/data boundary failures and prompt injection attacks.